in association with
The State of AI 2017
Inflection Point
MMC Ventures
Founded in 2000, MMC Ventures is a research-driven venture capital firm investing in high-growth
technology businesses. MMC has backed more than 50 enterprise software and consumer internet
companies that have the potential to change the future of financial services, the workplace and retail.
MMC invests on behalf of private and institutional investors. MMC has over 200 million under
management and is investing approximately 25 million annually.
MMC's portfolio includes: Appear Here, Bloom & Wild, CloudSense, Elder, Gousto, Interactive
Investor, Masabi, NewVoiceMedia, Signal Media, SafeGuard, Sky-Futures, Small World FS and
Tyres on the Drive.
mmcventures.com
@MMC_Ventures
Numis
Numis is the UK's leading mid market investment bank with a focus on high growth companies
both in the listed and unlisted equity markets. Numis has the no. 1 rated equity research team, the
leading market share in UK equity issuance and the most UK listed corporate clients, at 200, of any
investment bank. In 2015, Numis formalised its efforts in the unlisted market with the formation of
its Venture Broking team and its investment in Crowdcube, the largest UK investor in seed and stage
A companies.
numis.com
@NumisSecurities
1
Contents
Introduction
Summary
PART 1: An introduction to AI
1. What is AI?
2. Why is AI important?
3. Why is AI coming of age today?
PART 2: The applications, implications and adoption of AI
4. The applications of AI
5. The implications of AI
6. The adoption of AI
7. The growth of AI services
PART 3: Early stage AI companies in the UK
8. The dynamics of UK AI
9. AI entrepreneurs' perspectives
PART 4: Investing in AI
10. An investment framework for AI
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4
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31
39
51
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The State of AI 2017
Inflection Point
2
Introduction
David Kelnar
Report author
Investment Director
& Head of Research
MMC Ventures
The State of AI 2017: Inflection Point
Artificial Intelligence (AI) has been described as "the ultimate breakthrough
technology" (Satya Nadella, Microsoft). Five of the world's ten most valuable
companies Alphabet (Google), Amazon, Apple, Facebook and Microsoft are
repositioning to become AI-first organisations. While the last ten years have been
about building a world that is mobile-first, "in the next ten years, we will shift to a
world that is AI-first." (Sundar Pichai, Google).
While hype around AI is at a peak, and some expectations may exceed results in the
short term, we believe AI represents a paradigm shift in technology that warrants the
attention it is receiving. In 2017 AI reached an inflection point, driven by milestones
in investment, capability, entrepreneurship and adoption. The implications for
consumers, companies and society will be profound.
Our inaugural State of AI report for 2017 is intended to inform and empower
corporate executives, entrepreneurs and investors. While accessible and jargon-
free, it draws on new data and over 400 discussions with ecosystem participants to
go beyond the hype and explain the reality of AI today, what is to come and how to
take advantage. Every chapter includes actionable recommendations for executives,
entrepreneurs and investors.
In Part 1, we provide an accessible introduction to AI for the
non-specialist.
We explain how AI is a way for software to perform difficult tasks more effectively,
by learning through practice instead of following rules.
We describe why AI is important. For the first time, traditionally human capabilities
can be undertaken in software efficiently, inexpensively and at scale.
AI capability has reached an inflection point. We explain why, after seven false
dawns since the 1950s, AI technology is coming of age.
In Part 2, we explain the applications, implications and
adoption of AI.
AI has numerous, tangible use cases. We highlight 31 across eight sectors and
highlight why some sectors, such as financial services, will be affected more
than others.
We explain the profound implications of AI. AI will cause shifts in sector value
chains, require new competencies from companies, change companies'
competitive positioning, disrupt business models and accelerate cycles of
innovation. We also explore AI's potential benefits and harms to society, from
improved health to risks of job displacement and increased conflict.
The adoption of AI has reached an inflection point, moving from innovators
and early adopters to the early mainstream. We describe buyers' awareness,
understanding and spending intentions regarding AI, highlight sectors leading
AI adoption and identify which will be next. We also explain the dynamics of
corporate AI adoption. How are companies deploying AI, who is making the
decisions and what are the key inhibitors?
In 2017 AI
reached an
inflection
point, driven
by milestones
in investment,
capability,
entrepreneurship
and adoption.
3

In Part 3, we explore the ecosystem of early stage AI
companies in the UK.
We map 400 innovative, early stage AI software companies in the UK and highlight
key dynamics. AI entrepreneurship is thriving a new AI company is founded every
five days but focus is uneven, with areas of competition and opportunity. Nearly
two thirds of companies are at the earliest stages of their journeys, and their path
to monetisation can be longer. We also highlight 11 of the UK's leading early stage
AI companies.
We explore UK AI in a global context. UK AI startups comprise nearly half of
the European total, and UK companies are less embryonic than their European
counterparts. UK entrepreneurs are embracing AI; a higher proportion of startups
in the UK focus on AI than in Europe or even the US.
Capital dynamics for AI startups are unusual. We discover that investments into
early stage AI firms are 20%-50% larger than average and explain why.
We hear from 11 of the UK's leading AI entrepreneurs. They explain how AI will
impact the future, how buyers can maximise value when engaging with startups,
the biggest challenges they face when developing AI, and the key success factors
for AI entrepreneurship.
In Part 4, we explain how investors can identify promising
early stage AI companies.
The AI paradigm shift presents opportunities to invest in disruptive early stage
software companies as well as public companies developing competitive
advantage. We are entering a second wave of AI investment, with capital being
allocated to developers of vertical applications.
We provide an investment framework that describes success factors for early stage
applied AI companies. Spanning value potential, value realisation and defensibility,
the 16 factors provide a guide for investing in AI and a framework for early stage
companies to assess their strengths and challenges.
At MMC Ventures, AI is a core area of research, conviction and investment. In the
last 12 months we've made eight investments, comprising 54% of the capital we've
invested in that time, into some of the UK's most promising AI companies. If you're
an early stage AI company, get in touch to see if we can accelerate your journey.
Previous industrial revolutions stemmed from the ability to create or move power,
goods or information. Today, as we enter the fourth industrial revolution, value
creation lies in the processing of information. AI's ability to process information
more intelligently will create benefits both humble and historic. Welcome to the
end of the beginning.
We draw on new
data and 400
discussions
with ecosystem
participants to go
beyond the hype
and explain the
reality of AI today,
what is to come
and how to take
advantage.
In the next ten
years, we will
shift to a world
that is AI-first.
Sundar Pichai, Google
"At Numis, we recognise the potential of AI and other transformative technologies. In 2015 we
formed our Venture Broking team to engage with high growth private UK companies and help the
next generation of leading companies leverage our Number 1-rated public market research and 200
corporate broking relationships." Nick James Technology and Venture Research, Numis
4
Summary
Part 1: An introduction to AI
1. What is AI?
Artificial Intelligence ('AI') is a general term that refers to hardware or software that exhibit behaviour which appears intelligent.
Basic AI has existed since the 1950s, via rules-based programs that display rudimentary intelligence in limited contexts.
Rules-based systems are limited. Many real-world challenges, from making medical diagnoses to recognising objects in
images, are too complex or subtle to be solved by programs that follow sets of rules written by people.
Excitement regarding modern AI relates to a set of techniques called machine learning, where advances have been rapid
and significant. Machine learning is a sub-set of AI.
Machine learning enables programs to learn through training, instead of being programmed with rules. By processing training
data, machine learning systems provide results that improve with experience.
Machine learning can be applied to a wide variety of prediction and optimisation challenges, from determining the probability
of a credit card transaction being fraudulent to predicting when an industrial asset is likely to fail.
Deep learning is a subset of machine learning that is delivering breakthrough results in fields including computer vision
and language.
Deep learning emulates the way animals' brains learn subtle tasks it models the brain, not the world. Networks of
artificial neurons process input data to extract features and optimise variables relevant to a problem, with results improving
through training.
Recommendations
Executives
Familiarise yourself with the concepts of rules-based software, machine learning and deep learning.
Recognise that machine learning represents a paradigm shift in software development that offers new possibilities and
will impact your organisation.
Entrepreneurs
Explore the concepts of machine learning and deep learning, the benefits they offer, and how they are being applied
to solve problems in a range of sectors (Chapter 4).
Investors
Ensure leaders at existing portfolio companies are familiar with the concepts of machine learning and deep learning, given
their importance.
Familiarise yourself with different approaches to machine learning, including random forests, Bayesian networks, support vector
machines and deep learning, to differentiate between companies deploying meaningful machine learning and others.
The State of AI 2017
Inflection Point
Machine learning enables programs
to learn by training, instead of being
programmed with rules.
5
2. Why is AI important?
Increasingly, AI enables traditionally human capabilities understanding, reasoning, planning, communication and perception
to be undertaken by software effectively, efficiently and at low cost.
General analytical tasks, including finding patterns in data, that have been performed by software for many years can also be
performed more effectively using AI.
New possibilities enabled by AI include: autonomous vehicles; automated medical diagnosis; voice input; intelligent agents;
automated data synthesis; and enhanced decision-making.
Recommendations
Executives
Explore the new possibilities enabled by AI to appreciate the importance AI will have in the decade ahead.
Familiarise yourself with the five fields of AI research we describe. Identify core aspects of your company's value proposition
for example, planning or communication to which AI could be relevant.
The new possibilities enabled by AI will have secondary consequences. Read Chapter 5 to understand the implications of AI.
Entrepreneurs
Explore opportunities, within your own organisation and for customers, to apply progress in the five fields of AI research
we describe to solve intractable problems and ease difficult ones.
Given the importance AI will have in the decade ahead, explore best practices for developing an AI capability (Chapter 9).
Investors
Recognise that although AI is hyped, the possibilities it enables are significant.
Seek companies that are using AI to fulfil new possibilities. The paradigm shift to AI will create large new winners.
3. Why is AI coming of age?
After seven false dawns since its inception in 1956, AI technology has come of age.
The capabilities of AI systems have reached a tipping point due to the confluence of seven factors: new algorithms; the
availability of training data; specialised hardware; cloud AI services; open source software resources; greater investment;
and increased interest.
Together, these developments have transformed results while slashing the difficulty, time and cost of developing and
deploying AI.
Recommendations
Executives
Be aware that AI technology has come of age and will be a key enabler, and potential threat, in the coming decade.
Familiarise yourself with the seven enablers of AI, the applications of AI (Chapter 4), and the implications of AI (Chapter 5) to
lead and contribute to AI initiatives in your organisation.
After seven false dawns since its
inception in 1956, AI technology
has come of age.
6
Entrepreneurs
AI technology can deliver tangible benefits today. Look for opportunities to incorporate AI in your software, where appropriate,
whether or not you are an 'AI company'.
Explore AI infrastructure and services available from Google, Amazon, IBM and Microsoft, as well as open source machine
learning libraries. They enable experimentation with AI at speed and low cost.
Investors
AI will be a powerful tool for existing portfolio companies and a threat. Evaluate whether portfolio companies are embracing
AI as a means of competitive advantage.
With AI technology at a tipping point, seek opportunities to invest directly or indirectly in companies taking advantage of AI.
Part 2: The applications, implications and adoption of AI
4. The applications of AI
AI has numerous, tangible use cases today that are enabling corporate revenue growth and cost savings.
The capabilities of AI its power to incorporate broader data sets into analyses, identify concepts and patterns in data more
effectively, and enable human-to-machine conversation will have application in all sectors and numerous business processes.
Applications will be most numerous in sectors in which a large proportion of time is spent collecting and synthesising data:
financial services; retail and trade; professional services; manufacturing; and healthcare. Applications of AI-powered computer
vision will be particularly significant in the transport sector.
Use cases are proliferating as AI's potential is understood. We describe 31 core use cases across eight sectors: asset
management; healthcare; insurance; law & compliance; manufacturing; retail; transport; and utilities.
We illustrate how AI can be applied to multiple processes within a business function (human resources).
Recommendations
Executives
Examine AI use cases in a range of sectors to familiarise yourself with the technical capabilities of AI from incorporating
additional data sets into analyses to identifying patterns in data more effectively and understanding written and
spoken language.
Assess the extent to which time is spent collating and processing data in your industry. AI's impact will be greatest in
sectors where data synthesis and processing are core.

Identify business processes in your sector that could be improved, automated or reinvented using AI.
Entrepreneurs
AI offers new opportunities for disruption in sectors ranging from manufacturing to healthcare. Identify business processes
ripe for improvement or reinvention through AI, particularly in sectors in which data synthesis or processing are extensive.
AI has numerous capabilities, from multi-variate analysis to natural language processing. Identify opportunities to use multiple
aspects of AI, both within your company and for buyers.
Investors
Evaluate opportunities and threats to portfolio companies from the many applications of AI.
With AI poised to impact multiple sectors, develop a framework to identify preferred sectors for investment. Considerations
are likely to include fundamentals (scope for structural change in a sector) and pragmatic factors (sector expertise).
Summary
7
AI's value can be abstracted to four benefits:
innovation, efficacy, velocity and scalability.
5. The implications of AI
AI's value can be abstracted to four benefits: innovation (new products and services); efficacy (the performance of tasks more
effectively); velocity (the completion of tasks more quickly); and scalability (the extension of capabilities to new
market participants).
By automating capabilities previously delivered by human professionals, AI will reduce the cost and increase the scalability
of services, significantly broadening participation in select markets.
In multiple sectors AI will change where, and the extent to which, profits are made within a value chain.
New commercial success factors will determine a company's ability to be successful in the age of AI.
New leaders, followers, laggards and disruptors will emerge as the paradigm shift to AI causes significant shifts in companies'
competitive positioning.
AI, growth of 'x-as-a-service' consumption, and subscription payment models will obviate select business models and offer
new possibilities in sectors including transport, insurance and healthcare.
As AI gains adoption, the skills that companies seek, and companies' organisational structure, will change.
By reducing the time required for process-driven work, AI will accelerate the pace of business and innovation. This may
compress cycles of creative destruction, reducing the period of time for which all but a select number of super-competitors
maintain value.
AI will provide benefits to society including improved health, broader access to services and more personalised experiences.
It will also present risks and dilemmas, including issues of job displacement, bias, conflict and privacy.
Recommendations
Executives
Evaluate how the benefits unleashed by AI innovation, efficacy, velocity and scalability will impact your industry.
Assess the shifts in your industry value chain that will occur as AI adoption grows.
Evaluate the business model a disruptor might adopt in the age of AI, if freed from the "innovator's dilemma". What would
the Netflix to your Blockbuster look like?
Assess the extent to which your company is developing the commercial success factors, skills and organisational design
required for the age of AI.
Recognise the need for responsible stewardship. AI presents risks to society including issues of job displacement, bias,
and privacy as well as benefits.
Entrepreneurs

Identify opportunities to take advantage of probable shifts in sector value chains that will be caused by AI.
Develop initiatives that will take advantage of the new market participants and business models that AI will present.

Identify weaknesses in incumbents' competitive positioning that are likely to persist, or worsen, given their structure or strategy.
Investors
Assess how the innovation, efficacy and scalability enabled by AI will impact your existing portfolio companies.

Identify investment opportunities in sectors that will be transformed as a result of AI altering value chains and enabling
new market participants.
Evaluate opportunities to invest in companies structured around business models that will come of age as AI disrupts
existing markets.

8
Summary
6. The adoption of AI

Awareness of AI has reached an inflection point. Given media attention and vendor marketing, executives' awareness
of AI is high.
Understanding of AI among buyers is low. Technology principles, use cases and deployment methodologies are
poorly understood.
While nascent, AI adoption is 'crossing the chasm' from innovators and early adopters to the early majority. 20% of
AI-aware executives say they have adopted one or more AI-related technology at scale, or in a core part of their business
(McKinsey Global Institute).
Adoption of AI will increase significantly as buyers seek to unlock value from data and avoid losing competitive advantage.
75% of executives say AI will be "actively implemented" to some degree in their organisations within three years (Economist
Intelligence Unit).
High tech, automotive and assembly, and financial service firms lead AI adoption. Spending on AI will increase most
in sectors that currently lead adoption.
Poorly articulated business cases weigh on adoption. Better articulation of ROI by AI vendors can catalyse adoption.
While numerous pilot projects relate to chatbots, more than two thirds of buyers are deploying AI to improve decision-making
and enable process automation.
For mid-size and large companies, the C-suite is key for initiating, selecting and funding AI initiatives. In two thirds of
organisations, the CTO or CIO make AI technology decisions given its cross-functional implications.
AI deployment strategies are varied, with a mix of 'build' and buy' strategies, and in a state of flux. 'Hybrid' approaches
are typical. A quarter of companies deploying AI today prefer to purchase a standalone solution.
Lack of skills is the primary challenge for companies deploying AI. Defining an AI strategy, identifying use cases for AI,
and securing funding for AI initiatives are additional difficulties.
Recommendations
Executives
Adoption of AI is nascent but has passed a tipping point. Develop an AI strategy to avoid losing competitive advantage.
Understanding of AI within your organisation is likely to be low. Develop initiatives to improve senior executives' understanding
of AI by engaging with third-party experts.
Engage with AI software companies that articulate tangible use cases and ROI opportunities. Seek vendors offering solutions
to business problems, not slogans.
While chatbots receive extensive attention, recognise that your peers are more likely to be deploying AI to enhance business
decision-making and process automation.
Proactively address the likely challenges to your organisation's adoption of AI: lack of skills, the absence of an AI strategy,
lack of clarity regarding AI use cases, and prioritisation of funding.
Entrepreneurs
To address buyers' caution regarding AI technology, articulate solutions to business problems and ROI opportunities,
not AI technology as an end in itself.
Recognise that buyers' understanding of AI is low, and they are likely to lack AI skills and personnel within their organisations.
Become a strategic partner for customers by offering education and support.
Offer buyers improved decision-making and process automation to align with their priorities.
Given the importance of the C-suite in initiating and funding AI initiatives at large companies, prioritise securing senior
sponsorship for your initiatives.
AI adoption is 'crossing the chasm' from innovators
and early adopters to the early majority.
9
Investors
AI adoption is nascent, but crossing a tipping point from early adopters to the early mainstream. Identify opportunities to
invest in AI-first companies that can capitalise on increasing demand for AI.
Understanding of AI among buyers is limited, and C-level sponsorship may be required for deployments in large companies.
Given these go-to-market dynamics, evaluate management teams' ability to articulate to buyers tangible solutions to business
problems, and their C-level account management skills.
Prospects that provide solutions aligned with buyers' priorities improved decision-making and process automation
may be most attractive.
7. The growth of AI services
For every 1 spent on enterprise software, 3 is spent on IT services consulting, system integration and outsourcing.
IT service companies involved in AI 'AI service' companies assist buyers with AI initiatives ranging from reviews of
AI strategy to chatbot implementations.
A focal point for AI service activity is supporting buyers' rollout of analytics software that incorporates AI.
As mid-size companies and enterprises experiment with AI, most plan to involve a third-party AI service provider, fuelling
growth in the AI services market.
While early and modestly-sized today, the AI services market is poised for rapid growth. As buyers use AI to gain value from
historic investments in data collection, we expect AI services to offer a multi-billion-dollar market opportunity by 2020.
'Convergence' and consolidation are reshaping the market. Software companies are developing service capabilities to
support solution-selling, while service companies are developing and acquiring software assets to access client opportunities
and reduce cost to serve.
The delivery model for AI services is changing. Led by mid-market buyers, we expect a mix shift from traditional projects of fixed
scope, to managed services delivered via the cloud, paid for on an ongoing basis.
Competition for AI services work above the mid-market will be fierce. For large deals, global service firms will leverage their
data and data science personnel. Mid-size deals will represent a second battleground, with mid-tier vendors competing with
each other and vendors from above and below. For smaller deals, select boutiques offer buyers the right success factors
accessibility, flexibility and low cost to achieve scale and mature into mid-size vendors.
Specialisation is becoming a key success factor for competitive differentiation and defensibility. Increasingly, individual AI
service providers are focusing their competencies on specific verticals, business functions or business sub-functions.
Recommendations
Executives
Evaluate opportunities to catalyse time to value in AI by engaging with AI service providers.
Effective service providers focus on solving business problems, not AI technology for its own sake. Engage with companies
that describe clearly how they can improve your key performance indicators, using technology as an enabler.
Managed service deployments are coming of age. For AI-powered analytics, evaluate whether a third-party solution
delivered via the cloud could be suitable.
Competition for large contracts is fierce. Negotiate robustly with multiple suppliers to maximise value.
Entrepreneurs
Consider offering a managed service capability to take advantage of evolving buyer behaviour.
Evaluate a specialisation strategy to develop data network effects and competitive differentiation in a competitive market.
Proactively explore M&A to avoid being left sub-scale in a consolidating market.
10
Investors
Evaluate opportunities for investment in AI services, given potential for strong growth in the market.
Be cognisant of competitive dynamics and the risk of commoditisation in the market.
Evaluate whether encouraging portfolio companies to specialise in certain sectors or business functions could support
their defensibility.
Given extensive market consolidation, create and identify opportunities to achieve scale through mergers and realise
value through trade sales.
Part 3: Early stage companies in the UK
8. The dynamics of UK AI
There are nearly 400 independent, early stage software companies in the UK with AI at the heart of their value proposition.
AI entrepreneurship is thriving. The number of AI companies founded annually in the UK has doubled since 2014. A new
UK AI company has been founded every five days, on average, since 2014.
Over 80% of UK AI startups are vertically-focused business-to-business (B2B) suppliers. Few companies sell direct-to-
consumer given the difficulty of acquiring training data from a 'cold start' and the deployment of AI by global consumer
technology companies.
Entrepreneurial activity in AI is unevenly spread. More UK AI companies (one in seven) address the marketing & advertising
function than any other. For companies with a sector focus, finance dominates. In select sectors (manufacturing) and business
functions (finance), activity appears modest relative to market opportunities.
Few (one in ten) UK AI startups develop core AI technologies applicable to a wide variety of markets. Among these companies,
most focus on research into autonomous systems.
UK AI companies comprise nearly half the European total. AI is well represented in the UK, with a slightly higher proportion
of startups focused on AI than in Europe (excluding the UK) or the US.
UK AI companies are nascent. Two thirds of companies are in the earliest stages of their journey, with Seed or Angel funding.
The sector, however, is maturing rapidly. UK companies are less embryonic than their European counterparts.
Over 40% of companies we meet have yet to receive recurring revenue. The journey to monetisation for AI companies can
be longer given technical challenges, long sales cycles in a B2B-driven market, and client integration requirements.
Globally, investments into early stage AI firms are typically 20%-50% larger than capital infusions into general software
companies of comparable stages.
Staging of capital into UK AI companies can be atypical. One in three growth stage companies raised a significantly larger
post-Angel rounds than is typical.
We feature 11 leading B2B and B2C AI companies across a range of sectors to illustrate how early stage companies are using
AI to address opportunities.
Recommendations
Executives
Explore the rich ecosystem of early stage AI companies in the UK. Most will be B2B vendors and some will offer market-leading
solutions to challenges in your organisation.

Identify potential suppliers and partners in your sector, and in key business functions.
Anticipate that many AI companies will be nascent, which may limit their ability to provide customer references and
extensive resources.
Summary
11
Entrepreneurs

Identify potential competitors and partners using our market map.
AI entrepreneurship has accelerated, increasing the number of market entrants and competition. Prioritise customer
acquisition in an increasingly crowded market.

Implement technologies that can reduce the cost and time required to ingest data, process data and deploy your product
at client sites, to overcome challenging go-to-market dynamics that are common for early stage AI companies.
Recognise that capital raises for early stage AI companies are typically larger than for non-AI software companies.
Capitalise your business adequately to create and maintain competitive advantage.
Investors
With some segments over-supplied by startups and others under-served, identify attractive pockets of opportunity aligned
with themes on which you focus.
With investments into AI companies larger than average, valuations can be elevated. Consider whether or not you are willing
to 'overpay' to access opportunities.
A significant proportion of AI companies have yet to achieve recurring revenue. Further, a sizeable minority of Angel stage
companies are raising larger second rounds than is typical. Evaluate whether you are willing to invest in pre- or low revenue
companies to secure access.
9. AI entrepreneurs' perspectives
Entrepreneurs anticipate a new, AI-driven future. AI will improve decision-making and increase automation in every sector and
most businesses functions, with profound effects.
Early stage companies offer buyers innovation and flexibility. Startups enable established companies to harness new
technologies, and buyers can shape evolving propositions from early stage companies to their bespoke needs.
When engaging with early stage companies, buyers can maximise value by adopting a collaborative mindset and simplifying
procurement processes.
Successful AI entrepreneurs deliver solutions, not technology. AI companies should focus on solving a business problem, not
on technology as an end in itself. Identifying repetitive, data-intensive problems well suited to AI enables companies to attract
clients and address inefficiencies in their own organisations.
Access to data, scarce talent and difficult productisation processes are key challenges for early stage AI companies. Companies
can mitigate these challenges, respectively, by implementing data acquisition strategies early in their journey, building
relationships with academic institutions and research communities, and developing feedback loops between development
teams and customer success functions.
Key success factors for AI entrepreneurship are: customer focus; continuous technological evolution; development of data
access strategies; long-term planning; and perseverance in this demanding field.
Recommendations
Executives
Entrepreneurs have a valuable understanding of the AI-enabled future. Engage with them to improve your organisation's
understanding of AI, and how its potential could unlock strategic value for your organisation in the long term.
Early stage companies can be powerful enablers of innovation. Explore opportunities to collaborate with early stage companies
by creating horizontal innovation departments and engaging in proof-of-concept projects.
To maximise value from early stage companies, consider a simplified procurement process, adopt a collaborative mindset,
provide continual feedback and expect capabilities to evolve over time.
12
Entrepreneurs
AI has the potential to create value in most business processes and can be a powerful tool for all early stage companies
not just 'AI companies'. Identify opportunities to apply AI to business problems and develop an AI strategy to avoid losing
competitive advantage.
To attract customers and investors, articulate solutions to business problems rather than AI technology as an end in itself.
Given their importance and difficulty, from the inception of your company develop strategies for data access, AI talent
recruitment and productising AI. Plan for the long term.
View AI as a capability, not a feature. Anticipate ongoing development and resource the initiative accordingly.
AI can improve your own company's processes as well as customers'. Look within your company for opportunities to automate
manual processes and free personnel to focus on client activity.
Investors

Identify founders who combine a profound vision of AI's ability to unlock value with the ability to articulate to buyers down-to-
earth solutions that address business challenges.
Prioritise evaluating AI companies' access to data and ability to attract AI talent, given the importance of these factors to AI
companies' success.
Evaluate the extent to which leadership teams have the necessary domain expertise and account management capabilities to
engage with large buyers, given demanding go-to-market dynamics.
Part 4: Investing in AI
10. An investment framework for AI
The AI paradigm shift presents opportunities to invest in disruptive early stage software companies as well as public companies
developing competitive advantage.
AI acquisitions have increased significantly, averaging ten per month in 2017 (CB Insights).
A first wave of acquisitions focused on core AI technologies 'deep-tech' AI research or AI-powered computer vision and
language capabilities with cross-sector utility.
We are entering a second wave of AI investment and exits. Capital is being allocated to developers of vertical applications.
We provide our AI Investment Framework, which identifies 16 success factors for early stage, applied AI companies. We divide
the 16 factors into three categories: value potential, value realisation and defensibility. Applying the success factors helps
highlight attractive investment opportunities.
Keys to value potential are: scope for value release and disruption; unattractive alternatives; suitability of AI
to a business problem; a path to acceptable technical performance; and suitability of available data.
Keys to value realisation are: management commerciality; quantifiability of ROI; buyer readiness; benign regulation;
and deployment scalability.
Keys to defensibility are: distance from AI monoliths' offerings; domain complexity; data network effects; proprietary
algorithms; attractive AI talent dynamics; and strong capitalisation.
AI acquisitions have
increased significantly,
averaging ten per
month in 2017.
CB Insights
Summary
13
Recommendations
Executives
Apply the 16 factors to assess your own organisation's AI capabilities.
Use the 16 factors to identify strengths and weaknesses, and support due diligence, of AI partners and potential
acquisition opportunities.
Entrepreneurs
Evaluate your company's strengths and weaknesses against the 16 factors.
Highlight to buyers and investors, as appropriate, your company's strengths in key criteria including value release,
management commerciality, quantifiability of ROI, data network effects, AI talent, vertical focus and domain expertise.
Address headwinds to value realisation by automating deployment requirements, particularly customer data processing,
and focusing early on building a capable sales organisation.

Investors decline to invest in startups due to doubts about management commerciality and tangibility of ROI more than for
any other reasons. Focus remediation and messaging on these critical issues.
Investors
Consider developing a basket of AI-driven investments.
Apply the 16 factors, in addition to your usual considerations, to evaluate early stage applied AI companies.
Remain open-minded to select investment opportunities in horizontal AI providers. While rarer, and with differing dynamics
to application providers, companies with world-class technology valuable to an AI platform provider can be an attractive
technology or talent acquisition.
Get in touch with us to discuss your perspective. Where do you agree, or disagree, with our thinking?
We provide our AI
Investment Framework,
which identifies 16 success
factors for early stage,
applied AI companies.
14
15
What is AI?
Chapter 1
Summary
'AI' is a general term that refers to hardware or software that exhibit behaviour which appears
intelligent.
Basic AI has existed since the 1950s, via rules-based programs that display rudimentary
intelligence in limited contexts. Early forms of AI included 'expert systems' designed to mimic
human specialists.
Rules-based systems are limited. Many real-world challenges, from making medical diagnoses
to recognising objects in images, are too complex or subtle to be solved by programs that
follow sets of rules written by people.
Excitement regarding modern AI relates to a set of techniques called machine learning, where
advances have been rapid and significant. Machine learning is a sub-set of AI. All machine
learning is AI, but not all AI is machine learning.
Machine learning enables programs to learn through training, instead of being programmed
with rules. By processing training data, machine learning systems provide results that improve
with experience.
Machine learning can be applied to a wide variety of prediction and optimisation challenges,
from determining the probability of a credit card transaction being fraudulent to predicting
when an industrial asset is likely to fail.
There are more than 15 approaches to machine learning. Popular methodologies include
random forests, Bayesian networks and support vector machines.
Deep learning is a subset of machine learning that is delivering breakthrough results in fields
including computer vision and language. All deep learning is machine learning, but not all
machine learning is deep learning.
Deep learning emulates the way animals' brains learn subtle tasks it models the brain, not
the world. Networks of artificial neurons process input data to extract features and optimise
variables relevant to a problem, with results improving through training.
16
Recommendations
Familiarise yourself with the concepts of rules-based software, machine learning and deep learning.
Recognise that machine learning represents a paradigm shift in software development that offers new possibilities
and will impact your organisation.
Identify sources of AI expertise, and existing AI projects, within your organisation.
Explore why AI is important (Chapter 2) and the applications of AI (Chapter 4).
Understand how peers are deploying AI (Chapter 6) to catalyse next steps.
Executives
Chapter 1
What is AI?
Explore the concepts of machine learning and deep learning, the benefits they offer, and how they are being
applied to solve problems in a range of sectors (Chapter 4).
Entrepreneurs
Ensure leaders at existing portfolio companies are familiar with the concepts of machine learning and deep
learning, given their importance.
Explore how the limits of rules-based systems are inhibiting portfolio companies. What problems are too
complex, or subtle, to be solved by rules-based systems?
Familiarise yourself with different approaches to machine learning, including random forests, Bayesian networks,
support vector machines and deep learning, to differentiate between companies deploying meaningful machine
learning and others.
Investors
17
AI: the science of intelligent programs
Coined in 1956 by Dartmouth Assistant Professor John
McCarthy, Artificial Intelligence (AI) is a general term that refers
to hardware or software that exhibit behaviour which appears
intelligent. AI is "the science and engineering of making
intelligent machines, especially intelligent computer programs"
(John McCarthy).
Early AI: rules-based systems
Basic AI has existed for decades, via rules-based programs that
exhibit rudimentary displays of intelligence in specific contexts.
'Expert systems' were a popular early form of AI. Programmers
codified a body of knowledge in a specific field and a set of
rules designed to emulate an expert's reasoning process, to
create a program that would mimic the function of an expert.
SRI International's PROSPECTOR system of 1977 (fig. 1)
assisted geologists' work in mineral exploration. Incorporating
extensive information and over 1,000 rules, the system was
intended to emulate the process followed by a geological
expert investigating the potential of a drilling site (fig. 2).
While expert systems experienced some success
(PROSPECTOR predicted the existence of an unknown
molybdenum deposit in Washington State) their capabilities
were limited.
Source: SRI International
PROSPECTOR Expert System:
1977 Technical Note (Cover)
October 20, 1977
PROSPECTOR A Computer-Based Consultation
System for Mineral Exploration
by
P.E. Hart and R. O. Duda
Artificial Intelligence Center
SRI International
Menlo Park, California 94025
Technical Note No. 155
Presented at the Taita Hills Conference on Standards
for Computer Applications in Resource Studies
(Taita Hills, Kenya, November 8-15, 1977) ; to be published in
International Association for Mathmatical Geology
(Vol. 10, Nos. 5-6).
E1
E2
E4
H1
H2
H3
E5
E6
E7
E3
PROSPECTOR Expert System:
1977 Technical Note (Detail; Decision Tree)
YES
NO
YES
NO
YES
NO
Fig. 1. PROSPECTOR Expert System:
1977 Technical Note (Cover)
Fig. 2. PROSPECTOR Expert System:
1977 Technical Note (Detail: Decision Tree)
AI is a general term that refers to
hardware or software that exhibit
behaviour which appears intelligent.
18
Chapter 1
What is AI?
The limits of rules-based systems
Rules-based systems are limited, because many real-world
challenges are too complex or subtle to be solved by programs
that follow sets of rules written by people.
Providing a medical diagnosis, optimising the performance of
an industrial asset (fig. 3), or developing an ideal investment
portfolio are all complex problems. Each involves numerous
data sets, with non-linear relationships between variables.
Writing a set of rules that will produce ideal results is
extremely challenging.
What if the burden of finding solutions to complex or subtle
problems could be transferred from the programmer to the
program? This is the promise of modern AI.
Machine learning: programs that
learn through training
Excitement regarding modern AI relates to a set of techniques
called machine learning, where advances have been rapid
and significant. Machine learning is a sub-set of AI (fig. 4).
All machine learning is AI, but not all AI is machine learning.
Machine learning enables complex or subtle problems to
be solved by shifting much of the burden from programmers
to their programs. Instead of codifying rules for programs
to follow, programmers enable programs to learn. Machine
learning is the "field of study that gives computers the ability to
learn without being explicitly programmed" (Arthur Samuel).
Machine learning algorithms learn through training. In a
simplified example, an algorithm is fed inputs training data
whose outputs are usually known in advance ("supervised
learning"). The algorithm processes the input data to produce
a prediction or recommendation. The difference between the
algorithm's output and the correct output is then determined.
If the algorithm's output is wrong, the processing function in the
algorithm changes to improve the accuracy of its predictions.
Initially the results of a machine learning algorithm will be poor.
However, as larger volumes of training data are provided,
the program's predictions can become highly accurate
(fig. 5, overleaf).
Source: Alamy
Source: MMC Ventures
Fig. 4. The Evolution of AI: Machine learning
1955
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1995
2000
2005
2010
2015
2020
Artificial Intelligence
Machine learning (ML)
Fig. 3. A complex problem: Industrial asset optimisation
19
The defining characteristic of machine learning algorithms,
therefore, is that the quality of their predictions improves
with experience. The more data provided to a machine
learning system, typically up to a point, the more effective its
predictions. By learning through practice, instead of following
sets of rules, machine learning systems can deliver better
results than rules-based systems in numerous prediction
and optimisation challenges.
There are multiple approaches to
machine learning
There are more than 15 approaches to machine learning.
Each uses different types of algorithmic architecture to
optimise predictions based on input data.
One, deep learning, is delivering breakthrough results in new
domains. We explain deep learning below. Others receive
less attention, but are widely used given their utility and
applicability to a broad range of use cases. Popular machine
learning algorithms beyond deep learning include:
Random forests that create multitudes of decision trees
to optimise predictions;
Bayesian networks that use probabilistic approaches
to analyse variables and the relationships between them;
Support vector machines that are fed categorised
examples and create models to assign new inputs to one
of the categories.
Each approach offers advantages and disadvantages.
Frequently, combinations are used (an 'ensemble' approach).
The nature of available data frequently determines the
algorithms selected. In practice, developers experiment to
determine what is effective.
Machine learning can be applied to a wide variety of prediction
and optimisation challenges. Examples include determining
the probability that a credit card transaction is fraudulent,
identifying products a person is likely to buy based on their
prior purchases, and predicting when an industrial asset is likely
to experience mechanical failure. We describe the applications
of machine learning in multiple sectors in Chapter 4.
Source: Michael Nielsen
Note: The size of data set required to train a machine learning algorithm is context dependent and cannot be generalised.
Fig. 5. Large data sets enable effective machine learning
0%
25%
50%
75%
100%
100
1,000
10,000
100,000
Predictive accuracyNeural network algorithm
Size of training data set
Support vector machine algorithm
The defining
characteristic of machine
learning algorithms is
that the quality of their
predictions improves
with experience.
20
Deep learning: offloading feature
specification
Even with the power of general machine learning, it is difficult
to develop programs that perform certain tasks well, from
understanding speech to recognising objects in images.
In these cases, programmers cannot specify the features in
the input data to optimise. For example, it is difficult to write a
program that identifies images of dogs. Variation in dogs' visual
features is too broad to be described by a set of rules (fig. 6).
We cannot list dogs' features in a way that will enable correct
identification in all cases. Even if an exhaustive set of rules could
be created, the approach would not be scalable. A new set
of rules would be required for every type of object we wished
to classify.
Deep learning is delivering breakthrough results in these use
cases. Deep learning is a sub-set of machine learning and
one of the many approaches to it (fig. 7). All deep learning is
machine learning, but not all machine learning is deep learning.
Source: Google Images
Fig. 6. Identifying features can be difficult
('Dalmatians or ice cream?')
Source: MMC Ventures
Fig. 7. The Evolution of AI: Deep learning
1955
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1980
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2005
2010
2015
2020
Artificial Intelligence
Machine learning (ML)
Deep learning (DL)
Even with the power of
machine learning, it is difficult
to develop programs that
perform certain tasks well,
from understanding speech to
recognising objects in images.
Chapter 1
What is AI?
21
Deep learning is valuable because it transfers an additional
burden the process of feature extraction from the
programmer to their program (fig. 8).
People learn to complete subtle tasks, such as recognising
objects and understanding speech, not by following rules
but through practice and feedback. As children, individuals
experience the world (see a dog), make a prediction ("dog")
and receive feedback. People learn by training. Deep learning
works by recreating the mechanism of the brain (fig. 9, overleaf)
in software (fig. 10, overleaf). With deep learning, we model
the brain, not the world.
To undertake deep learning, developers create artificial
neurons software-based calculators that approximate the
function of neurons in a brain. Artificial neurons are connected
together to form a neural network. The network receives an
input, such as a picture of a dog, extracts features, and makes
a determination. If the output of the neural network is incorrect,
the connections between the neurons adjust to alter its
future predictions.
Initially the network's predictions will usually be incorrect.
As the network is fed many potentially millions of examples
in the domain, the connections between neurons become
finely tuned. When analysing new examples, the artificial
neural network will make correct determinations in almost
all cases.
To undertake deep learning,
developers create an
artificial neural network
that approximates the
function of a brain.
Source: MMC Ventures
Fig. 8. Deep learning offloads the burden of feature extraction
Deep learning
Machine learning
Input
Output
Output
Optimisation
Feature extraction
Input
Feature extraction + Optimisation
Dog
Not Dog
Dog
Not Dog
22
Deep learning has unlocked significant new capabilities,
particularly in the fields of vision and language. Deep learning
enables: autonomous vehicles to recognise objects in the
world around them (fig. 11); the identification of tumours in
medical images; the voice recognition systems of Apple and
Google; voice-controlled devices such as Amazon Echo; real-
time language translation (fig. 12); analysis of sentiment in text;
and more.
Deep learning is not well suited to every problem. It typically
requires large data sets for training. Extensive processing
power is required to train and operate neural networks.
Deep learning also suffers from an 'explainability' challenge
it can be difficult to know how a neural network developed
its predictions.
By freeing programmers from the task of feature specification,
however, deep learning has delivered successful prediction
engines for a range of important use cases and is a powerful
tool in the AI developer's arsenal.
Source: iStock
Source: MMC Ventures
Fig. 9. A biological neural network
Fig. 10. An artificial neural network
An artificial neural network
input layer
output layer
Source: Museum of Computer Science, MTV, CA
Source: Google / Pixel Buds
Fig. 11. Deep learning enables autonomous
vehicles to identify objects around them
Fig. 12. Google's Pixel Buds use deep learning
to provide real-time language translation
Deep learning has delivered
successful prediction
engines for a range of
important use cases and
is a powerful tool in the
AI developer's arsenal.
Chapter 1
What is AI?
23
Deep learning involves the creation of artificial neural
networks software-based calculating units (artificial
neurons) that are connected.
An artificial neuron (fig. 13) has one or more inputs. The
neuron performs a mathematical function on its inputs to
deliver an output. The output will depend on the weights
given to each input, and the configuration of the input-output
function in the neuron. The input-output function can vary.
An artificial neuron may be a:
linear unit (the output is proportional to the total
weighted input);
threshold unit (the output is set to one of two levels,
depending on whether the total input is above a
specified value);
sigmoid unit (the output varies continuously, but not
linearly as the input changes).
An artificial neural network (fig. 14) is created when artificial
neurons are connected to one another. The output of one
neuron becomes an input for another.
Source: MMC Ventures
Fig. 13.
Fig. 14.
An artificial neuron
An artificial neural network
An artificial neuron
An artificial neuron network
input 1
input 2
Output
input 3
f
input 1
input 2
input 3
f
f
f
f
An artificial neural network
is created when artificial
neurons are connected
together. The output
of one neuron becomes
an input for another.
How does deep learning work?
24
Neural networks are organised into multiple layers of neurons
(fig. 15) hence 'deep' learning. An input layer receives
information to be processed, such as a set of pictures. An
output layer delivers results. Between the input and output
layers are hidden layers, where features are detected. Typically,
the outputs of neurons on one level of a network all serve as
inputs to each neuron in the next layer.
We can consider the example of a neural network designed
to recognise pictures of human faces (fig. 16 overleaf). When
pictures are fed into the neural network, the first hidden layers
identify patterns of local contrast low level features such as
edges. As images traverse the hidden layers, progressively
higher level features, such as shapes and objects, are identified.
At its output layer, based on its training the neural network will
deliver a probability that the picture is of a human face.
Source: MMC Ventures
Fig. 15. Deep learning: structuring an artificial neural network
Deep learning: structuring an artificial neural network
Deep learning: the process of feature extraction
Input layer
Hidden layers
Output layer
Input layer
Hidden layers
Output layer
Chapter 1
What is AI?
How does deep learning work?
25
Typically, neural networks are trained through exposure to
a large number of labelled examples. Errors are detected
and the weights of the neurons' connections adjust to improve
results. After the optimisation process is repeated extensively,
the system is deployed to assess unlabelled images.
The neural network above is simple (and simplified), but
structures can vary and most are more complex. Architectural
variations include: connecting neurons on the same layer;
differing the number of neurons per layer; and connecting
neurons' outputs into previous layers in the network (recursive
neural networks).
Designing and improving a neural network requires
considerable skill. Steps include structuring the network
for a particular application, providing suitable training data,
adjusting the structure of the network according to progress
and combining multiple approaches to optimise results.
Source: MMC Ventures, Andrew Ng
Fig. 16. Deep learning: the process of feature extraction
Deep learning: structuring an artificial neural network
Deep learning: the process of feature extraction
Input lyer
Hidden layers
Output layer
Input layer
Hidden layers
Output layer
Typically, neural
networks are trained
through xposure to a
large number of labelled
examples. Errors are
detected and the
weights of the neurons'
connections adjust to
improve results.
How does deep learning work?
26
Why is AI
important?
Chapter 2
Summary
AI technology is important because increasingly, it enables human capabilities
understanding, reasoning, planning, communication and perception to be undertaken
by software effectively, efficiently and at low cost.
General analytical tasks, including finding patterns in data, that have been performed
by software for many years can also be performed more effectively using AI.
The automation of these abilities creates new opportunities in most business sectors and
consumer applications.
Significant new products, services and capabilities enabled by AI include autonomous
vehicles, automated medical diagnosis, voice input for human-computer interaction,
intelligent agents, automated data synthesis and enhanced decision-making.
27
28
Recommendations
Explore the new possibilities enabled by AI, from voice control and intelligent agents to autonomous vehicles
and automated diagnosis, to appreciate the importance AI will have in the decade ahead.
Familiarise yourself with the five fields of AI research we describe. Identify core aspects of your company's value
proposition for example, planning or communication to which AI could be relevant.
The new possibilities enabled by AI will have secondary consequences. Read Chapter 5 to understand the
implications of AI.
Executives
Chapter 2
Why is AI important?
Explore opportunities, within your own organisation and for customers, to apply progress in the five fields of
AI research we describe to solve intractable problems and ease difficult ones.
Given the importance AI will have in the decade ahead, explore best practices for developing an AI capability
(Chapter 9)
Entrepreneurs
Recognise that although AI is hyped, the possibilities it enables are significant. 'Amara's law' is likely to apply
while we tend to overestimate the effect of a technology in the short term, we underestimate its effect in the
long term.
Given the importance of AI, assess the extent to which existing portfolio companies and new prospects are aware
of AI and plan to take advantage of the technology.
Seek companies that are using AI to fulfil new possibilities. The paradigm shift to AI will create large new winners.
Investors
29
AI tackles profound technical challenges
AI is significant because it successfully tackles a profound
set of technical challenges. Increasingly, human capabilities
understanding, reasoning, planning, communication and
perception can be undertaken by software, at scale and at
low cost. General analytical tasks, including finding patterns
in data, that have been performed by software for many years
can also be performed more effectively using AI.
Together, these capabilities create new opportunities in
most business processes and consumer applications.
AI research is focused on five fields
Since its inception in the 1950s, AI research has focused
on five fields of enquiry:
1. Knowledge: The ability to represent knowledge
about the world.
For software to possess knowledge, it must understand
that: certain entities, facts and situations exist in the world;
these entities have properties (including relationships to one
another); and these entities and properties can be categorised.
2. Reasoning: The ability to solve problems through
logical reasoning.
Reasoning is the application of logic to derive beliefs, related
ideas and conclusions from information. Reasoning may be
deductive (specific conclusions are derived from general
premises believed to be true), inductive (general conclusions
are inferred from specific premises) or abductive (the simplest
and most likely explanation for an observation is sought).
3. Planning: The ability to set and achieve goals.
For software to be able to plan, it must be capable of specifying
an alternative, future state of the world that is desirable,
together with a sequence of actions to that will effect progress
towards it.
4. Communication: The ability to understand written
and spoken language.
To communicate with people, software must have the ability
to identify, understand and synthesise written or spoken
human language.
5. Perception: The ability to make deductions about
the world based on sensory input.
To perceive, software must be able to organise, identify and
interpret visual images, sounds and other sensory inputs.
Progress in AI unlocks new possibilities
Because most business processes and consumer applications
involve aspects of knowledge management, reasoning,
planning, communication or perception, progress in AI has
unlocked significant new capabilities.
Traditionally human
capabilities
understanding, reasoning,
planning, communication
and perception can be
undertaken by software, at
scale and at low cost.
New or improved possibilities
Knowledge
Reasoning
Communication
Planning
Perception
Medical
diagnosis
Financial
trading
Legal
analysis
Network
optimisation
Real-time
transcription
Autonomous
vehicles
Augmented
reality
Games
Logistics
Voice control
Drug
creation
Information
synthesis
Asset
management
Predictive
maintenance
Real-time
translation
Medical
imaging
Surveillance
Autonomous
weapons
Intelligent
agents
Media
recommendation
Consumer
targeting
Application
processing
Demand
forecasting
Client
service
Authentication
Industrial
analysis
Compliance
Navigation
Customer
support
Fleet
management
Source: MMC Ventures
In the following chapter, we describe specific AI use cases in
eight sectors.
30
Why is AI
coming of
age today?
Chapter 3
Summary
After seven false dawns since its inception in 1956, AI technology has come of age.
The capabilities of AI systems have reached a tipping point due to the confluence of seven
factors: new algorithms; the availability of training data; specialised hardware; cloud AI
services; open source software resources; greater investment; and increased interest.
Together, these developments have transformed results while slashing the difficulty, time
and cost of developing and deploying AI.
A virtuous cycle has developed. Progress in AI is attracting investment, entrepreneurship
and interest. These, in turn, are accelerating progress.
31
32
Recommendations
Be aware that AI technology has come of age and will be a key enabler, and potential threat, in the
coming decade.
Familiarise yourself with the seven enablers of AI, the applications of AI (Chapter 4), and the implications
of AI (Chapter 5) to lead and contribute to AI initiatives in your organisation.
Executives
Chapter 3
Why is AI coming of age today?
AI technology can deliver tangible benefits today. Look for opportunities to incorporate AI within your software,
where appropriate, whether or not you are an 'AI company'.
Explore AI infrastructure and services available from Google, Amazon, IBM and Microsoft, as well as open source
machine learning libraries. They enable experimentation with AI at speed and low cost.
Market your use of AI to gain attention from buyers and investors. Remember, however, that buyers seek
solutions to business problems (not technology as an end in itself) and investors will look beyond the hype
to evaluate your claims.
Entrepreneurs
AI will be a powerful tool for existing portfolio companies and a threat. Evaluate whether portfolio companies
are embracing AI as a means of competitive advantage.
With AI technology at a tipping point, seek opportunities to invest directly or indirectly in companies taking
advantage of AI.
Familiarise yourself with our AI Investment Framework (Chapter 10) for factors to consider when evaluating
applied AI companies.
Investors
33
There are seven enablers of AI
Research into AI began in 1956. After seven false dawns,
in which results from unsophisticated systems fell short of
expectations, AI capability has reached a tipping point.
AI is now delivering significant utility and its abilities are
advancing rapidly.
AI capabilities have been transformed in the last 48 months
due to:
1. the development of more effective AI algorithms;
2. increased availability of data to train AI systems;
3. specialised hardware to accelerate training of AI algorithms;
4. cloud-based AI services to catalyse developer adoption;
5. open source AI software frameworks that enable
experimentation;
6. increased investment in AI by large technology
companies and venture capitalists;
7. greater awareness of AI among investors, executives,
entrepreneurs and the public.
Together, these developments have improved results from
AI systems and increased the breadth of challenges to which
they can be applied. They have also irreversibly reduced the
difficulty, time and cost of developing basic AI systems.
1. Enhanced algorithms have improved results
Deep learning, a fruitful form of machine learning, is
not new; the first specification for an effective, multi-
layer neural network was published in 1965. In the last
decade, however, evolutions in the design of deep
learning algorithms have transformed results, delivering
breakthrough applications in areas including computer
vision (fig. 17) and language (fig. 18).
Convolutional Neural Networks (CNNs) have dramatically
improved computers' ability to recognise objects in images.
Employing a design inspired by the visual cortexes of animals,
each layer in a CNN acts as a filter for the presence of a specific
pattern. In 2015, Microsoft's CNN-based computer vision
system identified objects in pictures more effectively (95.1%
accuracy) than humans (94.9% accuracy) (Microsoft). "To our
knowledge," they wrote, "our result is the first to surpass human
level performance." Broader applications of CNNs include
video classification and speech recognition.
Recurrent Neural Networks (RNNs) are delivering
improved results in speech recognition and beyond. While
data progresses in a single direction in conventional ('feed
forward') neural networks, RNNs have feedback connections
that enable data to flow in a loop. With additional connections
and memory cells, RNNs 'remember' data processed
thousands of steps ago and use it to inform their analysis of
what follows. This is valuable for speech recognition, where
interpretation of an additional word is enhanced by analysis
of preceding ones.
The Long Short-Term Memory (LSTM) model is a particularly
effective new RNN architecture. From 2012, Google used
LSTMs to power speech recognition in the Android platform.
In October 2016, Microsoft reported that its LSTM speech
recognition system achieved a word error rate of 5.9%
human-level speech recognition for the first time in history
(Microsoft). By August 2017, word error rate had been
reduced to 5.1% (Microsoft).
Source: MMC Ventures, Microsoft
Source: MMC Ventures, Microsoft
Fig. 17. Human-level image recognition
Fig. 18. Human-level speech recognition
0%
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Computer image recognition
Human image recognition
Introduction of
deep learning
Introduction of
deep learning
Computer image recognition
Human image recognition
Word error rate (%)Error rate (%)0%
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deep learning
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Huan image recgnition
Word error rate (%)Error rate (%)
34
Chapter 3
Why is AI coming of age today?
2. Extensive data has enabled training of AI systems
Large data sets thousands or millions of examples, depending
on the domain are typically required to train a neural network.
The creation, and availability, of data have grown exponentially
in recent years, fuelling AI.
Today, humanity produces 2.5 exabytes (2,500 million
gigabytes) of data daily (Google). 90% of all data has
been created in the last 24 months (Sintef ICT). Data
has ballooned as humanity passed through two waves
of data creation, and now enters a third.
The first wave of data, which began in the 1980s, involved the
creation of documents and transactional data. It was catalysed
in the 1990s by the proliferation of internet-connected desktop
PCs. A second wave of data followed, with an explosion of
unstructured media (emails, photos, music and videos),
web data and meta data from pervasive, connected
smartphones.

Today we enter the third age of data. Machine sensors
deployed in industry and the home provide additional
monitoring-, analytical- and meta-data.
With much data created today transmitted for use via the
internet, growing internet traffic is a proxy for humanity's
increasing data production. In 1992, humanity transferred
100GB of data per day. By 2020, we will transfer 61,000GB
per second (fig.19) (Cisco, MMC Ventures).
Specialist data resources have further catalysed the
development of AI. ImageNet, a free database of 14.2 million
hand-labelled images, has supported the rapid development
of deep learning algorithms used to classify objects in images.
3. Specialised hardware has accelerated training
of AI systems
Graphical Processing Units (GPUs) are specialised electronic
circuits that slash the time required to train the neural networks
used in deep learning-based AI.
Modern GPUs were developed in the 1990s, to accelerate
3D gaming and 3D development applications. Panning or
zooming a camera in a simulated 3D environment uses a
mathematical process called a matrix computation.
Microprocessors with serial architectures, including the Central
Processing Units (CPUs) that interpret and execute commands
in today's computers, are poorly suited to the task. GPUs were
Source: Cisco, MMC Ventures
Gigabytes per second0.00
0.01
0.1
1
10
100
1,000
10,000
100,000
Global internet traffic is increasing logarithmically
1992
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2002
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2015
2020E
Fig. 19. Global internet traffic is increasing exponentially
35
4. Cloud AI services have fuelled adoption
Developers' adoption of a technology supports its proliferation.
Leading software providers including Google, Amazon, IBM
and Microsoft have offered cloud-based AI infrastructure and
services, enabling developers' use of AI.
The providers' infrastructure platforms include environments
in which to build and iterate AI algorithms, and 'GPUs-as-a-
service' to power them. Their services comprise a burgeoning
range of on-demand AI capabilities, from image recognition to
language translation, which developers can incorporate in their
own applications.
Google machine learning offers APIs for: computer vision
(object identification, explicit content detection, face
recognition and image sentiment analysis); speech (speech
recognition and speech-to-text); text analysis (entity
recognition, sentiment analysis, language detection and
translation); and employee job search (opportunity surfacing
and matching). Microsoft Cognitive Services include over 21
services in the fields of vision, speech, language, knowledge
and search. The accessibility and relative affordability of
cloud providers' AI infrastructure and services are significantly
increasing adoption of AI among developers.
developed with massively parallel architectures (Nvidia's M40
GPU has 3,072 cores) to perform matrix calculations efficiently.
Training a neural network involves numerous matrix
computations. GPUs, while conceived for 3D gaming,
therefore proved ideal for accelerating deep learning. Their
effect is considerable. A simple GPU can increase five-fold
the speed at which a neural network can be trained. Ten-fold
or larger gains are possible. When combined with Software
Development Kits (SDKs) tuned for popular deep learning
frameworks, improvements in training speed can be
even greater (fig. 20).
Source: NVIDIA, MMC Ventures
Fig. 20. 60x neural network training speed in three years
Speed-up of images/second (vs K40 GPU in 2013)0X
10X
20X
30X
40X
50X
60X
70X
60X training speed in three years
2013
2014
2015
2016
P100 GPU
+ CuDNN 5 SDK
M40 GPU
+ CuDNN 4 SDK
K80 GPU
+ CuDNN 1 SDK
K40 GPU
61,000
Gigabytes per second will
be transferred by humanity,
by the year 2020.
Source: Cisco, MMC Ventures
36
5. Open source software has catalysed experimentation
The availability of open source AI software frameworks
has lowered barriers to entry for experimentation and
proficiency in AI.

Researchers, and providers of cloud infrastructure and AI
services, are open-sourcing AI frameworks and libraries
of algorithms to catalyse developers' adoption of AI.
(Infrastructure providers also benefit from the proliferation of
data-intensive AI applications). Popular open source platforms
include TensorFlow (Google), Caffe2 (Facebook), Cognitive
Toolkit (Microsoft), TorchNet (Facebook), H2O (H2O.ai) and
Mahout (Apache Software Foundation).
Each framework offers benefits. Caffe2 is a scalable deep
learning framework that can process images at speed.
Cognitive Toolkit provides high performance on varying
hardware configurations. H2O reduces time-to-value for AI-
powered enterprise data analysis. Mahout provides scalability
and pre-made algorithms for tools such as H2O. Google's
decision to open source TensorFlow in November 2015 was
particularly significant, given the software's sophistication.
6. Investment in AI has increased ten-fold
Given opportunities for value creation, venture capital firms are
investing aggressively in AI. Investment dollars into early stage
AI companies globally have increased ten-fold in four years
(fig. 21), to over $5B in 2016 (CB Insights). Today's leading
technology companies including Apple, Amazon, Facebook,
Google, IBM, Microsoft and Salesforce are also spending
heavily on research and personnel to develop and deploy AI.
Investment dollars into early
stage AI companies globally have
increased ten-fold in four years,
to over $5bn in 2016.
CB Insights
Source: CB Insights
Fig. 21. Venture capital investment in AI has increased ten-fold
100
200
300
400
500
600
700
0
2012
AI dealsAI deals (le axis)
2013
2014
2015
2016
$5,120m
$3,126m
$2,677m
$1,040m
$503m
Disclosed fundingDisclosed funding (right axis)
$0
$4,000m
$5,000m
$6,000m
$7,000m
$3,000m
$1,000m
$2,000m
Chapter 3
Why is AI coming of age today?
37
7. Awareness of AI has grown significantly
Public interest in AI, measured by the proportion of Google
searches for 'machine learning', has increased six-fold in
five years (fig. 22).
Executives' awareness of AI has grown following extensive
coverage in business publications. In just the last 12 months,
6,600 articles referencing AI have appeared in Bloomberg
Businessweek, the Financial Times, Forbes, Fortune, the
Harvard Business Review and The Wall Street Journal (Signal
Media). One third of these references have appeared in the
last 12 weeks.
In the popular press, whether relevant (the opportunities and
threats posed by automation) or less so ('killer robots'), over
19,000 articles in US and UK newspapers have referred to AI,
fuelling public interest (Signal Media).
Source: Google Trends
Fig. 22. Interest in AI has increased 6-fold
'Machine Learning' (Proportion of Searches)1Q12 = 1XInterest in AI has increased 6-fold
1X
2X
3X
4X
5X
6X
1Q122Q123Q124Q121Q132Q133Q134Q131Q142Q143Q144Q141Q152Q153Q154Q151Q162Q163Q164Q161Q172Q173Q17In just the last 12 months,
6,600 articles referencing
AI have appeared in
Bloomberg Businessweek,
the Financial Times, Forbes,
Fortune, the Harvard
Business Review and The
Wall Street Journal.
Signal Media
38
The applications
of AI
Chapter 4
Summary
AI has numerous, tangible use cases today that are enabling corporate revenue growth and
cost savings.
The capabilities of AI its power to incorporate broader data sets into analyses, identify
concepts and patterns in data more effectively, and enable human-to-machine conversation
will have application in all sectors and numerous business processes.
Applications will be most numerous in sectors in which a large proportion of time is spent
collecting and synthesising data: financial services, retail and trade, professional services,
manufacturing and healthcare. Applications of AI-powered computer vision will be
particularly significant in the transport sector.
Use cases are proliferating as AI's potential is understood. We describe 31 core use
cases across eight sectors: asset management, healthcare, insurance, law & compliance,
manufacturing, retail, transport and utilities.
We illustrate how AI can be applied to multiple processes within a business function
(human resources).
39
40
Recommendations
Explore the breadth of AI use cases to understand the impact AI will have in the next five years.
Examine AI use cases in a range of sectors to familiarise yourself with the technical capabilities of AI from
incorporating additional data sets into analyses to identifying patterns in data more effectively and understanding
written and spoken language.
Assess the extent to which time is spent collating and processing data in your industry. AI's impact will be
greatest in sectors where data synthesis and processing are core.
Identify business processes in your sector that could be improved, automated or reinvented using AI.
Executives
Chapter 4
The applications of AI
AI offers new opportunities for disruption in sectors ranging from manufacturing to healthcare. Identify business
processes ripe for improvement or reinvention through AI, particularly in sectors in which data synthesis or
processing are extensive.
Customers buy solutions, not technology. If establishing a B2B AI company, focus on a clear sector use case or
business process and articulate a compelling ROI for business buyers.
AI has numerous capabilities, from multi-variate analysis to natural language processing. As appropriate, identify
opportunities to use multiple aspects of AI, both within your company and for buyers.
Familiarise yourself with early stage competitors in your sector (see Chapter 8) and monitor their focus, approach
and capabilities.
Entrepreneurs
Evaluate opportunities and threats to existing portfolio companies from the many applications of AI.
In sectors you favour, evaluate business processes most and least at risk of change through AI.
Leverage domain expertise when evaluating opportunities to invest in AI opportunities. Sector dynamics are
likely to be as, or more, important drivers of early stage companies' success than technological capability.
With AI poised to impact multiple sectors, develop a framework to identify preferred sectors for investment.
Considerations are likely to include fundamentals (scope for structural change in a sector due to AI) and
pragmatic factors (sector expertise).
Investors
41
The applications of AI are numerous
and tangible
AI is not a set of solutions looking for a problem; it is a set of
capabilities unlocking revenue growth and cost savings today.
The capabilities of AI its power to incorporate broader data
sets into analyses, identify concepts and patterns in data more
effectively than rules-based systems, and enable human-to-
machine conversation have applications in all sectors and
numerous business processes. In about 60% of occupations,
at least 30% of constituent activities are technically automatable
by adapting currently proven AI technologies. (McKinsey
Global Institute). As such, AI is a key 'enabling technology'.
Data-centric sectors will see the
greatest impact
In the next ten years, AI will be deployed in all sectors and to
a wide variety of business processes. However, AI will have
more numerous applications and greater impact in some
sectors than others.
AI's impact will be greatest in sectors in which a large
proportion of time is spent collecting or synthesising data,
or undertaking predictable physical work. In several sectors
(fig. 23), professionals spend one third or more of their time
on the above (McKinsey, Julius Baer). These sectors include:
Finance and insurance (50% of time)
Retail, transport and trade (40% of time)
Professional services (37% of time)
Manufacturing (33% of time)
Healthcare (33% of time)
Applications will be more limited in sectors where data
synthesis and processing activities are limited, or where the
majority of people's time is spent managing others, applying
expertise, or undertaking unpredictable physical work.
Occupations such as management and teaching will be
more resilient to AI in the medium term.
Food services
Accommodation
Education
Healthcare
Manufacturing
Admin & Government
Tech, media, telecom
Professional services
Retail, transport, trade
Finance & insurance
0%
20%
18%
18%
23%
33%
33%
34%
36%
37%
40%
50%
40%
60%
% of time spent processing and collecting data
Share of time spent on processing, collecting data
Source: McKinsey, Julius Baer
Fig. 23. Share of time spent collating and processing data
In about 60% of occupations,
at least 30% of constituent
activities are technically
automatable by adapting
currently proven AI
technologies.
McKinsey Global Institute
42
Chapter 4
The applications of AI
Core use cases vary
by sector
Use cases for AI are proliferating as
understanding of the technology
improves. We describe 31 core AI
use cases in eight sectors: asset
management, healthcare, insurance,
law & compliance, manufacturing,
retail, transport and utilities.
Asset management
AI's ability to extract content from unstructured data
using natural language processing, find subtle patterns
in disparate data sets, and enable machine-to-human
communication via chatbots, has multiple applications in
asset management. Core use cases include investment
strategy, portfolio construction, risk management and client
service. By augmenting or automating many of an asset
manager's tasks, AI enables asset managers to deliver to the
mass affluent a degree of personalisation and service quality
previously reserved for high net worth clients. Additionally,
AI can decrease the cost of portfolio construction while
improving quality the era of the 'robo-advisor'.
Investment strategy: AI can improve a firm's investment
strategy by synthesising its research and data, and
incorporating broader data sets including unstructured
information. Superior pattern recognition can then deliver
better multi-objective optimisation. AI can balance a
diverse range of inter-connected objectives (including fund
deployment, risk and profitability) to enhance returns more
effectively than rules-based systems.
Portfolio construction: AI tools can augment, and
increasingly automate, an asset manager's process of portfolio
construction. AI 'robo-advisors' can analyse a client's goals,
and within a firm's investment rules develop personalised,
optimised portfolios at low cost and high speed.
Risk management: AI can improve risk management for the
same reasons it enhances investment strategy: interpretation
of broader data sets and improved cognitive processing. 90%
of data generated today is unstructured information, stored
outside traditional databases (International Data Group).
Natural language processing enables additional data sets to
be incorporated into firms' analyses. Other AI techniques,
including deep learning, then enable patterns in data to be
identified with greater granularity and confidence. Together,
these capabilities enable risks to be identified and quantified
more effectively.
Client service: Chatbot interfaces are being applied within
and beyond asset management firms. Deployed in client-facing
channels, natural language systems enable client enrolment,
support and self-service. Embedded in internal tools, chatbots
let account managers query client details and understand
developments relevant to a client's portfolio in seconds instead
of minutes. Fewer account managers can then provide a higher
quality service to a greater number of clients.
Early stage UK companies include:
Sector
Asset
Management
Investment
strategy
Portfolio
construction
Risk
management
Client
service
Core use cases:
Case law
Discovery and
due diligence
Litigation
strategy
Compliance
Law &
compliance
Diagnostics
Drug discovery
Monitoring
Healthcare
Risk
assessment
Claims
processing
Fraud
detection
Customer
service
Insurance
Predictive
maintenance
Asset
performance
Utility
optimisation
Manufacturing
Autonomous
vehicles
Infrastructure
optimisation
Fleet
management
Control
applications
Transport
Customer
segmentation
Content
personalisation
Price
optimisation
Churn
prediction
Retail
Supply
management
Demand
optimisation
Security
Customer
experience
Utilities
Risk Forecasting

Algo Dynamix
Sales Optimisation
Arkera
Advisory
ForwardLane
Zenith One
Valuation
PriceHubble
Proportunity
Source: MMC Ventures
43
Healthcare
In the next 20 years, AI can unlock a paradigm shift in
healthcare to improve patient care and process efficiency.
Automated diagnosis was an early use case for rudimentary
AI in the 1980s. 'Expert systems' mimicked human
approaches to diagnosis, applying rules-based inferences
to bodies of knowledge. Modern AI, particularly deep
learning, is more effective and applicable to a wider
range of processes. Key use cases include diagnosis,
drug discovery and patient monitoring.

Diagnosis: Replacing complex, human-coded sets of
probabilistic rules, deep learning solutions identify subtle
correlations between vast, multi-variate data sets to deliver
scalable, automated diagnosis. While systems are nascent,
accuracy is improving rapidly. Separately, computer vision
solutions powered by deep learning are transforming
diagnostic imaging. While human radiologists require extensive
expertise and years of training to identify abnormalities in
magnetic resonance images and ultrasounds, deep learning
systems trained on large data sets deliver impressive results.
In 2017, diagnostic imaging powered by deep learnings offers
human-level accuracy and high speed in select contexts.
Drug discovery: Today's drug discovery process is lengthy,
averaging 12 years to market (California Biomedical Research
Association). Expense and uncertainty are also prohibitive;
drug development costs an average of $359m and just 2%
of US preclinical drugs are approved for human use (ibid).
AI is being applied to multiple stages of the drug development
process to accelerate time to market and reduce uncertainty.
AI is being applied to synthesise information and offer
hypotheses from the 10,000 research papers published daily,
predict how compounds will behave from an earlier stage
of the testing process, and identify patients for clinical trials.
Monitoring: Monitoring the vital signs of patients on non-
acute wards, or at-risk individuals in the home, remains a
manual process undertaken periodically. AI can synthesise
signals from inexpensive wearable devices worn by patients to
deliver clinical-grade monitoring, and enable a large group of
patients to be monitored in real-time by a single nurse. As data
sets are amalgamated and algorithms are tuned, AI will offer
predictive analytics. Patients in a ward or at home who require
further hospital care can be identified and supported, while
unnecessary use of hospital beds can be reduced.
Early stage UK companies include:
Insurance
While the fundamentals of insurance customer
prospecting, risk assessment, claims processing and fraud
detection have remained unchanged, modern AI can
improve every stage in the insurance process to deliver
efficiency savings and improved customer experience.
By identifying patterns in data better than rules-based
systems, AI can improve and accelerate decision-making
and claims processing, reduce fraud and automate a large
proportion of customer service enquiries.
Risk assessment: AI can gather information from broader data
sets, including web and social media profiles, to compile richer
customer information and inform risk assessment. AI can then
assess the risk of individual policies more accurately than rules-
based systems, by detecting non-linear patterns in multi-variate
data sets and making projections.
Diagnostics/Information
Aequa Science
Babylon Health
BrainWaveBank
Sime Diagnostics
Transformative
Your.MD
Workflow Optimisation
Clinithink
Deontics
Kaido
Kraydel
Optellum
Synthace
Visulytix
Movement Diagnostics
AIMO
Mental Health
Ieso Digital Health
Diagnostic Imaging
Avalon AI
Innersight Labs
Kheiron
ThinkSono
Viz
Genomics
Desktop Genetics
InsideDNA
PetaGene
Resurgo Genetics
Surgical Robotics
Cambridge Medical Robotics
Healthcare IoT
Drayson Technologies
Snap40
Drug Discovery
Exscientia
HealX
LabGenius
BenevolentAI
44
Chapter 4
The applications of AI
Claims processing: AI can reduce time-to-quote, time-to-
claim and claims processing costs for consumers and insurers.
By analysing images of damaged assets, AI can automatically
classify claims. Through improved pattern recognition
applied to prior cases, AI can also predict settlement costs.
Algorithms using deep learning are effective for image analysis,
while Bayesian (probability-based) AI is useful for predicting
settlement costs.
Fraud detection: Insurance fraud costs UK insurers 1.3B
annually and ads 50 to the average policyholder's annual
bill (Association of British Insurers). UK insurers invest
over 200m annually to tackle the challenge (ibid). Fraud
detection algorithms enhanced with AI can identify fraudulent
transactions, while reducing false positives, more effectively
than traditional rules-based or linear regression approaches.
Customer service: Chatbot interfaces integrated with insurers'
databases can use natural language processing to offer 24/7
product information and answers to policyholders' enquiries
in a scalable, inexpensive and personalised channel.
Early stage UK AI companies include:
Brolly, CoVi Analytics, Oseven, Spixii
Law and Compliance
AI's abilities to process language in documents, synthesise
knowledge and automate reasoning have broad application
in the legal services and compliance sector. With junior
lawyers spending a high proportion of their time accessing
and collating information, scope for augmentation and
automation is considerable. Key AI use cases include
identifying relevant case law, processing documents
for discovery and due diligence, and informing
litigation strategy.
Regarding compliance, costs have grown significantly
since 2008 particularly for financial services firms. With
rules-based software poorly suited to catching infractions,
banks have invested in additional compliance personnel.
Citi, while reducing its global headcount 32% between
2008 and 2016, doubled its regulatory and compliance
staff to 29,000 over 13% of it workforce (Citi). AI's ability to
learn patterns of behaviour over time, and highlight unusual
activity in real-time, offers greater scalability at lower cost.

Case law, discovery and due diligence: Natural language
processing AI can identify, classify and utilise content from
databases and unstructured documents at scale and speed,
saving legal firms time and cost for routine document review.
Use cases include sourcing and ranking relevant case law and
identifying key documents in due diligence and discovery
processes. With a merger and acquisition data room containing
an average of 34,000 pages for review (Luminance), AI can
increase business velocity and reduce costs.
Litigation strategy: AI can analyse past judgements at greater
speed, granularity and subtlety than has been possible to date.
By anticipating the probability of different outcomes, lawyers'
strategic decision-making can be informed and enhanced.
In high volume areas, such as personal injury, software can
help a firm decide whether to accept a case. In high value
areas, including corporate litigation, software can suggest
the probability of a particular outcome based on juries'
prior behaviour and opposing lawyers' tendency to settle
or proceed to trial.
Compliance: Preventing accidental or deliberate breaches
of policy, from the theft of sensitive data to accidentally
misaddressing an email containing a customer database, is
challenging for rules-based systems. By learning the habits
of users over time, AI systems can flag potential compliance
breaches in real-time, before they occur, with sufficient
accuracy to enable broad deployment.
Early stage UK companies include:
Identity Verification
AimBrain
Callsign
Eyn
iProov
Onfido
VChain Technology
Data Anonymisation
Anon AI
Data Classification
Exonar
Semantic Evolution
Regulation Analytics
ACognitiv+
CoVi Analytics
WaymarkTech
Contract Analysis
Eigen Technologies
Luminance
ThoughtRiver
Email Security
CheckRecipient
Monitoring Optimisation
Recordsure
45
Manufacturing
AI can significantly improve manufacturers' efficiency and
profitability. Overall Equipment Effectiveness (OEE), a
measure of manufacturers' productivity relative to potential,
varies considerably by industry, from 75%-91% (LNS
Research). The performance of companies within the same
industry also vary widely, offering scope for competitive
advantage. AI can boost OEE and profitability by predicting
equipment failure (to reduce unplanned downtime),
improving assets' operational efficiency, and reducing
utility supply costs.
Predictive maintenance: Failure of production assets is
costly; one hour of unplanned downtime on an automotive
assembly line can cost a manufacturer 1.5m (MMC Ventures).
AI can identify subtle patterns in data from vibration,
temperature, pressure and other sensors to identify leading
indicators of equipment failure. By predicting more accurately
which components are likely to fail, and when, parts can be
proactively replaced to prevent failures and save money.
Asset performance: AI can improve the operation of high
value assets, including gas and wind turbines, to optimise yield.
Rules-based programs deliver limited results when applied
to complex tasks, such as tuning fuel valves on a gas turbine
to optimise combustion while reducing wear and emissions.
Applying neural networks to optimise the turbine inputs can
improve results by 20% or more.
Utility optimisation: Optimising the purchase and
consumption of utilities, such as power and water, according
to real-time demands on a factory floor is too challenging and
variable to manage using rules-based software. AI enables
companies to anticipate, and align, utility consumption with
process requirements in real-time, lowering utility consumption
by 5% or more.
Early stage UK companies include:
Retail
E-commerce, now 17% of UK retail sales and growing
(eMarketer), has transformed the quantity, breadth and
granularity of data available to retailers. Retailers that
turn data into insight can increase competitive advantage
by engaging, monetising and retaining customers more
effectively. Every stage of a retailer's customer journey
from lead generation and content selection to price
optimisation and churn prediction can be improved by AI
algorithms that ingest richer data sets and identify patterns
in them better than rules-based systems. By enabling
analytics at the 'per-customer' level, AI is introducing the
era of retail personalisation. Leaders enjoy competitive
advantage; 75% of Netflix users select films recommended
to them by the Company's AI algorithm.
Customer segmentation: Limitations in available data, and
the linear analysis of information, inhibit the ability of traditional
customer segmentation software to identify desirable customer
attributes. Deep learning algorithms enable natural language
processing, which lets retailers access additional data sets
including social media data. Deep learning algorithms also
offer more granular analysis than rules-based systems, to
optimise segmentation, channel selection and messaging.
Content personalisation: Most content presented to online
shoppers is irrelevant or poorly suited to users' preferences,
reducing conversion to an average of 1.0% on smartphones and
2.8% on desktops (Adobe). As with customer segmentation,
AI offers additional unstructured data sets for analysis, and
improved multi-variate analysis to identify more subtle
correlations than rules-based systems can detect. When Netflix
recommends content to a user, in addition to analysing a user's
actions, ratings and searches, the Company's AI algorithm
considers social media data and meta-data from third parties.
Process Optimisation
Flexciton
Materialize.X
CloudNC
Predictive Maintenance
Senseye
IoT
Thingtrax
AI can identify subtle
patterns in data from
vibration, temperature,
pressure and other
sensors to identify
leading indicators of
equipment failure.
46
The Company is now analysing images from content, including
colour palette and scenery, for deeper personalisation.
Price optimisation: A 1% change in price provides, on
average, a 10% change in profitability (BlueYonder). The smaller
a company's margins, the greater the impact. Willingness
to pay is a key determinant for price. AI enables price
optimisation that is more sophisticated than traditional 'cost
plus', 'relative-to-competitors' or 'odd pricing' (0.99) models.
By identifying correlations within and between data sets,
AI can better optimise for factors including price elasticity,
revenue, profit, product availability and phases in a product's
lifecycle (introduction or end-of-life).
Churn prediction: Traditional programs struggle to
incorporate new sources of information, maximise the value
from multi-variate data sets or offer granular recommendations.
AI-powered churn prediction can identify leading indicators
of churn more effectively, and improve remediation by
predicting more accurately the format and content of
successful interventions.
Early stage UK companies include:
Transport
The transport sector will be transformed by AI.
Breakthroughs in computer vision are enabling the age of
autonomous vehicles self-driving cars, buses and trucks.
The implications, from shifts in sector value chains to new
business models, will be profound (see Chapter 5). As
well as enabling autonomy, AI can be applied to the many
prediction and optimisation challenges from congestion
modelling to fleet management at the core of today's
logistics networks.
Autonomous vehicles: AI computer vision systems enable
vehicles to sense and identify the physical features and
dynamics of their environment, from road lanes to pedestrians
and traffic lights, with a high degree of accuracy. Combined
with AI data processing and planning algorithms, AI is enabling
the age of autonomous transport. Cars, buses and trucks will
be able to operate and guide themselves, without human
involvement. SAE International, a US-based global professional
association and standards body, has identified five degrees
of vehicle autonomy, from Level 0 (no automation) to Level 5
(full automation; no requirement for human control). Select
companies, including Google, intend to release vehicles
offering Level 5 automation. Challenged by the autonomous
vehicle programmes of Google, Uber and Tesla, incumbent
manufacturers are accelerating their own initiatives by
increasing investment and making acquisitions. Ford intends to
deliver high-volume availability of at least a Level 4 autonomous
vehicle by 2021.
Infrastructure and system optimisation: AI's abilities to
detect patterns and optimise complex data are being applied
to traffic, congestion and infrastructure challenges in transport
Chapter 4
The applications of AI
Analytics/Optimisation
Boldmind
Hero
Connected Device
JCC Bowers
Third Space Auto
Store Analytics
Hoxton Analytics
Presence Orb
Proximus
ThirdEye
Augmented Reality
DigitalBridge
Metail
Home Device
Cocoon
Emotech
Product Recommendation
Cortexica
Orpiva
Pasabi
See Fashion
Thread
75%
of Netflix users select films
recommended to them by the
Company's AI algorithms.
Source: Netflix
47
systems. Predicting traffic flows, or modelling the deterioration
of transport infrastructure, are difficult because inputs are
complex (combining traffic, construction and environmental
data) and because the relationships between inputs and
outputs are non-linear (Transportation Research Circular).
In these contexts, machine learning and deep learning
systems are well suited to deliver better results than rules-
based systems.
Fleet management: Transportation fleets are pervasive,
from the logistics networks that underpin the economy to taxi
fleets and food delivery services that provide point-to-point
convenience. AI can optimise pick-ups, route planning and
delivery scheduling to maximise asset utilisation, while taking
into account economic, social and environmental impacts.
Control applications: Machine learning systems are well
suited to the numerous prediction and optimisation challenges
presented by air traffic control, vehicle traffic signalling, and
train control.
Early stage UK companies include:
Utilities
Information processing will become critical to utility
companies, and their business models, as the utility sector
undergoes a greater change in the next 25 years than it
has during the previous 150. 'Prosumers' consumers
who also own capacity for energy production will require
integration into the energy market. By processing data
more intelligently, AI will be a significant value driver in this
transition. AI use cases for utility companies are varied, from
demand optimisation and security to customer experience.
The foundations for AI adoption in the utilities sector are
robust. 67% of utility companies a higher proportion
than in any other sector use 'internet of things' (IoT)
technologies such as sensors (Gartner). Further, compared
with peers in other sectors, utility CIOs have a stronger focus
on cost reduction, managing geographically dispersed
assets and security.
Supply management: AI algorithms can predict changes
in supply, including those caused by the intermittency of
renewable resources, more effectively than rules-based
systems enabling smaller reserves and greater cost savings.
AI solutions can also optimise supply networks, which are
becoming increasingly complex as consumers deploy sources
of renewable energy that contribute energy back to the
National Grid.
Demand optimisation: By identifying detailed patterns in
consumer behaviour, AI algorithms can move consumption
of energy from periods of peak use and high prices to times
of lower demand and cost.
Security: Rules-based systems struggle to deliver system
security given the continually evolving nature of security threats.
By identifying abnormal patterns in network behaviour, deep
learning systems can identify breaches in network security that
elude traditional programs.
Customer Experience: Chatbots, which offer natural
language conversations powered by deep learning algorithms,
offer consumers self-service account administration, product
information and customer service.
Early stage UK companies include:
Autonomous Vehicles
Baro Vehicles
FiveAI
Oxbotica
Machines With Vision
Predina
Intelligent Infrastructure
Alchera Technologies
Open Capacity
Location Intelligence
NumberEight
TravelAI
67%
of utility companies a higher
proportion than any other sector
use 'internet of things' (IoT)
technologies such as sensors.
Source: Gartner
Grid Optimisation
Biscuit
Limejump
Intelligent Energy Systems
Green Running
Grid Edge
48
For an alternative perspective, we can explore the
applications of AI for business functions ('horizontals' such
as compliance, human resources, technology, sales or
marketing) instead of sectors ('verticals'). Over time AI will
become normalised a part of developers' standard toolkit
used to improve, and then reinvent, business functions.
In the human resources (HR) function, for example, AI is
reducing costs and improving outcomes in sub-functions
including recruitment, workforce management and learning.
In recruitment, AI can address buyers' primary pain points
(fig. 24): identifying high quality hires that meet hiring
managers' criteria, reducing time-to-hire, and managing cost-
to-hire (KellyOCG). Inefficiencies are considerable. On average,
pre-interview screening of applicant CVs consumes 60%
more than 20 days of a company's hiring process (iCIMS
Inc) (fig. 25, overleaf). The challenge is exacerbated by an
increasing number of applicants per role, a result of the
proliferation of job boards and shorter average job tenure.
Performance monitoring
Tech effectiveness
Poor processes
Quality of recruiters
Cost to hire
Time to hire
Hiring manager satisfaction
Quality of hires
0%
20%
30%
50%
10%
40%
60%
70%
Conditions that slow or stall the hiring process
(% respondents)
Quality, time and cost are key recruitment pain points
Source: KellyOCG
Fig. 24. Quality, time and cost are key recruitment pain points
Business function case study
Human Resources
Chapter 4
The applications of AI
49
Companies can improve candidate targeting by using
AI-driven natural language processing to review social media
profiles and develop fuller prospect profiles. AI can also
identify patterns in disparate data sets for example, visits
to a Company's recruitment page and a person's affinity for
certain brands to enable outreach to prospects who are
likely to engage. Further, by detecting indicative patterns of
behaviour, AI enables companies to target 'passive candidates'
prospects who may be open to new roles but have not
publicly begun a search.
During the applicant screening process, AI can reduce time-
and cost-to-hire, increase hiring quality, and improve candidate
experience. By extracting, structuring and identifying patterns
in candidates' applications, AI solutions can automate a high
proportion of first-round filtering. Chatbots can assist, soliciting
missing information from candidates and boosting candidate
engagement by answering their common questions scalably
and at low cost. AI is also in the early stages of predicting a
candidate's performance in a specific role, cultural fit, and
probability of leaving. In technical roles, such as coding, AI can
analyse candidates' published code and compare its quality
and style to a desirable baseline. In other roles, by identifying
correlations between candidate test result data and corporate
performance information, AI can predict whether a candidate
is likely to perform strongly in a particular role, or resign after a
short tenure. This is highly valued in industries in which rates of
churn, and therefore recruitment costs, are high.
Applying AI to HR presents challenges as well as opportunities.
Vendors must overcome caution regarding new technology,
procure access to disparate data sets, avoid reinforcing
existing biases in decision-making, and address questions
of explainability what was the basis of their solution's
recommendation?
Application
Screening
Review
Interviewing
Hiring
Pre-interview screening occupies 60% of companies' hiring process
UK
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
23%
23%
2%15%
37%
Hiring process duration
Source: iCIMS Inc.
Fig. 25. Pre-interview screening and review occupies 60% of companies' hiring process
In recruitment, AI can address buyers'
primary pain points: identifying high
quality hires that meet hiring managers'
criteria, reducing time-to-hire, and
managing cost-to-hire.
50
Chapter 5
Summary
AI's value can be abstracted to four benefits: innovation (new products and services); efficacy
(the performance of tasks more effectively); velocity (the completion of tasks more quickly);
and scalability (the extension of capabilities to new market participants).
These benefits will have significant implications for companies, consumers and society,
including: the introduction of new market participants; shifts in sector value chains; new
commercial success factors; shifts in companies' competitive positioning; new business
models; shifts in skills and organisational design; accelerating cycles of innovation; and
new benefits and risks to society.
By automating capabilities previously delivered by human professionals, AI will reduce
the cost and increase the scalability of services, significantly broadening participation
in select markets.

In multiple sectors AI will change where, and the extent to which, profits are made within
a value chain.
New commercial success factors will determine a company's ability to be successful in
the age of AI.
New leaders, followers, laggards and disruptors will emerge as the paradigm shift to AI causes
significant shifts in companies' competitive positioning.
AI, growth of 'x-as-a-service' consumption, and subscription payment models will obviate
select business models and offer new possibilities in sectors including transport, insurance
and healthcare.
As AI gains adoption, the skills that companies seek, and companies' organisational structure,
will change.
By reducing the time required for process-driven work, AI will accelerate the pace of business
and innovation. This may compress cycles of creative destruction, reducing the period of time
for which all but a select number of super-competitors maintain value.
AI will provide benefits to society including improved health, broader access to services and
more personalised experiences. It will also present risks and dilemmas, including issues of job
displacement, bias, conflict and privacy.
The implications
of AI
51
52
Recommendations
Executives
Evaluate how the benefits unleashed by AI innovation, efficacy, velocity and scalability will impact your industry.
Consider if AI can be used to reach new market participants and grow your addressable market.
Assess the shifts in your industry value chain that will occur as AI adoption grows.
Evaluate the business model a disruptor might adopt in the age of AI, if freed from the "innovator's dilemma".
What would the Netflix to your Blockbuster look like?
Assess the extent to which your company is developing the commercial success factors required for the age of AI.
Companies' competitive positioning will change as adoption of AI increases. Develop an AI strategy to become
a leader rather than a laggard.
Evaluate the suitability of your company's skills and organisational design in light of changes AI will necessitate.
Recognise the need for responsible stewardship. AI presents risks to society including issues of job
displacement, bias, and privacy as well as benefits.
Chapter 5
The implications of AI
Identify opportunities to take advantage of probable shifts in sector value chains that will be caused by AI.
Develop initiatives that will take advantage of the new market participants and business models that AI
will present.
Identify weaknesses in incumbents' competitive positioning that are likely to persist, or worsen, given their
structure or strategy.
Entrepreneurs
Assess how the innovation, efficacy and scalability enabled by AI will impact your existing portfolio companies.
Identify investment opportunities in sectors that will be transformed as a result of AI altering value chains and
enabling new market participants.
Evaluate opportunities to invest in companies structured around business models that will come of age as AI
disrupts existing markets.
When evaluating incumbents, assess the extent to which they could develop the commercial success factors
required for success in the age of AI.
Investors
53
AI will deliver innovation, efficacy, velocity
and scalability
AI's value, from finding patterns in data more effectively
to automating previously manual tasks, can be abstracted
to four key benefits:
AI will have significant implications
Innovation, efficacy, velocity and scalability will have
significant implications for economic systems, employees,
consumers and society. AI will lead to:
1. New market participants
2. Shifts in sector value chains
3. New commercial success factors
4. Changes in companies' competitive positioning
5. New business models
6. Shifts in skills and organisational design
7. Accelerating cycles of innovation
8. Benefits and risks for society
1. New market participants
By automating capabilities previously delivered by human
professionals, AI will reduce the cost and increase the
scalability of services, significantly broadening participation
in select markets.
Today, access to sectors including healthcare and financial
services is limited to subsets of the global population.
Medical diagnosis, for example, is inaccessible to people in
developing economies and expensive for those in developed
nations. Diagnosis has been undertaken by experienced
professionals, whose training is time consuming and whose
scalability is limited, inhibiting supply and increasing cost.
AI will provide automated diagnosis for a growing proportion
of conditions. The marginal cost of diagnosing a patient with
an AI algorithm will be nil. With smartphone adoption in
developing economies increasing rapidly, from 37% in 2017
to an estimated 57% by 2020 (GSMA), barriers to access are
also falling rapidly. By transferring the burden of diagnosis from
people to software, global access to primary care will increase.
Millions of additional individuals will benefit from primary
care, while the market for providers of relevant and associated
technologies will expand.
Benefit
Innovation
Efficacy
Velocity
Scalability
Explanation
New products
and services.
Perform tasks
more effectively.
Complete tasks
more rapidly.
Extend capabilities
to additional market
participants.
Examples
Autonomous vehicles
Voice-controlled devices
Fraud detection
Customer segmentation
Legal document processing
Manufacturing process optimisation
Automated medical diagnosis
Automated executive assistants
Source: MMC Ventures
54
Chapter 5
The implications of AI
2. Shifts in sector value chains
In multiple sectors AI will change where, and the extent to
which, profits are made within a value chain.
In the insurance sector, revenue from car insurance accounts for
42% of global insurance premiums (Autonomous Research). As
AI-powered autonomous vehicles gain adoption, the frequency
of accidents will reduce and with them, insurers' revenue.
UK car insurance premiums are expected to fall by as much as
63%, causing profits for insurers to fall by 81% (Autonomous
Research). Insurers must anticipate and plan for a profound shift
in their Sector's value chain.
In the legal services sector, clients are increasingly aware, and
less willing to pay, for deliverables that have not required the
time or expertise of an experienced lawyer. In March 2017,
Deutsche Bank announced that it will no longer pay City law
firms for legal work undertaken by trainees and newly qualified
lawyers. The automation enabled by AI will broaden the range
of tasks that can be provided to clients at low cost. As clients
expect greater use of AI, cost pressures on routine work will
increase and value will shift further to high-end work.
In the transport sector, automotive finance provides 19%, on
average, of car manufacturers' pre-tax profits (MMC Ventures).
Large automotive finance companies, including Ford Motor
Credit, Toyota Financial Services, Nissan Motor Acceptance
Corp and Hyundai Motor Finance loan consumers money to
buy new cars. As we describe below ('New business models'),
private vehicle ownership will reduce as subscription-based
services provide consumers with on-demand access to fleets
of autonomous vehicles. Demand for, and value in, automotive
finance for consumers is likely to decline.
The automation enabled by
AI will broaden the range of
tasks that can be provided
to clients at low cost.
As clients expect greater
use of AI, cost pressures on
routine work will increase
and value will shift further
to high-end work.
3. New commercial success factors
New commercial success factors will determine a company's
ability to be successful in the age of AI.
A paradigm shift in technology offers companies new benefits
while demanding new competencies. Cloud computing,
for example, offered flexibility, scalability, reduced capital
expenditure and faster upgrade cycles. However, it demanded
new diligence processes, different supplier relations and
dynamics, internal competencies in change management
and paying greater attention to security.
Success factors in the age of AI include:
1. The vision to embrace AI and the organisational changes
it requires.
2. Ownership of large, non-public data sets to train and
deploy market-leading AI algorithms.
3. A willingness to evaluate the opportunities and risks of
sharing training data with partners and competitors.
4. The ability to attract, develop, retain and integrate data
scientists within an organisation.
5. The ability to form effective partnerships with best-of-breed
third-party AI software and service providers.
6. The ability to diligence AI partners effectively.
7. A willingness to understand and respond to regulatory
challenges posed by AI.
8. A shift in mindset to the use of software that provides
probabilistic instead of binary recommendations.
9. The ability to manage workflow changes that result from
the implementation of AI systems.
10. The ability to manage challenges of organisational
design and culture as AI augments, and in some cases
replaces personnel.
42%
of global insurance premiums
come from car insurance
Source: Autonomous Research
55
4. Changes in companies' competitive positioning
New leaders, followers, laggards and disruptors will
emerge as the paradigm shift to AI causes significant
shifts in companies' competitive positioning.
Paradigm shifts in technology destabilise incumbents
and enable new leaders to emerge. As adoption of cloud
computing continues, for example, IT spend is being
reallocated to cloud-native platforms (such as Amazon)
and applications at the expense of incumbents (fig. 26).
AI will cause greater shifts as it alters value chains, enables
new business models and demands different success factors
from competitors. We expect 'Platforms', 'Disruptors',
'Leaders' and 'Laggards' to emerge.
Top increase spendingPercent of responses expecting vendors with largest gain or loss of
incremental share of IT budget, from a shi to cloud in next three years
Bottom decrease spendingAmazon
Microso
Google
Gain
Lose
Capgemini
NetApp
EMC
Oracle
IBM
Hewlett-Packard Enterprise
Salesforce.com
0%
10%
20%
30%
Source: AlphaWise, Morgan Stanley Research
Fig. 26. Paradigm shifts disrupt incumbents
56
Among providers of AI:
Platforms primarily Google, Amazon, IBM and Microsoft
(GAIM) provide the AI infrastructure, development
environments and 'plug and play' AI services used by many
developers and consumers of AI. With vast data sets, world-
class AI teams and extensive resources, select GAIM vendors
are well positioned to accrue value as platforms that support
the provision of AI.
GAIM do not, however, have the data advantage, expertise
or strategic desire to address the myriad domain-specific
use cases required by businesses in sectors ranging from
manufacturing, agriculture and education to retail, professional
services and finance. This presents opportunities for Disruptors.
Disruptors are early stage, AI-led software companies tackling
business problems in a novel way using AI. For incumbents,
Disruptors are a double-edged sword. Disruptors will enable
the enterprises, small- and medium-sized businesses that
embrace them, while eroding the value of those that lack
the foresight to do so. Select Disruptors will become
tomorrow's incumbents or be acquired by today's.
Among buyers of AI (today's enterprises, and small and
medium-sized businesses):
Leaders will emerge in key industries, by: anticipating the
shifts in value chains and business models caused by AI;
taking advantage of their large, proprietary data sets to train
and deploy AI algorithms; having the organisational ability to
deploy AI effectively; and by having sufficient resources and
reputation to attract high quality AI talent. Leaders will extend
their competitive advantage and enjoy particular benefits:
1.
In the 'data economy', economic returns will accrue
disproportionally to companies that can extract value
from information most effectively.
2. Data network effects create wider competitive moats.
Larger volumes of training data enable better algorithms,
which deliver better products and services, which win
more customers, who provide more data. Leaders will
benefit from data network effects that competitors will
struggle to overcome.
Laggards are buyers that lack the will or organisational ability
to use AI effectively. While some enterprises will lack the
foresight to adapt, more will falter due to limited organisational
capability. Laggards will: move slowly to partner with Disruptors
or invest in their own AI teams; fail to take advantage of the
extensive data sets and resources at their disposal; and
struggle to attract AI talent. In the 'data economy', laggards
will lose competitive advantage and market share significantly
and rapidly.

5. New business models
AI, growth of 'x-as-a-service' consumption, and subscription
payment models will obviate select business models and
offer new possibilities in sectors including transport,
insurance and healthcare.
The greatest impact of new corporate and consumer
technologies is the new business models they enable, not
the technical capabilities they provide.
In the transport sector, AI will transform the economic fabric
of ownership and insurance. Cars are parked for an average of
96% of their lives (UITP Millennium Cities Database). Despite
the cost and inefficiency of private car ownership, the model
has been necessary to enable spontaneity, point-to-point
convenience, comfort, privacy and security during travel.
An autonomous vehicle, summoned whenever required from
a distributed fleet and used for the duration of a journey, will
offer the same benefits while optimally utilising a fleet.
With the cost of the driver removed, and the cost of the vehicle
and insurance divided over a greater volume of trips in
a given period, the marginal cost of a journey will be lower.
With growing use of transport-as-a-service subscription
models, in which consumers pay a low monthly fee for on-
demand access to a fleet of autonomous vehicles, private car
ownership is likely to decline.
The impact on 'downstream' market participants will be as
significant. The business models of local car dealerships,
vehicle repair centres, petrol stations and charging centres will
change as local ownership of private vehicles is displaced by
large, managed fleets.
In the insurance sector, associated business models will be
disrupted. The object of car insurance is likely to change,
from a driver (who will play no role in an autonomous vehicle's
operations) to the vehicle manufacturer or service provider. The
immediate buyer of car insurance will also change, from the
end user to the manufacturer or service provider. (Ultimately,
the fee will be repaid by the end user as a small component of
their monthly subscription fee). Accordingly, insurers' business
models in the automotive sector may shift from private policies
to fleet-based agreements. Today, 87% of car insurance
policies are personal, not commercial. This may fall to 40%
(Autonomous Research).

Chapter 5
The implications of AI
57
6. Shifts in skills and organisational design
As AI gains adoption the skills that companies seek, and
companies' organisational structure, will change.
As companies vie for leadership in the AI era, companies
will seek different personnel and change the organisational
principles around which they are structured.
41% of companies are considering the impact of AI on
future skill requirements (PWC). A mix shift to employing
data scientists is likely. Data scientists extract meaning from
data by collating, cleaning and processing data and then
applying statistical techniques and AI algorithms. Companies'
engagement with data scientists is limited today. For example,
while the world's largest professional services and consulting
firms average 5,000 to 15,000 in-house analytics professionals,
we estimate that fewer than 8% of these are data scientists
(MMC Ventures). Some large companies have as few as 100
data scientists. Tomorrow's leaders are aggressively expanding
their data science teams, recognising that time to market is key
because of the potential for competitive advantage through
data network effects (more data yields better algorithms, which
provide improved products that attract more clients and data).
While adjusting their mix of personnel, companies will alter
their organisational design. Hiring for adaptability will be
increasingly important, as the range of tasks supported or
undertaken by AI systems increases. One in three companies
are redesigning their organisational structures from traditional
hierarchies to multi-disciplinary teams (Deloitte) to enable
greater adaptability.
7. Accelerating cycles of innovation
By reducing the time required for process-driven work,
AI will accelerate the pace of business and innovation.
This may compress cycles of creative destruction, reducing
the period of time for which all but a select number of super-
competitors maintain value.
With several occupations, and numerous constituent activities,
automated or augmented with AI, the speed at which tasks
can be completed will increase. By accelerating the pace of
business, AI is likely to shorten cycles of innovation, adoption
and consumption that have been compressing since the 1950s
(fig. 27).
Source: European Environment Agency, based on Kurzweil
Fig. 27. Cycles of innovation, adoption and consumption are compressing
Pre-interview screening occupies 60% of companies' hiring process
Time before mass use
Long
Short
Black and white television
26
Invention available to the general public
1926
1873
1897
1876
1870
1880
1890
1900
1910
1920
1930
1940
1950
1960
1970
1980
1990
2000
2010
1983
1991
1991
1979
1975
1951
Computer
16
Mobile phone
13
Compact disc
12
7
10
World Wide Web
Smartphones
31
Radio
Telephone
35
Electricity
46
Colour television
18
Years neccessary for an invention
to be used by a quarter of the US population
58
Historically, accelerating cycles of innovation have reduced
the period of time for which large companies retain value. In
1965, companies in the S&P500 stayed in the index for an
average of 33 years (Innosight). By 1990, average longevity had
narrowed to 20 years. By 2012, 18 years was typical. By 2026,
average tenure in the S&P500 is forecast to shrink to 14 years
(Innosight). While reduced longevity in stock market indices
arises partly due to technical factors, such as increasing merger
and acquisition activity, creative destruction of incumbents has
been accelerating. Faster cycles of disruption due to AI could
reduce, further, large companies' ability to maintain value.
However, the dynamics of AI, and today's market leaders, may
result in a bifurcation in longevity and the emergence of a small
number of super-competitors. Three factors could lead to the
emergence of super-competitors that maintain value for longer
than companies in recent history.
First, AI offers network effects through data. Because training AI
algorithms typically requires large volumes of data, companies
with large, proprietary data sets can deliver more effective
AI systems. Superior systems provide better results, which
attract more customers, who bring additional data creating
a virtuous circle and powerful defensibility. Several of today's
largest technology companies including Google, Amazon,
Apple and Microsoft have vast consumer data sets inaccessible
to Disruptors.
Second, today's leading technology companies are investing,
and expanding, into emerging technologies and product
categories more forcefully than many companies in the past.
Leading technology companies are disrupting themselves.
Google, a company conceived to index pages on the world
wide web, has become a leader in autonomous vehicles
and quantum computing. Amazon, a company that sold
books online, is becoming a force in so many sectors that the
Company is mentioned on 10% of all US company quarterly
earnings calls (Reuters).
Today's leading technology
companies are investing,
and expanding, into
emerging technologies
and product categories
more forcefully than many
companies in the past.
Third, select 21st century technology companies are
consolidating power by expanding up, and down, the
technology 'stack'. Providers of cloud storage, such as
Amazon and Microsoft, are layering ever-higher levels of
functionality such as AI and security into the environments
they provide. Technology leaders are also expanding down the
technology stack. Google and Apple now develop their own
microprocessors for competitive advantage in mobile and AI
computing. By expanding up and down the technology stack,
companies can consolidate control and customer spend.
The combination of data network effects, greater investment
in emerging technologies and product categories, and
expansion up and down the technology stack may enable a
small number of super-competitors to capture and maintain
economic influence for a longer period of time than has been
possible in recent history amidst a broader bifurcation in
corporate longevity.
The dynamics of AI, and
today's market leaders, may
result in a bifurcation in
companies' longevity and
the emergence of a small
number of super competitors.
8. Benefits and risks to society
AI will provide benefits to society including improved
health, broader access to services and more personalised
experiences. It will also present challenges and dilemmas,
including issues of job displacement, bias, conflict and
privacy.
The benefits of AI for societies will be profound and
numerous. They include: broader access to better and less
expensive healthcare; increased mobility and fewer accidents;
broader access to lower cost legal services; increased
agricultural productivity and manufacturing capability;
more efficient and satisfying retail experiences; improved
management of financial assets and risk; accelerated cycles of
innovation; and greater day-to-day convenience.
AI will also present significant challenges and dilemmas.
Job displacement is a significant risk associated with the
proliferation of AI. AI will directly enable the automation of
several occupations that involve routine and repetition from
Chapter 5
The implications of AI
59
driving to telemarketing. Truck driving comprises 3.6 million
jobs in the US (American Trucking Association). In many
other occupations, AI will augment and then displace some
workers in more complex roles, while reducing the need for
additional workers to be hired as companies expand. In about
60% of occupations, at least 30% of constituent activities are
technically automatable by adapting currently proven
AI technologies (McKinsey Global Institute).
Analysis of UK census data since 1871 shows that historically,
contracting employment in agriculture and manufacturing a
result, in part, of automation have been more than offset
by rapid growth in the caring, creative, technology and
business service sectors (Deloitte). Greater automation of both
manual and business service roles, however, may concentrate
employment further in occupations resistant to automation,
including care work and teaching. Whether or not, over time,
AI creates more jobs than it destroys, the short time frame in
which a large number of workers could be displaced, coupled
with a reduction in the availability of similar roles, could prevent
those who lose their jobs from being rapidly re-absorbed into
the workforce. Social dislocation, with political consequences,
may result.
Whether or not AI creates
more jobs than it destroys,
the short period of time in
which a large number of
workers could be displaced,
coupled with a reduction
in the availability of similar
roles, could prevent those
who lose their jobs from
being rapidly re-absorbed
into the workforce.
A second risk is that AI reinforces existing social inequalities
and prejudices. AI has the potential to free decision-making
from human bias by finding objective patterns in large data
sets. However, AI systems learn by processing training data.
The data sets available reflect systemic historic biases, including
those of gender and race. The results from 'word embedding',
an AI technique that has proven effective at interpreting written
and spoken language, are an example. Word embedding
creates mathematical representations of language. The
meaning of a word is abstracted to a set of numbers based
on the words that frequently appear near to it. However,
when trained on the Common Crawl data set (a 145-terabyte
collection of data taken from material published online),
the word 'women' is closely associated with occupations
in the humanities and the home, while 'man' is associated
closely with science and technology professions (Caliskan,
Bryson and Narayanan). From recruitment decisions to the
provision of loans, algorithms will make decisions that have
significant ramifications for individuals. Unless issues of bias are
recognised and addressed, algorithms may learn and reinforce
human prejudices.
The proliferation of autonomous weapon systems pose an
additional risk. Weapon systems have incorporated a degree
of autonomy for decades. The Phalanx CIWS weapon system,
for example, defends ships in 20 countries' navies from missile
attacks. The Phalanx combines a 20mm rotating Vulcan cannon
with an automated system to interpret radar data, decide
whether a target is a threat, and engage it. However, the
combination of AI-powered computer vision systems, AI-based
decision-making algorithms, and improved robotics, empower
humanoid and aerial drones with greater capability and
autonomy. The risk of 'killer robots' turning against their masters
may be overstated. Less considered is the possibility that
conflict between nations may increase if the human costs of
war are lower. A country that thinks twice about sending young
people into conflict may be more adventurous if the only assets
in harm's way are equipment.
Governments and citizens will also need to re-evaluate the
balance between security and privacy they desire.
AI-powered facial recognition systems offer unprecedented
capability. This technical evolution coincides with the
proliferation of high resolution cameras. Every smartphone
owner carries a camera in their pocket, while over 1.85
million CCTV cameras were in place in the UK as early as 2011
(Cheshire Constabulary Camera Survey). On average, a citizen
is captured on CCTV an estimated 68 times per day (ibid). To
what extent will citizens and governments be willing to sacrifice
anonymity and privacy to prevent and detect crime?
On average, a citizen is
captured on CCTV an
estimated 68 times per day
Cheshire Constabulary
Camera Survey
60
Chapter 6
Summary
Awareness of AI has reached an inflection point. Given media attention and vendor marketing,
executives' awareness of AI is high.
Understanding of AI among buyers is low. Technology principles, use cases and deployment
methodologies are poorly understood.
20% of AI-aware executives say they have adopted one or more AI-related technology at
scale, or in a core part of their business (McKinsey Global Institute). While nascent, we believe
AI adoption is 'crossing the chasm' from innovators and early adopters to the early majority.
Adoption of AI will increase significantly as buyers seek to unlock value from data and avoid
losing competitive advantage. 75% of executives say AI will be "actively implemented" to
some degree in their organisations within three years (Economist Intelligence Unit).
High tech, automotive and assembly, and financial service firms lead AI adoption. Spending
on AI will increase most in sectors that currently lead adoption.
Poorly articulated business cases weigh on adoption. Better articulation of ROI by AI vendors
can catalyse adoption.
While extensive media attention and numerous pilot projects relate to chatbots, more than
two thirds of buyers are deploying AI to improve decision-making and enable process
automation.
For mid-size and large companies, the C-suite is key for initiating, selecting and funding AI
initiatives. In two thirds of organisations, the CTO or CIO make AI technology decisions given
its cross-functional implications.
AI deployment strategies are varied, with a mix of 'build' and buy' strategies, and in a state
of flux. 'Hybrid' approaches are typical. A quarter of companies deploying AI today prefer
to purchase a standalone solution.
Lack of skills is the primary challenge for companies deploying AI. Defining an AI strategy,
identifying use cases for AI, and securing funding for AI initiatives are additional difficulties.
The adoption
of AI
61
62
Executives
Adoption of AI is nascent but has passed a tipping point. Develop an AI strategy to avoid losing
competitive advantage.
Understanding of AI within your organisation is likely to be low. Develop initiatives to improve senior executives'
understanding of AI by engaging with third-party experts.
Increasingly, peers are investing in AI to gain value from their data. Ensure AI initiatives are a budget priority
to enable test-and-learn deployments.
Engage with AI software companies that articulate tangible use cases and ROI opportunities. Seek vendors
offering solutions to business problems, not slogans.
While chatbots receive extensive attention, recognise that your peers are more likely to be deploying AI
to enhance business decision-making and process automation.
Support the C-suite's efforts to catalyse AI. They are likely to be the initiator of AI initiatives and will play
a significant role in selecting and funding projects.
Proactively address the likely challenges to your organisation's adoption of AI: lack of skills;
the absence of an AI strategy; lack of clarity regarding AI use cases; and prioritisation of funding.
Chapter 6
The adoption of AI
Recommendations
To address buyers' caution regarding AI technology, articulate solutions to business problems and ROI
opportunities, not AI technology as an end in itself.
Recognise that buyers' understanding of AI is low, and they are likely to lack AI skills and personnel within their
organisations. Become a strategic partner for customers by offering education and support.
Offer buyers improved decision-making and process automation to align with their priorities.
Given the importance of the C-suite in initiating and funding AI initiatives at large companies, prioritise securing
senior sponsorship for your initiatives.
A quarter of buyers prefer AI solutions from independent software vendors. Qualify these attractive prospects
early in your engagement process and highlight the benefits you can offer as a best-of-breed vendor.
Proactively address buyers' potential concerns regarding product scalability and performance.
Entrepreneurs
AI adoption is nascent, but crossing a tipping point from early adopters to the early mainstream. Identify
opportunities to invest in AI-first companies that can capitalise on increasing demand for AI.
Understanding of AI among buyers is limited, and C-level sponsorship may be required for deployments in large
companies. Given these go-to-market dynamics, evaluate management teams' ability to articulate to buyers
tangible solutions to business problems, and their C-level account management skills.
Prospects that provide solutions aligned with buyers' priorities improved decision-making and process
automation may be most attractive.
Investors
63
Awareness of AI has reached an
inflection point
Given media attention on AI and vendors' marketing of the
technology, awareness of AI is high among executives at mid-
size companies and large enterprises.
Interest has also passed an inflection point. "In January 2016,
the term 'artificial intelligence' didn't even make it into the top
100 search terms on gartner.com. But just a year later, the term
ranked at No. 11, and in May 2017 the term ranked at No. 7,
indicating the popularity of the topic and interest from Gartner
clients in understanding how AI can and should be used as
part of their digital business strategy" (Gartner (August 2017)
Survey Analysis - Enterprises Dipping Toes Into AI but Are
Hindered by Skills Gap).
Understanding of AI is low
There is a gulf between buyers' awareness of AI and their
understanding of the technology. Companies' understanding
of AI is low. Technology principles, use cases and deployment
methodologies are poorly understood. "Even among CIOs,
understanding of AI is extremely low." (Senior Executive, global
consumer packaged goods company).
AI adoption is 'crossing the chasm'
In a survey of 3,073 AI-aware C-level executes across ten
countries and 14 sectors (fig. 28 overleaf), 20% said they had
adopted one or more AI-related technology at scale, or in a
core part of their business (McKinsey Global Institute).
We believe adoption of AI is spreading from innovators
(the first 2.5% to adopt a technology) and early adopters
(the next 13.5%) to the early majority (the subsequent 34%)
(fig. 29 overleaf).
AI adoption is, nonetheless, nascent. Just 10% of AI-aware
companies have deployed three or more AI technologies
(McKinsey Global Institute). 80% of companies that are
aware of AI are gathering knowledge about the technology,
developing strategy or experimenting with the technology
(ibid). Engagements begin with proof-of-concept projects.
Our conversations with senior executives underscore the early
stage of the paradigm shift to AI, which will unfold within the
enterprise in the coming decade and beyond.
"The buzz over artificial
intelligence has grown loud
enough to penetrate the
C-suites of organisations
around the world."
McKinsey Global Institute
"The current AI wave
is poised to finally
break through."

McKinsey Global Institute
"Understanding of AI? it's
probably a 3 out of 10."
Vice President, global consumer
products company
64
Chapter 6
The adoption of AI
Source: McKinsey Global Institute
Source: Everett Rogers, Geoffrey Moore
Fig. 28. 20% of AI-aware companies
have begun adoption
Fig. 29. AI adoption is 'crossing the chasm'
to the early majority
Adoption will increase significantly
While understanding of AI is limited, there is significant
appetite for AI investment as buyers seek value from data and
wish to avoid losing competitive advantage. 75% of executives
say AI will be "actively implemented" to some degree in their
organisations within three years (Economist Intelligence Unit).
"If we don't embrace AI,
it may be that we lose
competitive advantage."
Manager, utility company
Our discussions with executives highlight their intention to
invest in AI. "We understand that machine learning is needed
within the business. It's an eight or nine out of ten in terms
of deployment priorities" (Chief Finance Officer, UK internet
retailer). "We have a huge amount of data. We play with it. We
don't actually use it. That needs to change" (Chief Information
Officer, global ecommerce company). "I do intend to deploy
advanced, machine learning-led analytics within the next
couple of years" (Chief Digital Officer, UK transport company).
"I get it, and my boss does, so in the near future it will be part of
our toolset" (Product Manager, global equipment supplier).
High-tech and financial service firms lead
adoption and demand
Adoption of AI is greatest in the following sectors (fig. 30
overleaf): high-tech and telecommunications; automotive and
assembly; financial services; energy and resources; media and
entertainment; and transportation and logistics (McKinsey
Global Institute). Companies in these sectors encounter
numerous prediction and optimisation challenges that can be
addressed with AI, have large data sets to train and deploy AI
algorithms, can readily assess the return on investment (ROI)
offered by AI, face unattractive alternatives to digitisation in
the form of expensive personnel, and have the resources and
technological foresight to embrace new technologies.
Spending on AI will increase most in sectors that currently lead
adoption. Companies in the financial services and high-tech
and telecommunications sectors are likely to increase their
AI expenditure most in the next three years (McKinsey
Global Institute).
Adoptors
20%
Experimenters
10%
Contemplators
40%
Partial adoptors
31%
AI adoption is 'crossing the chasm' to the early majority
20% of AI-aware companies have begun adoption
Innovators
Early adopters
CHASMEarly majority
Late majority
Laggards
Population
2.5%
34%
34%
16%
13.5%
65
Source: McKinsey Global Institute AI adoption and use survey; McKinsey Global Institute analysis
(1) Based on the midpoint of the range selected by the survey respondent. (2) Results are weighted by firm size.
Fig. 30. High-tech and financial services firms lead demand for AI
Poorly articulated business cases
weigh on adoption
While positive about the potential for AI, many executives
express nervousness around undertaking AI initiatives given
suppliers' failure to articulate solutions to specific business
problems, difficulty demonstrating ROI, over-promising
by suppliers and the failure of some high-profile projects.
41% of firms say they are uncertain about the benefits of AI
(McKinsey Global Institute).
"Buyers feel there's value,
but are nervous around
making bets."
Vice President, global consumer
products company
Further, buyers are still implementing or consolidating prior
investments in data management, including data lakes
and reporting tools. Many have significant data collection,
consolidation and harmonisation challenges to address before
investing in AI. "We, and our peers, are trying to get our data
infrastructure in place first" (Manager, UK utility company).
A focus on ROI can catalyse spend
"The key is transforming
the messaging to make it
simple to play back to the
organisation: 'this will
achieve your target'".
Director, UK service company
To unlock value in the market, providers must articulate
and deliver a tangible return on investment (ROI). Whether
impacting direct drivers of revenue (uplift, conversion, yield
or price) or reducing a company's excess spend or resource
requirements, a provider's results will be assessed against a
buyer's existing process and key performance indicators.
To their cost, certain providers offer 'AI technology' without
articulating business value. In a market driven by measurable
results, not perceived gains, companies delivering tangible
benefits enjoy a competitive advantage.
Future AI demand trajectory1Average estimated % change in AI spending,next 3 years, weighted by firm size20
1
2
3
4
5
6
7
8
9
10
11
12
13
High tech and financial services firms lead demand for AI
Falling behind
Travel and tourism
Construction
Current AI adoption
Percent of firms adopting one or more AI technology at scale or in a core part of their business, weighted by firm size2
Health care
Professional services
Transportation and logistics
Financial services
High-tech
and telcommunications
Automotive
and assembly
Leading sectors
Education
Media and entertainment
Energy and resources
Retail
Consumer package goods
0
2
4
6
8
10
12
14
16
18
20
22
24
26
28
30
32
66
Base: n = 80, Gartner Research Circle Members; excludes "Haven't decided yet". Question: What type of artificial intelligence initiatives(s) is your organisation
investigating/developing/has your organisation deployed or is planning to deploy?
*Source: Gartner (August 2017) Survey Analysis - Enterprises Dipping Toes Into AI but Are Hindered by Skills Gap (Fig. 2).
Deployments focus on analytics and process
automation
While extensive media attention and numerous pilot projects
relate to chatbots, more companies are deploying AI to
improve their ability to make decisions, or enable process
automation (fig. 31).
While there is a novelty to innovations such as chatbots,
organisations are more pragmatic, with 74% saying they
want to apply AI to improve decision making and offer more
personalised recommendations, especially in relation to
customers (Gartner*). And, nearly two-thirds of surveyed
organisations plan to use AI to automate business processes,
especially in areas that are manually intensive (ibid). All other
AI use cases, combined, account for fewer than half
of deployment goals.
By focusing on decision-making and process automation,
companies will unlock two of the fundamental benefits of AI:
effectiveness and efficiency. Finding subtle correlations in data
enables companies to improve their analyses and actions,
while automating business processes offers reduced costs
and accelerated innovation.
Percentage of Respondents
Figure 2. Business Use Case for Using AI Within Organisation
Solutions for decision-making/recommendations
Process automation
Virtual personal assistant/chatbot that communicates in natural language
Self-learning mechanical robotics
Other
Embedded AI in products that can learn and adapt to owners
0%
20%
40%
60%
80%
64
40
18
18
11
74
Chapter 6
The adoption of AI
Fig. 31. Business use case for using AI within organisation
67
Fig. 32. Top three challenges to adopting AI by organisations
Lack of skills is the primary inhibitor
to AI adoption
Most respondents to a Gartner survey report that lack of skills
is the top challenge to adopting AI. Defining an AI strategy,
identifying use cases for AI (fig. 32), and funding AI initiatives
are the next most common difficulties (Gartner*).
These challenges are symptomatic of the early stage of a new
technology paradigm. As mobile computing began to gain
traction, companies struggled to define their mobile strategy,
clarify use cases for smaller form factors, and hire enough
designers and developers with mobile expertise.
Lack of necessary skills is the greatest inhibitor of companies'
adoption of AI. High quality data scientists, AI engineers and
AI researchers are in short supply. In the UK, the number of
open positions for general data scientists grew 32% year-
on-year during the first half of 2016 (Procorre), for example,
outstripping growth in supply. In the US, demand for data
scientists and data engineers is projected to grow 39%
(Burning Glass Technologies). With 81% of data science and
analytics jobs requiring workers with 3-5 years of experience or
more (ibid), the problem will not be resolved in the near-term
and will serve has a headwind to the pace of AI adoption. Many
new AI experts are being recruited by AI platform companies
(Google, Amazon, IBM, Microsoft), by consumer technology
companies (Apple, Facebook) or are choosing to found new
AI-driven startups.
Companies' secondary challenges defining an AI strategy,
identifying use cases for AI, and securing funding for AI
initiatives will ease within the next three years. There are
numerous use cases for AI that offer tangible benefits.
Understanding of the technology and these use cases
will improve. As pilot projects mature, demonstrable
ROI will unlock funding. While at times buyers will
experience disappointment, secondary challenges
will slowly lessen over time.
Percentage of respondents
54%
37%
35%
35%
30%
27%
23%
18%
13%
11%
5%
0%
10%
20%
30%
40%
50%
60%
Lack of necessary skills
Defining our AI strategy
Identifying use cases for AI
Funding for AI initiatives
Security or privacy concerns
Complexity of integrating AI
with our existing infrastructure
Determining how to measure value from AI
Potential risks or liabilities
Governance issues
Understanding what AI is
Other
Analytics
Strategy
Organisational
Technical
"At the end of the day,
we just don't have the
expertise in-house."
Product Manager,
global equipment supplier
Base: n = 83, Gartner Research Circle Members
Question: What are the top three challenges to the adoption of artificial intelligence within your organisation?
*Source: Gartner (August 2017) Survey Analysis - Enterprises Dipping Toes Into AI but Are Hindered by Skills Gap (Fig. 4).
68
Chapter 6
The adoption of AI
The C-suite is key for initiating, selecting
and funding AI initiatives
For large and mid-size organisations, the C-suite plays a vital
role in initiating AI projects, making technology decisions in
relation to them, and approving project funding (fig. 33). This
reflects the C-suite's recognition of the strategic importance
of AI, and a belief that the CIO is an appropriate lead for AI
initiatives given the cross-functional implications of AI and its
impact to existing systems.
Chief Information Officers (CIOs) and Chief Technology
Officers (CTOs) are primary decision-makers. Together, they
are responsible for initiating AI initiatives in more than half of
organisations, and making technology decisions in nearly
two thirds. The Chief Executive Officer (CEO) also plays a
significant role, initiating AI initiatives in a tenth of organisations
and approving funding in over a fifth. Chief Financial Officers'
(CFOs') engagement with AI initiatives are primarily to approve
funding, in just over a tenth of organisations.
Base: Initiated the effort, n = 82; make technology decisions, n = 80; approves the funding, n = 81; Gartner Research Circle Members; excludes "Not sure".
Question: Who in your organisation primarily initiated the artificial intelligence initiative?
*Source: Gartner (August 2017) Survey Analysis - Enterprises Dipping Toes Into AI but Are Hindered by Skills Gap (Fig. 5).
Fig. 33. AI initiator and decision-making roles within organisation
0%
Initiates the effort
Makes technology decisions
Approves the funding
5%
10%
15%
20% 25% 30% 35%
40%
Chief Information Officer (CIO)
Chief Technology Officer (CTO)
or Technology Director
Business Unit Head or Managing Director
Chief Exectutive Officer (CEO)
Head of R&D / Engineering
Chief Operations Officer (COO)
Chief Data Officer (CDO)
Chief Finance Officer
Percentage of respondents
Chief Information Officers
(CIOs) and Chief Technology
Officers (CTOs) are the
primary decision-makers.
69
Deployment strategies vary a 'hybrid'
approach is common
Among companies implementing or planning to implement
AI, deployment strategies are split fairly evenly, between 'buy'
and 'build', and in a state of flux. Nearly a quarter of companies
have yet to determine a preferred approach (fig. 34).
Nearly a third of mid-size to large organisations report that
their preference is to build custom AI solutions. A desire for
customisation, control, skills development and learning drives
these companies' planning strategies. "We probably need
to get our hands around the technology and see what it can
deliver for us" (Manager, UK infrastructure company).
Our conversations with buyers highlight that few expect to
adopt a 'build' approach in isolation. Given skills shortages,
and the opportunity for faster time-to-value, most large buyers
anticipate a 'hybrid' approach in which they combine in-house
development with solutions from third-party AI software and
service companies. The proportion of small companies that
seek to build custom AI solutions in-house will be low, given
a lack of resources and in-house data science teams.
Over a quarter of organisations deploying AI today are
purchasing standalone AI solutions to achieve their objectives
(Gartner). "Bring in someone who has the expertise,
knowledge and skill-set and can hold a mirror up to the
organisation." (Manager, UK energy company). "We want to
get someone in who specialises in data and can unlock value."
(Product Manager, global transport supplier). There is ample
opportunity to do so. In the UK there are 400 early stage
'best of breed' software companies offering AI-first solutions
to challenges in most business functions and an accessible
'on-ramp' to AI (see Chapter 8). These companies offer AI-
native products, skilled teams of AI specialists, implementation
support for nervous buyers, and iteration at speed and low
cost. Challenges, when engaging with early stage suppliers,
include their limited scale and maturing product scalability
and performance. While small and mid-size buyers are more
inclined to use third-party software for all their data science
initiatives, large companies who buy standalone AI solutions
may combine their use with some in-house development
the 'hybrid' approach.
Just over a fifth of organisations prefer to take advantage of AI
functionality added to their existing applications, over time, by
traditional vendors. Doing so can ease integration requirements
and limit the requirement for further investment. This approach
poses three challenges. First, availability is uneven. While
Salesforce, for example, is acquiring and developing AI
functionality for the sales function, in other functions and
some sectors (manufacturing and utilities) incumbents are less
forthcoming. Second, many incumbents are moving slowly,
offering functionality that is limited compared with new, best-
of-breed vendors. Finally, companies adopting this approach
can lack competitive advantage. By definition, peers will have
access to the same capabilities.
Base: Implemented or planning to implement, n = 80; Preferred option for implementation, n = 81, Gartner Research Circle Members; excludes "Not sure".
Question: For the most part, how has your organisation implemented or is planning to implement this artificial intelligence solution? And what is the preferred
option for the implementation?
*Source: Gartner (August 2017) Survey Analysis - Enterprises Dipping Toes Into AI but Are Hindered by Skills Gap (Fig. 3).
"We learned that a 'half-way
house' suppliers, plus in-
house work is best."
Chief Digital Officer,
UK transport company
Fig.34. Preferred AI implementation approach by end-user organisations
Implemented or planning to implement
Preferred option for implementation
33%
30%
Purchase
stand-alone
AI solution
Build
Custom
AI solution
30%
23%
Haven't
decided yet
20%
26%
Use
embedded
AI from
existing app
18%
21%
70
The growth
of AI services
Chapter 7
Summary
For every 1 spent on enterprise software, 3 is spent on IT services consulting, system
integration and outsourcing.

IT service companies involved in AI 'AI service' companies assist buyers with AI initiatives
ranging from reviews of AI strategy to chatbot implementations.
A focal point for AI service activity is supporting buyers' rollout of analytics software that
incorporates AI.
As mid-size companies and enterprises experiment with AI, most plan to involve a third
party AI service provider, fuelling growth in the AI services market.
While early and modestly-sized today, the AI services market is poised for rapid growth.
As buyers use AI to gain value from historic investments in data collection, we expect AI
services to offer a multi-billion-dollar market opportunity by 2020.

'Convergence' and consolidation are reshaping the market. Software companies are
developing service capabilities to support solution-selling, while service companies are
developing and acquiring software assets to access client opportunities and reduce cost
to serve.
The delivery model for AI services is changing. Led by mid-market buyers, we expect a mix
shift from traditional projects of fixed scope, to managed services delivered via the cloud,
paid for on an ongoing basis.
Competition for AI services work above the mid-market will be fierce. For large deals,
global service firms will leverage their data and data science personnel. Mid-size deals will
represent a second battleground, with mid-tier vendors competing with each other and
vendors from above and below. For smaller deals, select boutiques offer buyers the right
success factors accessibility, flexibility and low cost to achieve scale and mature into
mid-size vendors.
Specialisation is becoming a key success factor for competitive differentiation and
defensibility. Increasingly, individual AI service providers are focusing their competencies
on specific verticals, business functions or business sub-functions.
71
72
Recommendations
Executives
AI service providers deliver strategies, technologies and implementations for a range of AI initiatives, from
chatbots to analytics. Evaluate opportunities to catalyse time to value in AI by engaging with AI service providers.
Effective service providers will focus on solving business problems, not AI technology for its own sake. Engage
with companies that describe clearly how they can improve your key performance indicators, using technology
as an enabler.
Managed service deployments are coming of age. For AI-powered analytics, evaluate whether a third-party
solution delivered via the cloud could provide an evolving capability, at lower cost and with better support, than
a traditional time-and-materials engagement.
Competition for large contracts is fierce. Negotiate robustly with multiple suppliers to maximise value.
Make use of vendor specialisation. Identify vendors offering expertise and trained machine learning algorithms
in your chosen business function and sector.
Chapter 7
The growth of AI services
Consider offering a managed service capability to take advantage of evolving buyer behaviour.
Explore under-served AI service market segments. Competition for generic, large deals is intense.
Evaluate a specialisation strategy to develop data network effects and competitive differentiation in a
competitive market.
Proactively explore M&A to avoid being left sub-scale in a consolidating market.
Entrepreneurs
Evaluate opportunities for investment in AI services, given potential for strong growth in the market.
Be cognisant of competitive dynamics and the risk of commoditisation in the market.
Evaluate whether encouraging portfolio companies to specialise in certain sectors or business functions could
support their defensibility.
Given extensive market consolidation, create and identify opportunities to achieve scale through mergers
and realise value through trade sales.
Investors
73
Analytics is a focal point for AI services
For every 1 spent on enterprise software, 3 is spent on IT
services consulting, system integration and outsourcing.
IT service companies involved in AI ('AI service' companies)
assist mid-size buyers and enterprises with AI initiatives, ranging
from reviews of AI strategy and chatbot implementations to
deployments of analytics software enhanced with AI.
Most companies deploying AI today are focused on improving
decision-making (see Chapter 6). A focal point for AI service
companies' activity, therefore, is supporting the rollout of
analytics software that incorporates AI 'AI Analytics'. AI
Analytics enables buyers to derive insight from enterprise data.
AI Analytics frequently draws on:
enterprise resource data, such as inventory and order
management information, to derive business intelligence;
data from business functions to improve performance
for example, analysis of marketing information to improve
customer segmentation and churn prediction.
AI buyers seek to involve AI service
providers
As mid-size companies and enterprises experiment with AI,
most plan to include at least an element of outsourcing to AI
service providers to achieve their goals, fuelling growth in the
AI services market.
Companies lack the AI skills to 'go it alone', seek experts to
deliver early wins during test-and-learn cycles, and cannot
re-deploy existing staff without slowing other initiatives.
In the next three years, most companies will involve third
parties in their data science initiatives. Small and mid-size
companies are inclined to outsource entire AI initiatives,
given resource constraints and risks associated with hiring
in-house data science teams. Large enterprises typically adopt
a 'hybrid' approach, engaging with third-party providers while
developing in-house data science capabilities.
'Convergence' and consolidation are
reshaping the market
A powerful trend of 'convergence' is reshaping the market for
AI services. Software providers are strengthening their service
capabilities to enable broader, more successful deployments.
Conversely, service companies are developing and acquiring
technology assets, from tools to broad applications, to access
client opportunities and reduce cost-to-serve.
"We're 80% revenue from
services, 20% from software
licenses. But that won't give
you the full picture since the
products are super-critical."
Senior Executive, AI services company
Service companies are acquiring technology assets as well
as developing them. In 2015, consulting company McKinsey
acquired advanced analytics companies 4Tree (price and
promotion optimisation for consumer goods), VisualDoD
(analytics for the defence industry) and QuantumBlack
(analytics for organisational performance).
Further, we expect extensive consolidation in the AI services
market. The market is fragmented. Participants range from
global consulting companies and system integrators to 'best-of-
breed' AI service boutiques and mid-size providers. Boutiques
and mid-size specialists with high quality AI personnel and
expertise in specific business functions or sectors will be
attractive targets to global vendors. For smaller vendors this
is a double-edged sword. Some will achieve attractive exits,
while others will be left sub-scale in a consolidating market.
74
Chapter 7
The growth of AI services
We expect a shift towards managed services
The delivery model for AI services is changing. Most large
AI service companies offer clients the option of either:
a managed service a cloud service whereby fees are paid
monthly for ongoing access to a remotely hosted capability;
a time and materials deployment a project of defined
specification, cost and length after which engagement ends.
"I would prefer it on
a subscription basis,
certainly initially. In a
subscription model,
the tech always evolves."
Product manager, global
equipment supplier
To date, enterprise customers have preferred time and
materials engagements. We estimate that less than 25%
of AI service companies' revenue is derived from managed
service deployments.
However, demand for managed services is increasing. We
expect the proportion of managed service deployments
to double in the medium term, comprising up to 50% of
engagements. Mid-size companies, that are leading adoption
of cloud services more broadly, are driving this trend. Smaller
buyers taking their first steps with AI technology value the
lower up-front cost, increased flexibility, ongoing support,
and regular technology updates that managed service
deployments provide.
Competition above the mid-market
will be fierce
Despite the nascent stage of the AI services market,
competition for large and mid-size contracts will be fierce.
Global system integrators, consulting companies and
professional service firms that compete for today's largest
general analytics contracts (10m to 100m, or more, per
year) are repositioning for strength in AI. Companies including
Accenture, Atos, CapGemini, Cognizant, Deloitte, EY, IBM,
Infosys, KPMG, McKinsey, Palantir, PwC, TCS and Wipro have
developed multi-billion-dollar traditional analytics practices,
typically with 5,000 to 15,000 in-house analytics professionals.
However, on average less than 8% of the companies' analytics
personnel are data scientists (MMC Ventures). Firms are
investing heavily to increase the size of their data science teams
but progress is uneven. We estimate an average of 1,200 data
scientists per firm, but some have as few as 100. Speed will be
important. Global vendors can collect larger customer data
sets than smaller competitors. Data offers a network effect
more data enables better algorithms, whose improved results
attract more customers and data.
Some large vendors focus on board-level engagements and
multi-year global transformation projects worth hundreds
of millions of pounds. An increasing number, however, are
engaging with buyers' IT groups and targeting smaller
analytics projects worth 300,000 or more per year.
These global vendors pose a growing competitive threat
to mid-size providers.
While embracing the opportunities of AI, incumbent global
service providers must consider a challenge. Automation will
challenge business models that rely on charging clients for
large numbers of personnel with limited experience. AI should
enable actionable advice with fewer people, at lower cost.
Mid-size AI service providers including Mu Sigma, Fractal
Analytics, BlueYonder, Cartesian and Opera Solutions have
significant revenue, sizeable workforces and a presence in
multiple territories and sectors. Early to market, with strong
AI expertise and specialisation around specific sectors or
business functions, mid-size providers compete effectively
for AI service deals worth 150,000 to 1m, or more, per year.
While positioned to benefit from the increasing adoption of
AI services, they will face pressure from select global vendors
moving down-market, and boutique vendors pressuring
from below.
75
Given the early stage of AI adoption among buyers, there is
an attractive opportunity for boutique AI service providers to
capture smaller initial contracts, of under 150,000 per year,
with mid-size buyers or larger companies taking their first steps
in AI. There are numerous boutique vendors globally. While
some are lifestyle businesses modest in scale, others have the
ambition and capability to grow into mid-tier winners. Peak, a
UK-based AI services provider, combines an AI technology
platform with high quality data scientists to address this market
opportunity. Our discussions with mid-size AI service buyers
highlight their openness to working with smaller providers
and often a preference to do so. Boutiques offer a responsive
relationship, specialisation in a business function, potentially
stronger AI expertise and lower cost than large vendors.
While the boutiques' market for contracts under 150,000
per year is less contested, boutiques contend with limited
marketing budgets that inhibit their presence. They may be left
behind as competitors consolidate, and will face a challenging
competitive environment if they move up-market. Further, as
software companies from Google to Salesforce increase the
AI in their software platforms and applications, the 'on-ramp' to
AI will become ever-gentler potentially lessening the need for
small companies to engage third-party help.
Specialisation is becoming a key
success factor
AI service providers are specialising focusing their
competencies on specific verticals (such as retail),
business functions (marketing) or business sub-functions
(customer segmentation).
"You become big
by specialising."
Senior Executive,
AI services company
Large buyers value a supplier that offers specialisation.
A specialised supplier offers: expertise in the customer's
vertical or business challenge; large cross-customer data
sets to optimise AI algorithms; and peer referenceability in
the customer's vertical. In the short term, specialisation is
becoming a success factor for companies to win market share
among large enterprise customers. In the long term, buyers will
demand greater specialisation as their in-house data science
capabilities mature.
Smaller enterprises and mid-market customers that are
experimenting with AI, seeking an 'on-ramp' to the technology,
or have limited budgets typically prioritise other selection
criteria. Smaller buyers value vendors that: rapidly address
a tangible business problem to deliver ROI; provide a high
touch service to guide buyers through 'test and learn' phases;
offer geographic proximity; provide flexible pricing for initial
deployments; and can integrate easily with existing IT systems
to extract and process data.
While embracing the opportunities of AI, incumbent
global service providers must consider a challenge.
Automation will challenge business models that rely
on charging clients for large numbers of personnel
with limited experience. AI should enable actionable
advice with fewer people, at lower cost.
76
77
The dynamics
of UK AI
Chapter 8
Summary
There are nearly 400 independent, early stage software companies in the UK with AI at the
heart of their value proposition.
Over 80% of UK AI startups are vertically-focused business-to-business (B2B) suppliers.
Few companies sell direct-to-consumer given the difficulty of acquiring training data from
a 'cold start' and the deployment of AI by global consumer technology companies.
Entrepreneurial activity in AI is unevenly spread. More UK AI companies (one in seven) address
the marketing & advertising function than any other. For companies with a sector focus,
finance dominates. In select sectors (manufacturing) and business functions (finance), activity
appears modest relative to market opportunities.
Few (one in ten) UK AI startups focus on developing core AI technologies applicable to
a wide variety of markets. Among these companies, most focus on research into
autonomous systems.
AI entrepreneurship is thriving. The number of AI companies founded annually in the UK has
doubled since 2014. A new UK AI company has been founded every five days, on average,
since 2014.
UK AI companies comprise nearly half the European total. AI is well represented in the UK,
with a slightly higher proportion of startups focused on AI than in Europe (excluding the UK)
or the US.
UK AI companies are nascent. Two thirds of companies are in the earliest stages of their
journey, with Seed or Angel funding. The sector, however, is maturing rapidly. UK companies
are less embryonic than their European counterparts, offering competitive advantage
in procurements.
Over 40% of companies we meet have yet to receive recurring revenue. The journey to
monetisation for AI companies can be longer given technical challenges, long sales cycles
in a B2B-driven market, and client integration requirements.
Globally, investments into early stage AI firms are typically 20%-50% larger than capital
infusions into general software companies of comparable stages.
Staging of capital into UK AI companies can be atypical. One in three growth stage
companies raised a significantly larger post-Angel rounds than is typical.
We feature eleven leading B2B and B2C AI companies across a range of sectors to illustrate
how early stage AI companies are using AI to address opportunities.
78
Recommendations
Chapter 8
The dynamics of UK AI
Identify potential competitors and partners using our market map.
AI entrepreneurship has accelerated, increasing the number of market entrants and competition. Prioritise
customer acquisition in an increasingly crowded market.
Most UK AI companies are nascent. If you are a later stage company, leverage product maturity, customer
referenceability and capital to secure competitive advantage. If you are an early stage company, prioritise
adaptability and speed of execution.
Implement technologies that can reduce the cost and time required to ingest data, process data and deploy
your product at client sites, to overcome challenging go-to-market dynamics that are common for early stage
AI companies.
The journey to monetisation for AI companies can be longer, given technical challenges and B2B market
dynamics. Adequately capitalise your business to withstand this, and to maximise your pace of customer
acquisition which will enable you to lock in data network effects.
Recognise that capital raises for early stage AI companies are typically larger than for non-AI software companies.
Capitalise your business adequately to create and maintain competitive advantage.
Entrepreneurs
With some segments over-supplied by startups and others under-served, identify attractive pockets of
opportunity aligned with themes on which you focus.
Use the market map and related data to evaluate the context of prospects (including competitors and volume
of new entrants) and to anticipate market dynamics.
With investments into AI companies larger than average, valuations can be elevated. Consider whether or not
you are willing to 'overpay' to access opportunities.
A significant proportion of AI companies have yet to achieve recurring revenue. Further, a sizeable minority of
Angel stage companies are raising larger second rounds than is typical. Evaluate whether you are willing to invest
in pre- or low revenue companies to secure access.
Investors
Executives
Explore the rich ecosystem of early stage AI companies in the UK. Most will be B2B vendors and some will offer
market-leading solutions to challenges in your organisation.
Identify potential suppliers and partners in your sector and in key business functions.
Anticipate that many AI companies will be nascent, which may limit their ability to provide customer references
and extensive resources.
79
There are 400 early stage AI software
companies in the UK
With every paradigm shift in technology, innovative early stage
companies emerge to improve and then reimagine business
processes and consumer applications. There are nearly 400
early stage, privately held AI software companies in the UK.
Over time, the distinction between 'AI companies' and other
software providers will blur and then disappear as AI is applied
to most business processes and sectors. Today, however,
it is possible to highlight a sub-set of early stage software
companies that have AI at the heart of their value proposition.
The market map, overleaf, places the 400 companies
according to:
Purpose: Does the company focus on a business function
(for example, marketing or human resources), a sector
(healthcare, education) or core AI technology with cross-
domain application?
Customer: Does the company predominantly sell to other
businesses (B2B) or to consumers (B2C)?
Funding: How much funding has the company disclosed
to date? We categorise companies as: Angel or Seed stage
(under $500,000 to $2m); or Early or Growth stage (over
$2m to c. $200m).
With every paradigm shift in
technology, innovative early
stage companies emerge to
improve and then reimagine
business processes and
consumer applications.
80
IT
ANALYTICS/OPTIMISATION
TARGETING
UK AI Landscape (Early stage companies)
Sources: MMC Ventures, Beauhurst, Crunchbase, Tracxn. Additions or corrections? Email us at insights@mmcventures.com
Gousto
VITL
ANGEL / SEED
EARLY STAGE / GROWTH
Nuritas
Captain
VideraBio
Dogtooth Technologies
ANGEL / SEED
EARLY STAGE / GROWTH
Hadean
Seldon
Grakn Labs
ANGEL / SEED
EARLY STAGE / GROWTH
Brytlyt
Memgraph
Graphcore
Academy of Robotics
Evolve Dynamics
ANGEL / SEED
FiveAI
EARLY STAGE / GROWTH
Oxbotica
Sky-Futures
Drone Space AI
Intelligent Robots
Accelerated Dynamics
Baro Vehicles
Machines With Vision
Predina
React AI
Robik.ai
ANGEL / SEED
Neurence
EARLY STAGE / GROWTH
Spectral Edge
Tractable
Visio Ingenii
Blue Vision Labs
Emteq
Xihelm
ANGEL / SEED
Audio analytic
EARLY STAGE / GROWTH
Cambridge Quantum Computing
Prowler.io
Weave.ai
VividQ
Global Surface Intelligence
Hummingbird Technologies
KisanHub
Bird.i
Black Swan Data
Massive Analytic
Peak
Rezatec
Ripjar
Satalia
Semantic Evolution
Signal Media
10x
ANGEL / SEED
EARLY STAGE / GROWTH
Brandwatch
Airfinity
Amplyfi
Algo Digital Solutions
Chorus Intelligence
Data quarks
Flumes
Gyana
Hertzian
illumr
Import.Io
Logical Glue
Observe
Optimal Labs
Synap
BridgeU
Kwiziq
ANGEL / SEED
EARLY STAGE / GROWTH
Century
Oxademy
Aire
ANGEL / SEED
EARLY STAGE / GROWTH
ForwardLane
ProvidensAI
Arkera
BMLL Technologies
Cleo AI
Cytora
FriendlyScore
Hello Soda
Knowsis
Metafused
Oseven Telematics
Plum
Multiply
Crowd Connected
Disperse.io
Limejump
Alchera Technologies
Archangel Imaging
Calipsa
Biscuit
ANGEL / SEED
EARLY STAGE / GROWTH
Green Running
TravelAI
Grid Edge
OpenCapacity
NumberEight
Luminance
Cognitiv+
Eigen Technologies
ThoughtRiver
ANGEL / SEED
EARLY STAGE / GROWTH
CloudNC
Flexciton
Materialize.X
Senseye
Thingtrax
ANGEL / SEED
EARLY STAGE / GROWTH
Sime Diagnostics
Babylon Health
BrainWaveBank
Cambridge Medical Robotics
Clinithink
ANGEL / SEED
EARLY STAGE / GROWTH
LabGenius
Desktop Genetics
Drayson Technologies
Deontics
Healx
Ieso Digital Health
Optellum
Exscientia
InnersightLabs
InsideDNA
Kaido
Kraydel
Aequa Science
Kheiron
Snap40
BenevolentAI
Synthace
Your.MD
PetaGene
Resurgo Genetics
Visulytix
ThinkSono
Transformative
Viz
DeepAR
eBlur
Artomatix
Jukedeck
AI Music
Aiva Technologies
Antix
ANGEL / SEED
EARLY STAGE / GROWTH
ANDi
Thingthing
Sure
Spirit AI
Skim.it
Kompas
Pimloc
Popsa
Grumgo
Cortexica
DigitalBridge
Cocoon
Emotech Ltd
Hero
Metail
Thread
ANGEL / SEED
EARLY STAGE / GROWTH
WaymarkTech
Callsign
Checkrecipient
Exonar
Anon AI
CoVi Analytics
Eyn
AimBrain
iProov
Onfido
VChain Technology
ANGEL / SEED
EARLY STAGE / GROWTH
MeVitae
Behavox
PredictiveHire
Cyra
Grad DNA
Headstart App
Beamery
Qlearsite
Rotageek
Saberr
ThisWay Global
Human
Potentially
StatusToday
Unmind
ANGEL / SEED
EARLY STAGE / GROWTH
Occupassion
Sentiment
DigitalGenius
Gluru
ANGEL / SEED
EARLY STAGE / GROWTH
True AI
Velmai
PriceHubble
Proportunity
AlgoDynamix
Almax Analytics
Acuity Trading
Abaka
EnAlgo
DealX
Brolly
Chip
Financial Network Analytics
ForecastThis
Alpha I
Yedup
Zenith One
Zoral
RightIndem
Silicon Investing
Spixii
Torafugu Tech
TradeRiser
TradeTeq
TypeScore
Wluper
Vortexa
Reportbrain
SeeQuestor
Shouter
Simudyne
Singular Intelligence
Terrabotics
Kite Edge
Krzana
Policy Radar
LiveMetrics.io
Oxford Semantic Technologies
Aurora AI
4th Office
Focal Point Positioning
Speechmatics
Sumerian
Trint
Rainbird Technologies
Recordsure
Redsi
ANGEL / SEED
EARLY STAGE / GROWTH
Aria Networks
Ampliphae
Automated Intelligence
Celaton
Diffblue
MicroBlink
Fantoo
Fedr8
Firedrop
Fraim
Intelligent Voice
jClarity
Linguamatics
Mentat Innovations
Action.AI
Darktrace
RepKnight
Alchemy Data
Barac
Cyberlytic
Encode
Corax
CyberSparta
ANGEL / SEED
EARLY STAGE / GROWTH
Trudera
Honeycomb Technologies
Cybertonica
SixthCents
Fraugster
Ravelin
Salviol
Featurespace
ANGEL / SEED
EARLY STAGE / GROWTH
Fractal Labs
Rimilia
ANGEL / SEED
EARLY STAGE / GROWTH
Context Scout
Automorph
BigHand
Autto
Capito Systems
Cardinality
Cyanapse
Digital Taxonomy
Mettrr
Retechnica
Rossum
Skipjaq
TextRazor
Thingful
Unity {Cloud}Ware
Previse
keelvar
Quotable
VendorMach
Matchdeck
ANGEL / SEED
EARLY STAGE / GROWTH
Patsnap
Sparrho
FeedStock
Iris.AI
Klydo
GlassAI
Wizdom
ANGEL / SEED
EARLY STAGE / GROWTH
DueDil
Cognism
Draer
Conversity
GrowthIntel
Kluster Intelligence
SalesSi
Synoptic Technologies
ANGEL / SEED
EARLY STAGE / GROWTH
Netz
AUGMENTED CONTENT
Blippar
BoomApp
ANGEL / SEED
EARLY STAGE / GROWTH
Gamar
Vyking
Selerio
Echobox
Adoreboard
Colourtext
ANGEL / SEED
EARLY STAGE / GROWTH
TheySay
SentiSum
DigitalMR
Adbrain
Big Data for Humans
ANGEL / SEED
EARLY STAGE / GROWTH
Platform360
Idio
iotec
Pixoneye
Codec
Otus Labs
DataSine
Deep Index
Permutive
Personalyze
Popcorn Metrics
Visii
ANGEL / SEED
EARLY STAGE / GROWTH
PURCHASE DISCOVERY/
RECOMMENDATION
Decibel Insight
Admedo
Perfect Channel
Realeyes
Storystream
Twizoo
EARLY STAGE / GROWTH
Fresh Relevance
Insider
Intent HQ
Jampp
LoopMe
Advizzo
ANGEL / SEED
Analytics Intelligence
Aiden
Bibblio
Carsi
Chattermill
Concured
Creative AI
Crowdemotion
Crystal Apps
CustomSell
FindTheRipple
Incisively
Media IQ
Mercanto
Metageni
Mobile Acuity
TUMRA
Swogo
Viewsy
rais
re:infer
Sweet Pricing
Nudgr
Phrasee
KEY
B2B
Angel/Seed: <$500k$2m
Early stage/Growth: >$2mc.$200m
Funding category
New company (since 2016)
B2C
BioBeats
Bloomsbury AI
Mind Foundry
Mindi
Bloom.ai
Factmata
See Fashion
Hoxton Analytics
JCC Bowers
Pasabi
Proximus
ThirdEye
Third Space Auto
Boldmind
Orpiva
Presence Orb
Vaix
Enterprise Bot
Hutoma
Humley
Sentient Machines
rasa
Procensus
Digital Contact
LAW
FINANCE
AUTONOMOUS SYSTEMS
Core Technologies
OTHER
FRAUD DETECTION
FINANCE
CYBERSECURITY
CUSTOMER SERVICE
BI & ANALYTICS
COMPLIANCE
Sectors
AGRICULTURE
FOOD & BEVERAGE
RETAIL
COMPUTER VISION/PERCEPTION
AI INFRASTRUCTURE
EDUCATION
MANUFACTURING
INFRASTRUCTURE
HEALTHCARE
MEDIA & ENTERTAINMENT
HUMAN RESOURCES
R&D
SALES
SENTIMENT ANALYSIS
PROCUREMENT
Functions
Function Marketing & Advertising
UK AI Landscape (Early stage companies)
Sources: MMC Ventures, Beauhurst, Crunchbase, Tracxn.
Additions or corrections? Email us at insights@mmcventures.com
81
Chapter 8
The dynamics of UK AI startups
IT
ANALYTICS/OPTIMISATION
TARGETING
UK AI Landscape (Early stage companies)
Sources: MMC Ventures, Beauhurst, Crunchbase, Tracxn. Additions or corrections? Email us at insights@mmcventures.com
Gousto
VITL
ANGEL / SEED
EARLY STAGE / GROWTH
Nuritas
Captain
VideraBio
Dogtooth Technologies
ANGEL / SEED
EARLY STAGE / GROWTH
Hadean
Seldon
Grakn Labs
ANGEL / SEED
EARLY STAGE / GROWTH
Brytlyt
Memgraph
Graphcore
Academy of Robotics
Evolve Dynamics
ANGEL / SEED
FiveAI
EARLY STAGE / GROWTH
Oxbotica
Sky-Futures
Drone Space AI
Intelligent Robots
Accelerated Dynamics
Baro Vehicles
Machines With Vision
Predina
React AI
Robik.ai
ANGEL / SEED
Neurence
EARLY STAGE / GROWTH
Spectral Edge
Tractable
Visio Ingenii
Blue Vision Labs
Emteq
Xihelm
ANGEL / SEED
Audio analytic
EARLY STAGE / GROWTH
Cambridge Quantum Computing
Prowler.io
Weave.ai
VividQ
Global Surface Intelligence
Hummingbird Technologies
KisanHub
Bird.i
Black Swan Data
Massive Analytic
Peak
Rezatec
Ripjar
Satalia
Semantic Evolution
Signal Media
10x
ANGEL / SEED
EARLY STAGE / GROWTH
Brandwatch
Airfinity
Amplyfi
Algo Digital Solutions
Chorus Intelligence
Data quarks
Flumes
Gyana
Hertzian
illumr
Import.Io
Logical Glue
Observe
Optimal Labs
Synap
BridgeU
Kwiziq
ANGEL / SEED
EARLY STAGE / GROWTH
Century
Oxademy
Aire
ANGEL / SEED
EARLY STAGE / GROWTH
ForwardLane
ProvidensAI
Arkera
BMLL Technologies
Cleo AI
Cytora
FriendlyScore
Hello Soda
Knowsis
Metafused
Oseven Telematics
Plum
Multiply
Crowd Connected
Disperse.io
Limejump
Alchera Technologies
Archangel Imaging
Calipsa
Biscuit
ANGEL / SEED
EARLY STAGE / GROWTH
Green Running
TravelAI
Grid Edge
OpenCapacity
NumberEight
Luminance
Cognitiv+
Eigen Technologies
ThoughtRiver
ANGEL / SEED
EARLY STAGE / GROWTH
CloudNC
Flexciton
Materialize.X
Senseye
Thingtrax
ANGEL / SEED
EARLY STAGE / GROWTH
Sime Diagnostics
Babylon Health
BrainWaveBank
Cambridge Medical Robotics
Clinithink
ANGEL / SEED
EARLY STAGE / GROWTH
LabGenius
Desktop Genetics
Drayson Technologies
Deontics
Healx
Ieso Digital Health
Optellum
Exscientia
InnersightLabs
InsideDNA
Kaido
Kraydel
Aequa Science
Kheiron
Snap40
BenevolentAI
Synthace
Your.MD
PetaGene
Resurgo Genetics
Visulytix
ThinkSono
Transformative
Viz
DeepAR
eBlur
Artomatix
Jukedeck
AI Music
Aiva Technologies
Antix
ANGEL / SEED
EARLY STAGE / GROWTH
ANDi
Thingthing
Sure
Spirit AI
Skim.it
Kompas
Pimloc
Popsa
Grumgo
Cortexica
DigitalBridge
Cocoon
Emotech Ltd
Hero
Metail
Thread
ANGEL / SEED
EARLY STAGE / GROWTH
WaymarkTech
Callsign
Checkrecipient
Exonar
Anon AI
CoVi Analytics
Eyn
AimBrain
iProov
Onfido
VChain Technology
ANGEL / SEED
EARLY STAGE / GROWTH
MeVitae
Behavox
PredictiveHire
Cyra
Grad DNA
Headstart App
Beamery
Qlearsite
Rotageek
Saberr
ThisWay Global
Human
Potentially
StatusToday
Unmind
ANGEL / SEED
EARLY STAGE / GROWTH
Occupassion
Sentiment
DigitalGenius
Gluru
ANGEL / SEED
EARLY STAGE / GROWTH
True AI
Velmai
PriceHubble
Proportunity
AlgoDynamix
Almax Analytics
Acuity Trading
Abaka
EnAlgo
DealX
Brolly
Chip
Financial Network Analytics
ForecastThis
Alpha I
Yedup
Zenith One
Zoral
RightIndem
Silicon Investing
Spixii
Torafugu Tech
TradeRiser
TradeTeq
TypeScore
Wluper
Vortexa
Reportbrain
SeeQuestor
Shouter
Simudyne
Singular Intelligence
Terrabotics
Kite Edge
Krzana
Policy Radar
LiveMetrics.io
Oxford Semantic Technologies
Aurora AI
4th Office
Focal Point Positioning
Speechmatics
Sumerian
Trint
Rainbird Technologies
Recordsure
Redsi
ANGEL / SEED
EARLY STAGE / GROWTH
Aria Networks
Ampliphae
Automated Intelligence
Celaton
Diffblue
MicroBlink
Fantoo
Fedr8
Firedrop
Fraim
Intelligent Voice
jClarity
Linguamatics
Mentat Innovations
Action.AI
Darktrace
RepKnight
Alchemy Data
Barac
Cyberlytic
Encode
Corax
CyberSparta
ANGEL / SEED
EARLY STAGE / GROWTH
Trudera
Honeycomb Technologies
Cybertonica
SixthCents
Fraugster
Ravelin
Salviol
Featurespace
ANGEL / SEED
EARLY STAGE / GROWTH
Fractal Labs
Rimilia
ANGEL / SEED
EARLY STAGE / GROWTH
Context Scout
Automorph
BigHand
Autto
Capito Systems
Cardinality
Cyanapse
Digital Taxonomy
Mettrr
Retechnica
Rossum
Skipjaq
TextRazor
Thingful
Unity {Cloud}Ware
Previse
keelvar
Quotable
VendorMach
Matchdeck
ANGEL / SEED
EARLY STAGE / GROWTH
Patsnap
Sparrho
FeedStock
Iris.AI
Klydo
GlassAI
Wizdom
ANGEL / SEED
EARLY STAGE / GROWTH
DueDil
Cognism
Draer
Conversity
GrowthIntel
Kluster Intelligence
SalesSi
Synoptic Technologies
ANGEL / SEED
EARLY STAGE / GROWTH
Netz
AUGMENTED CONTENT
Blippar
BoomApp
ANGEL / SEED
EARLY STAGE / GROWTH
Gamar
Vyking
Selerio
Echobox
Adoreboard
Colourtext
ANGEL / SEED
EARLY STAGE / GROWTH
TheySay
SentiSum
DigitalMR
Adbrain
Big Data for Humans
ANGEL / SEED
EARLY STAGE / GROWTH
Platform360
Idio
iotec
Pixoneye
Codec
Otus Labs
DataSine
Deep Index
Permutive
Personalyze
Popcorn Metrics
Visii
ANGEL / SEED
EARLY STAGE / GROWTH
PURCHASE DISCOVERY/
RECOMMENDATION
Decibel Insight
Admedo
Perfect Channel
Realeyes
Storystream
Twizoo
EARLY STAGE / GROWTH
Fresh Relevance
Insider
Intent HQ
Jampp
LoopMe
Advizzo
ANGEL / SEED
Analytics Intelligence
Aiden
Bibblio
Carsi
Chattermill
Concured
Creative AI
Crowdemotion
Crystal Apps
CustomSell
FindTheRipple
Incisively
Media IQ
Mercanto
Metageni
Mobile Acuity
TUMRA
Swogo
Viewsy
rais
re:infer
Sweet Pricing
Nudgr
Phrasee
KEY
B2B
Angel/Seed: <$500k$2m
Early stage/Growth: >$2mc.$200m
Funding category
New company (since 2016)
B2C
BioBeats
Bloomsbury AI
Mind Foundry
Mindi
Bloom.ai
Factmata
See Fashion
Hoxton Analytics
JCC Bowers
Pasabi
Proximus
ThirdEye
Third Space Auto
Boldmind
Orpiva
Presence Orb
Vaix
Enterprise Bot
Hutoma
Humley
Sentient Machines
rasa
Procensus
Digital Contact
LAW
FINANCE
AUTONOMOUS SYSTEMS
Core Technologies
OTHER
FRAUD DETECTION
FINANCE
CYBERSECURITY
CUSTOMER SERVICE
BI & ANALYTICS
COMPLIANCE
Sectors
AGRICULTURE
FOOD & BEVERAGE
RETAIL
COMPUTER VISION/PERCEPTION
AI INFRASTRUCTURE
EDUCATION
MANUFACTURING
INFRASTRUCTURE
HEALTHCARE
MEDIA & ENTERTAINMENT
HUMAN RESOURCES
R&D
SALES
SENTIMENT ANALYSIS
PROCUREMENT
Functions
Function Marketing & Advertising
82
Chapter 8
The dynamics of UK AI
Most AI software startups are vertically-
focused B2B vendors
Nine in ten early stage AI software companies in the UK are
solving a problem in a specific business function or sector
(fig.35). Just one in ten is developing a core AI technology
a capability, platform or set of algorithms applicable across
multiple domains.
Further, nine in ten UK AI companies are predominantly B2B
(fig.36), developing and selling solutions to other businesses.
Just one in ten sells directly to consumers (B2C).
A 'cold start' challenge around data limits the number of new
B2C AI companies. Training AI algorithms typically requires
large volumes of data. While B2B companies can analyse the
extensive data sets of the businesses they serve, in the absence
of public or permissioned data (such as Facebook profile
data), customer-facing companies usually begin without large
volumes of consumer data to analyse. Typically, they deploy
AI over time as their user bases and data sets grow.
Given the 'cold start' challenge, most consumers first
experience AI via the world's most popular consumer
applications Facebook, Google, Amazon, Netflix, Pinterest
and others which leverage vast data sets and AI teams
to deliver facial recognition, search and entertainment
recommendations, translation capabilities and more using AI.
Source: Beauhurst, Crunchbase, Traxcn, MMC Ventures
Source: Beauhurst, Crunchbase, Traxcn, MMC Ventures
Fig. 35. Nine in ten AI companies are
application providers
Fig. 36. Nine in ten AI companies are B2B
Vertical
91%
Horizontal
9%
B2B
90%
B2C
10%
Nine in ten early stage UK
AI companies are applying
AI to solve a problem in
a specific business
function or sector.
83
AI entrepreneurship is unevenly spread
More UK AI companies are addressing the marketing &
advertising function (one in seven companies) than any
others (fig.37). Of companies with a sector focus, finance
(more than one in ten UK AI companies) and healthcare
companies predominate (fig.38).
Activity is extensive within the general IT and BI &
analytics buiness functions, and the retail and media
& entertainment sectors.
Source: Beauhurst, Crunchbase, Traxcn, MMC Ventures
Fig. 37. UK AI companies (Business functions)
Fig. 38. UK AI companies (Sectors)
UK AI cmpanies (Business Functions)
UK AI companies (Sectors)
61
Marketing & Advertising
41
Finance
32
Healthcare
17
Retail
6
Agriculture
17
Media & Entertainment
12
Infrastructure
5
Education
5
Food & Beverage
5
Manufacturing
4
Law
38
IT
34
BI & Analytics
16
Human Resources
11
Customer service
9
Sales
7
R&D
5
Procurement
10
Cybersecurity
2
Finance
6
Fraud detection
11
Compliance
UK AI companies (Business Functions)
UK AI companies (Sectors)
61
Marketing & Advertising
41
Finance
32
Healthcare
17
Retail
6
Agriculture
17
Media & Entertainment
12
Infrastructure
5
Education
5
Food & Beverage
5
Manufacturing
4
Law
38
IT
34
BI & Analytics
16
Human Resources
11
Customer service
9
Sales
7
R&D
5
Procurement
10
Cybersecurity
2
Finance
6
Fraud detection
11
Compliance
84
Activity is these areas is high, in part, because they are
attractively positioned to benefit from AI. All offer:
numerous prediction and optimisation challenges well
suited to the application of AI;
the opportunity for significant, demonstrable value creation.
In the finance sector, performance against a benchmark or
reduced time-to-serve are measurable. In the marketing
function, improved campaign conversion is quantifiable;
large data sets for training and deployment, although access
to data in healthcare can be a challenge;
a path to better-than-human performance, through AI,
that is technically achievable;
alternatives to automation that are impractical (healthcare)
or expensive (finance).
Modern marketing & advertising represents a sweet-spot for
AI. Consumers have billions of touch points with websites and
apps, which provide a rich seam of complex data that is difficult
to analyse using rules-based software. Supplementary data
sources, such as social media, can also be analysed at scale
for the first time using natural language processing AI. Further,
almost every stage of the marketing & advertising value chain
is ripe for optimisation and automation, including: consumer
segmentation; consumer targeting; programmatic advertising;
consumer purchase discovery; and consumer sentiment
analysis. Given significant opportunity, but extensive activity,
competition and commoditisation are likely to be
the primary challenges for early stage AI marketing &
advertising companies.
In select areas, activity is modest relative to market
opportunities. In manufacturing, few startups address a
substantial need. Manufacturers' material costs could be
reduced if analysis of product quality data were improved.
Buffering (the storage of raw materials to compensate for
unforeseen production inefficiencies) could be reduced by up
to 30% with more predictable production. The requirement
for significant domain expertise likely serves as an inhibitor to
younger entrepreneurs in this area.
Opportunities in the finance function, and legal sector, are also
greater than activity implies.
In select areas, activity
is modest relative to
market opportunities. In
Manufacturing, few startups
address a substantial need.
Chapter 8
The dynamics of UK AI
Source: Beauhurst, Crunchbase, Traxcn, MMC Ventures
Fig.39. UK AI companies (Core technologies)
UK AI companies (Business Functions)
13
Autonomous Systems
9
Other
6
AI Infrastructure
7
Computer Vision / Perception
85
One in ten UK AI startups develop core, 'deep tech' solutions
applicable to a wide variety of markets, instead of applications
for specific business functions or sectors. Among these
companies, activity is greatest in the field of autonomous
systems (fig. 39). Entrepreneurs are addressing a requirement
for better independent decision-making within autonomous
cars, trucks, drones and industrial equipment. The technology
can also be applied to overcome challenges relating to fleet
management, vehicle pick-ups and drop-offs, and logistics
networks. Solutions to these challenges are valuable as
network models for transport (Uber), food delivery (Deliveroo)
and other services gain adoption.
AI entrepreneurship has doubled
The number of AI companies founded annually in the UK has
doubled in recent years (20142016) compared with the prior
period (20112013) (fig. 40). Two thirds of all UK AI companies
have been founded since 2014. Since 2014, on average a new
AI company has been founded in the UK every five days.
The number of AI startups is being fuelled by maturing AI
technology and catalysts for AI entrepreneurship.
Technological seeds planted during the last 20 years of AI
research are bearing fruit today. New algorithms are delivering
more effective results. An exponential increase in the availability
of training data has improved algorithms' predictive power.
Additionally, the development of graphical processing units
(GPUs) has reduced the time required to train artificial neural
networks by as much as 90%.
Additional factors, however, are supporting AI
entrepreneurship. Globally, venture capital investment in AI
companies has increased five-fold since 2013 (CB Insights).
The provision of AI infrastructure and services from Google,
Amazon, Microsoft and IBM has reduced the difficulty and cost
of deploying AI solutions. Further, the growth of open source
software, particularly TensorFlow which provides a library of AI
algorithms, has reduced barriers to involvement.
Source: Beauhurst, Crunchbase, Traxcn, MMC Ventures
Fig. 40. AI entrepreneurship has doubled since 2014
10
20
30
40
50
60
70
80
90
100
0
AI companies founded2010 & before
2011
2012
2013
2014
2015
2016
39
21
40
40
68
87
81
86
Chapter 8
The dynamics of UK AI
Marketing & advertising, and finance, have
experienced an influx of new entrants
From a significant base, the marketing & advertising function
(fig.41) and the finance sector (fig.42) have experienced the
largest number of new entrants from the beginning of 2016
to the present.
In areas with few early stage competitors, activity has
increased significantly in the compliance function and
the healthcare sector.
Opportunities in both areas are significant. Regarding
compliance, JP Morgan Chase & Co increased related
expenditure 50% between 2011 and 2015, to $9bn,
employing an additional 19,000 staff (JPMorgan Chase & Co.).
Citi, while reducing its global headcount 32% between 2008
and 2016, doubled its regulatory and compliance staff to
29,000 over 13% of its workforce (Citi). Historically, in-house
efforts by banks, concern regarding client concentration
and competition from US companies may have limited new
compliance startups in the UK, but activity and opportunities
today are extensive.
Source: Beauhurst, Crunchbase, Traxcn, MMC Ventures
Source: Beauhurst, Crunchbase, Traxcn, MMC Ventures
Fig. 41. The marketing & advertising function has the highest number of new AI companies
Fig. 42. The finance sector has the highest number of new AI companies
Number of new AI companies0
Marketing & Advertising
IT
BI & Analytics
Compliance
Customer service Human Resources
10
8
2
2
4
6
The Marketing & Advertising function has the highest number of new companies
2
4
6
8
10
Finance
Media & Entertainment
Healthcare
Agriculture
Number of new AI companies16
14
18
12
10
8
6
4
2
0
17
8
8
3
3
3
Infrastructure
Retail
The Finance centre has the highest proportion of new companies
87
A nascent sector that is maturing rapidly
UK AI companies are nascent. Two thirds are at the earliest
stages of their journey, with Seed or Angel funding (fig. 43).
The sector is maturing rapidly, however, with nearly one in
seven having received more mature, Growth-stage funding.
Relative to European counterparts, UK AI companies are less
embryonic (fig. 44). The proportion of European companies
at the earliest stages of their funding is 20 percentage points
higher than in the UK. Further, the proportion of mature
(Growth stage) European companies is less than half that
of the UK.
Source: Beauhurst, Crunchbase, Tracxn, MMC Ventures
Source: Beauhurst, Crunchbase, Tracxn, MMC Ventures
Fig. 43. The UK AI sector is nascent
Fig. 44. European AI companies are highly embryonic
43%
23%
13%
21%
AI soware companies by stage
AI soware companies by stage
UK
UK
Europe (ex UK)
0%
10%
20%
30%
40%
Angel
Seed
Early Stage
Growth
Angel
Seed
Early Stage
Growth
50%
60%
70%
80%
90%
100%
The UK AI sector is Nascent
Overseas AI companies are Highly Embryonic
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
5%
43%
23%
13%
AI Soware Companies by Stage (2016)
UK
Europe (Ex UK)
US
Angel
Seed
Early Stage
Growth
Overseas AI Companies are Highly Embryonic
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
78%
71%
6%
7%
9%
15%
10%
4%
62%
16%
15%
8%
21%
63%
19%
13%
43%
23%
13%
21%
AI soware companies by stage
AI soware companies by stage
UK
UK
Europe (ex UK)
0%
10%
20%
30%
40%
Angel
Seed
Early Stage
Growth
Angel
Seed
Early Stage
Growth
50%
60%
70%
80%
90%
100%
The UK AI sector is Nascent
Overseas AI companies are Highly Embryonic
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
5%
43%
23%
13%
AI Soware Companies by Stage (2016)
UK
Europe (Ex UK)
US
Angel
Seed
Early Stage
Growth
Overseas AI Companies are Highly Embryonic
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
78%
71%
6%
7%
9%
15%
10%
4%
62%
16%
15%
8%
21%
63%
19%
13%
88
Chapter 8
The dynamics of UK AI
Half of Europe's AI companies are in the UK
The UK's 389 Angel to Growth stage AI software companies
compete with approximately 490 in Europe (excluding the UK)
and 1,267 in the US (fig. 45). Nearly half of all AI companies in
Europe are in the UK.
AI is well represented in the UK, where a slightly higher
proportion of startups (2.0%) focus on AI than in the US
(1.6%) or Europe ex-UK (1.7%) (Crunchbase) (fig. 46). The UK
maintains valuable assets for strength in AI, including a quarter
of the world's top 25 universities and a growing ecosystem
of AI executives and investors following the acquisitions of
Deep Mind, SwiftKey, Magic Pony and other UK AI companies.
London also offers ready access to leading financial
services customers.
The competitive environment will remain fierce, given the large
number of AI startups in the US, richly funded US competitors,
a broader US AI ecosystem and talent pool, and the potential
impact of Brexit.
Source: Crunchbase, MMC Ventures
Source: Crunchbase, MMC Ventures
Fig. 45. UK AI comprises 18% of the European and US total
Fig.46. A slightly higher proportion of UK startups focus on AI
UK
Europe (ex UK)
US
UK AI comprises 18% of the European and US total
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
490 companies
(23%)
389 companies
(18%)
1,267 companies
(59%)
Early stage AI companies in the UK and US
US
Europe (ex-UK)
UK
AI startups as % total startups4%
3%
2%
1%
0%
1.6%
1.7%
2.0%
The compliance function has the highest proportion of new companies
89
The journey to monetisation can
be longer
Over 40% of the UK AI companies we meet have yet to
receive recurring revenue (fig.47). This is not solely a result of
companies being 'early stage'. In a sample of 70 companies,
the median profile is a company founded 3 years ago that has
raised $2.0m.
There is a perception, among some entrepreneurs in other
fields and corporate executives, that most AI companies intend
to be acquired for large sums of money while pre-revenue,
instead of growing revenue by selling software and services.
We believe this perception out of date, and based on an earlier,
first wave of AI companies that developed core AI technology
instead of vertical applications. All the AI companies we meet
are implementing or developing monetisation plans.
AI companies can take longer to achieve significant
monetisation because:
1. The bar to a minimum viable product in this technically
challenging field can be higher, requiring longer
development periods.
2. Companies may prioritise data acquisition ahead of
revenue generation, to create defensibility through data
network effects.
3. Over 90% of AI companies are B2B. The long sales cycles
typical in B2B sales are exacerbated by many AI companies'
focus on sectors, such as finance, with sensitive data sets and
arduous procurement cycles.
4. Deployment periods can be lengthy given extensive data
integration, data cleansing and product customisation
requirements for each client. Many of the companies we
meet monetise client integration and customisation work
via project revenue.
5. A limited number of implementation personnel can inhibit
growth. "We couldn't implement more sales even if we
had them" (CEO, early stage UK AI company). In many
early stage companies, that have small teams, one third
of personnel are engaged in deployment support.
The longer path to monetisation poses a challenge to AI
startups, particularly because expenditure can be higher due
to the cost of AI talent. We recommend that AI companies raise
sufficient capital to withstand this challenging period and to
maximise go-to-market initiatives.
Source: MMC Ventures
Fig. 47. Many companies have yet to receive
recurring revenue
Over 40% of companies are pre-revenue
Proof of
concept revenue
33%
Recurring revenue
55%
Pre-revenue
12%
90
Early stage AI companies are attracting
larger investment rounds
Globally, investments in early stage AI firms are typically
20%-50% larger than capital infusions into general software
companies at a comparable stage (fig. 48). This effect, while
pronounced from the Angel to Series B stages, generally
reduces and then disappears from the Series C stage and
beyond. (Series D raises may be an exception, but the number
of data points is modest).
Source: Pitchbook
Fig. 48. Globally, early stage AI companies are raising larger rounds
$5M
$10M
$15M
$20M
$25M
$30M
$35M
$40M
$0M
Angel
Seed
Series A
Series B
Series C
Series D
Series E
Median Capital InvestmentMedian Investment (2016): Global soware company
Median Investment (2016): Global AI soware company
The 'AI extra'
122%
38%
20%
52%
21%
-7%
0%
Early stage AI companies
are attracting larger
funding rounds due to
sector fundamentals and
dynamics in the supply
and demand of capital.
Chapter 8
The dynamics of UK AI
91
Early stage AI companies are attracting larger funding rounds
due to sector fundamentals and dynamics in the supply and
demand of capital. AI companies' capital requirements can
justify greater investment, given the longer development cycles
to achieve a minimum viable product, the high cost of AI talent,
and the larger teams required for complex deployments.
Beyond fundamentals, capital infusions are being inflated by
extensive supply and limited demand. Many venture capitalists
wish to invest in AI, and there are relatively few AI companies
in which to invest. Globally, venture capital investment in early
stage AI companies has increased ten-fold in five years, while
the number of investable prospects remains limited.
This dynamic is reflected in the UK, where staging of capital
can be atypical. A sizeable minority of UK AI companies
progress from their seed round to a larger raise than is typical
for a subsequent round. One in three UK AI companies that
raised more than $8m in a funding round had raised less than
$1m previously (fig. 49).
One in three UK
AI companies that
raised more than
$8m in a funding
round had raised
less than $1m
previously.
10x
Globally, venture capital
investment in early stage
AI companies has increased
ten-fold in five years.
Source: CB Insights
Source: Beauhurst, Crunchbase, Tracxn MMC Ventures
Fig. 49. One in three UK AI companies raise
large post-Angel rounds
1 in 3 UK companies raise large post-Angel rounds
Large (>$8m)
post-Angel round
30%
Typical
post-Angel round
70%
92
Customers
Source: CheckRecipient
Function
How do you use AI to
solve problems?
Businesses
Compliance
"We're using machine learning to build a next-generation email security platform, to stop
highly sensitive emails being sent to the wrong people. Misaddressed emails are the
number one digital data security incident reported to the Information Commissioner's
Office. Incumbent solutions rely on admin-heavy, rules-based approaches or require
significant user behaviour change.
We apply machine learning to historical email data, to understand conventional behaviour
of users within an email network and their sending patterns. We can then detect
deviations from these patterns and flag them. This automatically protects against data loss,
without any administrative overhead or change in the way end users normally send emails."
Featured company
Chapter 8
The dynamics of UK AI
93
Customers
Source: Darktrace
Function
How do you use AI to
solve problems?
Businesses
Cybersecurity
"We use artificial intelligence to discover and respond to cyber-threats, which would
otherwise have gone unnoticed, within organisations.
Our AI system is inspired by the principles of the human immune system. It can detect
novel threats without prior knowledge of what is malicious. By learning what is 'normal'
within a network, our technology identifies abnormal behaviour. With our technology,
a threat can automatically and quickly be contained, giving a human security operator
time to catch up.
Our AI was successful in defending against the WannaCry ransomware campaign that
affected over 200,000 devices across 150 countries in May 2017. Our Enterprise
Immune System solution not only detected the attack at its first signs, but also stopped
it from spreading. It has also identified in-progress insider threats, hacks of connected
devices (IoT), and other sophisticated cyber-attacks."
Featured company
94
Chapter 8
The dynamics of UK AI
Customers
Source: DigitalGenius
Function
How do you use AI to
solve problems?
Businesses
Customer Service
"We use AI to automate the repetitive parts of the customer service workflow and deliver
better customer experiences by making conversations more efficient in a range of
channels from email and chat to social media and mobile messaging.
Our Human+AI Customer Service Platform combines the best of human and machine
intelligence to enable companies to exceed rising customer service expectations. We
use deep learning algorithms to train neural networks on end-to-end historical customer
service conversations. The AI converts words into sets of numbers and uses mathematical
operations to extract meaning, context, and nuances of customer interactions. This
enables the AI to deliver accurate prompts, offer automated replies and complete fields
with correct data. With DigitalGenius, customer service agents focus on cases where
human interaction and expertise are needed."
Featured company
95
Customers
Source: Gousto
Sector
How do you use AI to
solve problems?
Consumers
Food & Drink
"We apply AI to provide huge improvements to customer experience. It's helped us to
reduce food waste to nearly 0%, achieve record low error rates in packaging and delight
customers with new recipes.
While supermarkets waste around 20% of food, we're significantly lower due to AI-
powered probabilistic forecasting and optimisation, which enable just-in-time grocery
operations.
To enhance customer experience, we've applied AI to our own recipe ontology (a
knowledge database), which enables us to fully understand our recipes, compare new
recipes to popular existing ones, and make recommendations to our customers efficiently."
Featured company
96
Chapter 8
The dynamics of UK AI
Customers
Source: Luminance
Sector
How do you use AI to
solve problems?
Businesses
Law
"We use AI to offer an entirely new approach to legal document review. Our vision is to
give lawyers instant, meaningful insight into vast bodies of documents, and enable AI
to carry out low-level cognitive tasks on behalf of legal teams so that they can focus on
higher value analysis. We provide lawyers with a plug-and-play tool to assist with the 'first
pass' of document review, by intelligently sorting documents in order of likely priority
with potential issues highlighted. Our initial use case is in document review for M&A due
diligence and our customers have found efficiency gains of over 50% vital in the fast-
paced and competitive legal market. There are many other use cases for our technology,
including insurance, compliance, property and in-house contract management.
Our technology, developed by our team of Cambridge University mathematicians, works
by comparing thousands of legal documents simultaneously to detect patterns in their
language using a unique combination of machine learning techniques. These patterns can
be used to identify key information within documents such as clause types, languages
and locations or to expose anomalies that may pose a risk to the project. Lawyers can
focus and prioritise their review early in a project, while gaining additional insight through
data visualisation."
Featured company
97
Customers
Source: Peak
Function
How do you use AI to
solve problems?
Businesses
Analytics
"We enable our customers to use AI to grow their businesses. We deploy, for example,
AI-powered predictive analytics for lead scoring, lead generation, churn prediction
and up-sell estimation. All of these have a huge impact on a company's ability to
grow revenues.
We've deployed demand forecasting algorithms that enabled companies to plan and
meet demand more effectively. Our customers increased revenue, order fulfilment and
customer satisfaction while reducing inventory and transport costs. Through improved
demand forecasting, for example, we've helped FTSE listed businesses free up millions
of pounds of working capital and save on waste."
Featured company
98
Chapter 8
The dynamics of UK AI
Customers
Source: Senseye
Sector
How do you use AI to
solve problems?
Businesses
Manufacturing
"We enable manufacturers to perform predictive maintenance with much greater accuracy
than has been possible before, preventing expensive unplanned downtime.
We use machine learning to improve and automate what condition-monitoring engineers
have done manually in manufacturing organisations, at a scale far beyond human
capability. We provide value from the data companies already have, but are not using
effectively. This generates significant value in sectors that are margin sensitive or looking
for competitive advantage."
Featured company
99
Customers
Source: Signal Media
Function
How do you use AI to
solve problems?
Businesses
Analytics: Media Monitoring
"We're using AI to transform how organisations use information to drive business impact.
Our vision is to power informed decision-making everywhere. We have built a powerful
AI engine that can monitor, analyse, extract insight and recommend actions to users in
every key business function, from sales to strategy and public relations.
Our AI understands context and reads 2 billion articles in 2 seconds, offering hyper-
relevant results in real-time and surfacing them in a simple-to-use platform. It also
translates from over 40 languages - entirely for free."
Featured company
100
Customers
Source: Snap 40
Sector
How do you use AI to
solve problems?
Hospitals
Healthcare
"Our goal is to protect the health of every human being by bringing clinical attention to
deteriorating health at the earliest possible point.
We operate in hospitals and in patients' homes, collecting real-time data and metrics
from the patient using our own monitoring device. We monitor the patient with ICU-
like accuracy, but without any leads or wires - the patient can go about their day, with
snap40 at their side. We then take this huge volume of data and identify the patients that
require physician or nurse attention. No physician or nurse can monitor data streams from
hundreds of patients - that's where AI comes in. If we can bring medical attention to at-risk
patients earlier, we can stop them deteriorating. We can enable patients to stay in their
own homes, prevent the need for unnecessary hospitalisation, and save lives.
By allowing physicians, nurses and healthcare providers to scale up massively the number
of patients they can manage in lower acuity environments, like patients' own homes, we
also provide cost savings to providers through fewer hospitalisations and reduced
lengths of hospital stays."
Featured company
Chapter 8
The dynamics of UK AI
101
Customers
Source: StoryStream
Function
How do you use AI to
solve problems?
Businesses
Marketing & Advertising
"We help global brands transform the performance of their digital content to increase their
marketing ROI. The complexity in understanding what content to create, how to manage
and publish it at scale, and then show its ROI is a significant data problem as the link
between these factors is generally broken. AI offers a powerful way to connect and scale a
brand's content operations by helping marketing teams more efficiently analyse, manage
and distribute content and show its ROI better than they have been able to do before.
We use custom-built deep learning algorithms to automatically tag and analyse content.
We then match that content with consumer analysis to enable a more personalised and
engaging brand experience."
Featured company
102
Chapter 8
The dynamics of UK AI
Customers
Source: Vortexa
Sector
How do you use AI to
solve problems?
Businesses
Energy
"We use AI to help optimise the flow of energy on the planet. Energy is a $7 trillion industry
which is directly linked to the prosperity of our society. Today, there is an immense gap
in information between what happens in the physical world and what reaches the energy
markets before decisions need to be made.
Vortexa uses machine learning algorithms to piece together an extremely large amount
of complex and disparate data, particularly from new satellite constellations, to help us
maximise the utility and value of our natural energy resources."
Featured company
103
104
AI entrepreneurs'
perspectives
Chapter 9
Summary
Entrepreneurs anticipate a new, AI-driven future. AI will improve decision-making and
increase automation in every sector and most businesses functions, with profound effects.
Early stage companies offer buyers innovation and flexibility. Startups enable established
companies to harness new technologies, and buyers can shape evolving propositions from
early stage companies to their bespoke needs.
When engaging with early stage companies, buyers can maximise value by adopting a
collaborative mindset and simplifying procurement processes.
Successful AI entrepreneurs deliver solutions, not technology. AI companies should focus
on solving a business problem, not on technology as an end in itself. Identifying repetitive,
data-intensive problems well suited to AI enables companies to attract clients and address
inefficiencies in their own organisations.
Access to data, scarce talent and difficult productisation processes are key challenges
for early stage AI companies. Companies can mitigate these challenges, respectively, by
implementing data acquisition strategies early in their journey, building relationships with
academic institutions and research communities, and developing feedback loops between
development teams and customer success functions.
Key success factors for AI entrepreneurship are: customer focus; continuous technological
evolution; development of data access strategies; long-term planning; and perseverance
in this demanding field.
105
106
Recommendations
Executives
Entrepreneurs have a valuable understanding of the AI-enabled future. Engage with them to improve your
organisation's understanding of AI, and how its potential could unlock strategic value for your organisation
in the long term.
Early stage companies can be powerful enablers of innovation. Explore opportunities to collaborate with early
stage companies by creating horizontal innovation departments and engaging in proof-of-concept projects.
To maximise value from early stage companies, consider a simplified procurement process, adopt a collaborative
mindset, provide continual feedback and expect capabilities to evolve over time.
Your company's data and referencability are valuable assets for early stage companies. Explore, reasonably,
opportunities to shape a supplier's offering to your organisation's specific needs, given your value to the supplier.
AI entrepreneurs will face challenges, including access to talent and difficult productisation processes, that your
organisation will encounter if it develops AI capabilities internally. Through test-and-learn engagements with
AI startups, develop your own organisation's talent and productisation strategies.
Chapter 9
AI entrepreneurs' perspectives
AI has the potential to create value in most business processes and can be a powerful tool for all early stage
companies not just 'AI companies'. Identify opportunities to apply AI to business problems and develop
an AI strategy to avoid losing competitive advantage.
To attract customers and investors, articulate solutions to business problems rather than AI technology as an
end in itself.
Given their importance and difficulty, from the inception of your company develop strategies for data access,
AI talent recruitment and productising AI. Plan for the long term.
Create buy-in across your company, and processes for cross-functional collaboration, to support the effective
delivery of AI.
View AI as a capability, not a feature. Anticipate ongoing development and resource the initiative accordingly.
AI can improve your own company's processes as well as customers'. Look within your company for
opportunities to automate manual processes and free personnel to focus on client activity.
Entrepreneurs
Identify founders, who combine a profound vision of AI's ability to unlock value with the ability to articulate to
buyers down-to-earth solutions that address business challenges.
Prioritise evaluating AI companies' access to data and ability to attract AI talent, given the importance of these
factors to AI companies' success.
Evaluate the extent to which leadership teams have the necessary domain expertise and account management
capabilities to engage with large buyers, given demanding go-to-market dynamics.
Investors
107
Entrepreneurs anticipate a new
AI-driven future
"It's super clear to me
we are moving towards a
completely different reality
in the coming decades."
Timo Boldt, Gousto
Entrepreneurs believe AI will have a "profound effect."
(Richard Potter, Peak). "The impact of AI will be like that of
electrification." (David Benigson, Signal Media). By enabling
companies to incorporate broader data sets into analyses,
and identify patterns in data more effectively, AI will improve
decision-making and increase automation. "An AI system is
incredibly good at digesting data, drawing out patterns and
identifying correlations at near-instant speeds." (Emily Foges,
Luminance). As a result, "AI drastically alters the information
available for decisions and helps companies make better
and smarter decisions with greater business impact. With AI
we can deliver insights with real business value far faster, far
more effectively and far more accurately." (David Benigson,
Signal Media).
Entrepreneurs highlight that AI will impact every sector,
from energy and healthcare to law and manufacturing.
"The applications of AI are endless." (Timo Boldt, Gousto).
Energy: "The ability to harness, move and utilise energy is
directly linked to our prosperity and well-being. AI is able to
process data captured from the physical world in real-time to
help us make better decisions about the flow and use of energy
at a global scale." (Fabio Kuhn, Vortexa).
Healthcare: "The impact on healthcare will be huge. With
patients living longer and greater incidence of chronic disease
as a result, we either have to recruit thousands more doctors
and nurses or ask how we can use technology to better
leverage the people we have. AI multiplies the efforts of a
single physician." (Chris McCann, Snap40).
Law: "AI is starting to have a transformative impact on the legal
sector. With AI taking the burden of low-level cognitive tasks,
lawyers can optimise their practice and work smarter, faster and
more effectively." (Emily Foges, Luminance).
Manufacturing: "AI's impact on manufacturing will be
massive. The sector is data-rich, but not doing a lot with that
data. Using AI, we enable manufacturers to get value from the
data they have." (Simon Kampa, Senseye).
Most business functions, from analytics and compliance to
cybersecurity and marketing, will also be improved through
AI. "AI will be at the centre of business." (Dmitry Aksenov,
DigitalGenius).
Analytics: "In the IT industry, AI can fix a broken model.
Companies shouldn't pay for consulting work on a time and
materials basis, or try to make best use of software themselves.
They should pay for guaranteed results. AI can democratise
data analytics, offering advanced technology to all." (Richard
Potter, Peak).
Compliance: "AI is having a disruptive impact. AI can
automate compliance, providing a better user experience and
reducing costs, while delivering results that are superior to
what humans could achieve." (Tim Sadler, CheckRecipient).
Customer Service: "Imagine customer service inquiries that
are solved in minutes, not hours or days, regardless of the
channel being used. AI will make that possible, by automating
repetitive parts of agents' workflow and enabling faster
customer service resolutions." (Dmitry Aksenov, DigitalGenius).
Cyber Security: "Cyber security is one of the greatest
challenges of our day. In this age of limitless data and complex
networks, humans can't keep up, perfectly, 24/7. AI is having a
huge impact." (Nicole Eagan, Darktrace).
Marketing: "I think AI is fundamental and will have a profound
impact in marketing. The art of storytelling hasn't gone away.
But to do that at scale now is incredibly difficult." (Alex Vaidya,
StoryStream).
Tim Sadler
CheckRecipient
Nicole Eagan
Darktrace
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Chapter 9
AI entrepreneurs' perspectives
Innovative early stage companies recognise the competitive
advantage they provide to buyers. "We can offer real-time,
hyper-relevant monitoring and analysis in 40 languages while
competitors offer next-day services." (David Benigson, Signal
Media). "There's much excitement about AI in sectors that tend
to be margin sensitive and driving for competitive advantage."
(Simon Kampa, Senseye).
AI technology is expected to become ubiquitous. "We believe
AI will become a fundamental part of every organisation
in the next five years." (Alex Vaidya, StoryStream). "In five
years, everyone will presume you're using AI." (Tim Sadler,
CheckRecipient). "Today, we're talking about humans being
supported by AI. In five years, we'll see AI running most
repetitive parts of an organisation, managed by people
focusing on the high-touch inquiries." (Dmitry Aksenov,
DigitalGenius).
Early stage companies offer buyers
innovation and flexibility

Early stage companies can be key enablers of innovation
for large buyers. "There is huge opportunity for established
organisations to harness the forward-thinking culture and
technology of startups to bring innovation to their own
businesses." (Emily Foges, Luminance). While AI solutions
today are nascent, "they can solve a wide range of problems."
(Alex Vaidya, StoryStream).
"There's real opportunity
for would-be clients to get
in early and help shape
an AI service around their
bespoke needs."
David Benigson, Signal Media
For buyers willing to engage with early stage companies, the
advantages are significant. Early stage companies "have a
direction and compass, but not a detailed roadmap." (David
Benigson, Signal Media). "Early adopters in the UK and US
recognise they can shape the product and proposition.
They know that if they give feedback, we'll seriously
consider implementing it." (Chris McCann, Snap40).
"Early stage companies will often dedicate more time and
resources to a client than later stage companies can."
(Nicole Eagan, Darktrace).
Buyers maximise value through
collaboration and simplified procurement
Being open to innovation is essential for buyers to maximise
value from early stage companies. "A lot of it is cultural.
Companies need to be open about engaging with startups."
(Simon Kampa, Senseye). Buyers can catalyse innovation,
and engagement with early stage companies, by: creating
horizontal innovation departments that seek sources of
disruption; structuring proof-of-concept programs to test
products in less demanding environments; and initiating
corporate venture capital initiatives. During deployments,
"constructive feedback, and seeking solutions together rather
than standing off, is by far the best way to engage." (David
Benigson, Signal Media).
"Working with a very
collaborative approach
is key."
Alex Vaidya, StoryStream
Procurement processes are at best a headwind, and at worst
a barrier, to early stage companies' engagement with large
buyers. Enterprise procurement processes are rarely suited to
suppliers with small balance sheets and limited track records.
Simplified procurement processes for early stage suppliers
can assist.
Dmitry Aksenov
DigitalGenius
Timo Boldt
Gousto
109

Conversely, there are steps early stage companies can take
to ease buyers' engagement. Entrepreneurs recommend
that early stage companies: ensure they receive sufficient
information to understand customers' requirements deeply;
offer pilot projects to demonstrate value before requiring
commitments; focus on end user experience to increase
client-side adoption; and manage expectations that
development will be an iterative process.
Successful entrepreneurs offer
solutions, not technology
Effective entrepreneurs solve tangible business problems
instead of focusing on technology as an end in itself. "Always
focus on the problem you're using AI to solve, rather than the
AI technology. Customers don't care how you're solving the
problem." (Tim Sadler, CheckRecipient). Others agree.
"You need to have a strong use case and need for AI.
Otherwise it's very likely it won't have any impact." (Timo
Boldt, Gousto). "AI itself doesn't solve anything. Use it to
solve things." (Simon Kampa, Senseye).
AI entrepreneurs are identifying repetitive, data-intensive
problems to which AI is well suited both for customers and
within their own companies. "Machine learning can transform
predictive analytics for lead scoring, churn propensity, price
optimisation, demand forecasting and more. Internally, we use
AI to automate our day-to-day work, which enables our team
to help more companies and deliver greater value to clients."
(Richard Potter, Peak). "Work out what parts of your business
require intensive repetitive effort that's where AI can be most
effective internally." (David Benigson, Signal Media).
Successful entrepreneurs are also mindful of contextual
considerations. In some sectors and business functions,
transparency around AI-led decision-making is important. "In
healthcare it's not good enough to say you can't look inside the
algorithm. There's a greater need to understand how, and why,
a model is doing what it's doing." (Chris McCann, Snap40).
In other sectors, enabling behaviour change is key. "No matter
how sophisticated the technology, most users won't have the
time or inclination to learn and adapt to a complicated new
system. Entrepreneurs should think as much about usability
and approachability as underlying mathematics." (Emily
Foges, Luminance).
"Users won't have the time
or inclination to adapt
to a complicated new
technology. Entrepreneurs
should think as much about
usability as underlying
mathematics."
Emily Foges, Luminance
Richard Potter
Peak
Simon Kampa
Senseye
Emily Foges
Luminance
110
Data, talent and productisation are key
challenges for AI startups
Entrepreneurs highlight the limited availability of training
data, competition for AI talent, and the difficulty of creating
production-ready technology as key challenges when
developing AI.
"After people, data is
the single most valuable
element of an AI company.
The right data can massively
accelerate the feedback
loop for AI models, making
it virtually impossible for
others to catch up."
Fabio Kuhn, Vortexa
1. Access to training data is critical
Data is "the lifeblood of any AI. Without it, nothing happens."
(David Benigson, Signal Media). "After people, data is the
single most valuable element of an AI company." (Fabio Kuhn,
Vortexa). Access to initial data sets for training is challenging.
"It's a classic chicken and egg problem. Early customers, and
thus data, are hard to get when you don't have any existing
reference client." (Tim Sadler, CheckRecipient).
Companies mitigate the difficulty by developing powerful
use cases "customers won't give up their data unless you're
offering them real value." (David Benigson, Signal Media) and
by implementing a data acquisition strategy from early in their
lives. "We started collecting data very early in our journey."
(Timo Boldt, Gousto). Compromising on early pricing to
secure access to valuable customer data can be effective.
2. Recruiting AI talent is challenging
"The number one challenge when developing AI is recruiting
the best (human) brains. It takes effort, but is non-linear. In
complex problems, a genius can do what an army of people
can't, and often, you only need a handful of geniuses to 'make
a dent in the universe'." (Fabio Kuhn, Vortexa). "Access to
talent, and its competitiveness, is the biggest challenge."
(David Benigson, Signal Media).
Startups compete with multiple categories of competitors
including large technology companies (Google, Amazon,
IBM, Microsoft, Facebook), banks, professional service firms,
and other early stage companies for data scientists, AI
experts and AI engineers. Recruiting staff that have a balance
between theoretical expertise and commercial experience,
and experience running an AI team, are additional difficulties.
"Because it's a nascent field, most of the people we interview
are highly academic. But they haven't productised at scale.
We've had to be super selective." (Chris McCann, Snap40).
London is "a good place to be, when looking for AI talent."
(Dmitry Aksenov, DigitalGenius). "London has one of the best
pools of AI talent in the world which is the main reason why
we are here." (Fabio Kuhn, Vortexa). To identify and attract
talent, entrepreneurs recommend "building deep relationships
with academic institutions, being an active member of research
communities, publishing papers and collaborating with
universities. We try to engage with developers well before
they're looking for a job, and let them do what they love."
(David Benigson, Signal Media).
3. Developing production-ready AI is difficult
"Taking what works well in a lab and getting it to work in
a diverse population is a big challenge." (Chris McCann,
Snap40). "Developing AI in a lab on static datasets is fun, but
running AI in production is very hard. The real world is full of
black swans and exceptions. We've learned to overcome them
by getting great at cross functional collaboration, building
integration with the tech team, and constant monitoring of
risk." (Timo Boldt, Gousto). Entrepreneurs recommend "taking
AI into the real world as soon as possible. For AI to work on real
data in real environments, it can't just be trained in the lab on
small, controllable sets." (Nicole Eagan, Darktrace).
Chapter 9
AI entrepreneurs' perspectives
Chris McCann
Snap40
David Benigson
Signal Media
111
Beyond productisation, true innovation is demanding.
"Most data scientists take existing algorithms, mash them
together, and see what they can do with data. We've had to
do fundamental research to create new algorithms." (Simon
Kampa, Senseye). "Finding the balance between innovation
and impact is also key. Without being in-market, it's very hard
to know whether the extra 1% is really adding value to the client
experience." (David Benigson, Signal Media). Feedback loops
between development and customer success teams
are valuable.
4. Commercial messaging must resonate
High levels of interest in AI present difficulties as well as
opportunities. "There's a lot of marketing language out there
about AI. Noise in the market isn't always helpful since many
others aren't actually doing heavy-duty machine learning."
(Simon Kampa, Senseye). Messaging may need to overcome
buyer reticence. "Integrating advanced technology into a
sector, such as law, known for conservatism will always pose
a challenge." (Emily Foges, Luminance). "We go through a
change management program to educate the workforce on
our 'human plus AI' approach. We explain that AI takes care of
repetitive tasks so people can focus more on bigger things."
(Dmitry Aksenov, DigitalGenius).
Customer focus, continual evolution and
long-term planning are key success factors
for AI entrepreneurship
For early stage companies developing AI, leading
entrepreneurs offer five recommendations:
1. Identify the customer benefits of AI
"It's very easy to use AI as a me-too, jump on the bandwagon
idea. But you can't sell AI unless it has a very clear value to your
end customer. If you're going to invest in AI, you need to be
clear about what the benefit is and how you can differentiate
using it." (Alex Vaidya, StoryStream). "Don't use AI just because
you think it'll sound good." (Chris McCann, Snap40).
2. Anticipate continuous evolution.
"Remember that AI is a capability, not a product. It's always
improving, and the more exposure it has to stimuli the faster it
will learn. The more you push the envelope, the better it will
be." (David Benigson, Signal Media).
3. Develop a data access strategy
"The more data your AI has, the better its output will be.
So make sure you have enough of it. Build access to data
at scale from day one." (David Benigson, Signal Media).
Entrepreneurs recommend investing significantly in data
ingestion, processing and management systems to provide
a foundation of clean data.
4. Adopt a holistic, long-term view
"AI capabilities can improve everything, from customer
experience to business performance. Plan for the long-term
and then obsess about capabilities to make your vision come
true over five to ten years." (Timo Boldt, Gousto).
5. Persevere
"For AI companies, the ability to solve some of the really
hard problems in the world takes time and depth. It follows a
different curve. It requires tremendous persistence. Endurance
is key." (Fabio Kuhn, Vortexa). "As with anything worth doing,
AI is hard." (Richard Potter, Peak).
Fabio Kuhn
Vortexa
Alex Vaidya
StoryStream
"Plan for the long term and then obsess
about capabilities to make your vision
come true over five to ten years."
Timo Boldt, Gousto
112
113
An investment
framework for AI
Chapter 10
Summary
The AI paradigm shift presents opportunities to invest in disruptive early stage software
companies as well as public companies developing competitive advantage.
AI acquisitions have increased significantly, averaging ten per month in 2017.
A first wave of acquisitions focused on core AI technologies 'deep-tech' AI research
or AI-powered computer vision and language capabilities with cross-sector utility.
We are entering a second wave of AI investment and exits. Capital is being allocated
to developers of vertical applications.
We provide our AI Investment Framework, which identifies 16 success factors for early stage,
applied AI companies. We divide the 16 factors into three categories: value potential, value
realisation and defensibility. Applying the success factors helps highlight attractive
investment opportunities.
Keys to value potential are: scope for value release and disruption; unattractive alternatives;
suitability of AI to a business problem; a path to acceptable technical performance; and
suitability of available data.
Keys to value realisation are: management commerciality; quantifiability of ROI; buyer
readiness; benign regulation; and deployment scalability.
Keys to defensibility are: distance from AI monoliths' offerings; domain complexity; data
network effects; proprietary algorithms; attractive AI talent dynamics; and strong capitalisation.
114
Recommendations
Chapter 10
An investment framework for AI
Evaluate your company's strengths and weaknesses against the 16 factors.
Highlight to buyers and investors, as appropriate, your company's strengths in key criteria including value
release, management commerciality, quantifiability of ROI, data network effects, AI talent, vertical focus and
domain expertise.
Address headwinds to value realisation by automating deployment requirements, particularly customer data
processing, and focusing early on building a capable sales organisation.
Investors decline to invest in startups due to doubts about management commerciality and tangibility
of ROI more than for any other reasons. Focus remediation and messaging on these critical issues.
Entrepreneurs
While most companies will incorporate AI in the years ahead, today there remains a category of companies
disrupting incumbents by placing AI at the heart of their value proposition. Consider developing a basket of
AI-driven investments.
Apply the 16 factors, in addition to your usual considerations, to evaluate early stage applied AI companies.
Remain open-minded to select investment opportunities in horizontal AI providers. While rarer, and with differing
dynamics to application providers, companies with world-class technology valuable to an AI platform provider
can be an attractive technology or talent acquisition.
Get in touch with us to discuss your perspective. Where do you agree, or disagree, with our thinking?
Investors
Executives
Apply the 16 factors to assess your own organisation's AI capabilities.
Use the 16 factors to identify strengths and weaknesses, and support due diligence, of AI partners
and potential acquisition opportunities.
115
AI acquisitions have increased significantly
The AI paradigm shift presents opportunities to invest in disruptive private software
companies as well as public companies establishing competitive advantage.
Acquisitions of early stage AI companies have increased significantly, annually, since 2012.
If activity in 1H17 is sustained, AI acquisitions in 2017 will average ten per month (fig.50).
The first wave of acquisitions focused on
core AI technologies
The first wave of capital allocation and acquisitions in AI
focused on providers of 'deep-tech' AI research (for example,
Deep Mind) and AI-powered computer vision and language
capabilities with cross-sector utility (Perceptio, Magic Pony,
Wit.ai, Equivio). Global consumer and enterprise technology
companies, including Google, Apple and Microsoft, have led
consolidation (fig.51, overleaf). Innovators recognised early
that AI unlocks value from large data sets, particularly visual
data, and offers new opportunities for growth from novel
ways for people to interact with computers (voice control of
smartphones and in-home devices) to new product categories
(autonomous vehicles).
15
30
45
60
75
90
105
120
135
150
0
AI Acquisitions2012
2013
2014
2015
2016
2017E*
*Estimate, extrapolated from 60 acquisitions in 1H17
11
21
39
44
78
120
Source: CB Insights
Fig. 50. AI acquisitions have increased annually since 2012
116
Chapter 10
An investment framework for AI
Fig. 51. Google has led an acceleration in AI acquisitions
Source: CB Insights
117
The second wave of activity will focus
on application providers
We are entering a second phase of AI investment and
acquisitions. Acquisitions of core AI technology companies
will continue. However, as AI technologies mature and
become more accessible, focus will increase on providers
of AI-powered vertical applications. 90% of early stage
AI companies in the UK are now applying AI to address
challenges in specific business functions or sectors
(MMC Ventures). We describe 31 business processes
being addressed, across eight sectors, in Chapter 4.
Our Investment Framework describes
success factors for applied AI companies
We have developed an Investment Framework that identifies
16 success factors for early stage applied AI companies. The
Framework is applicable to the nine in ten companies applying
AI to solve a problem in a specific business function or sector.
Success factors for developers of 'core' AI technologies differ.
Companies that have the potential to create significant value,
can realise their potential, and can defend their value, offer
the prospect of attractive returns. Accordingly, we group the
16 success factors into three categories: value potential, value
realisation and defensibility (fig. 52). The factors span six
competencies: strategy, technology, data, people, execution
and capital (fig. 53).
Value
Potential
Value
Realisation
Defensibility
16 Success Factors:
in three categories
16 Success Factors:
across six competencies
Data
Technology
Strategy
Capital
Execution
People
Source: MMC Ventures
Fig. 52.
Fig. 53.
16 success factors: in three categories
16 success factors: aross six cmpetencies
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Chapter 10
An investment framework for AI
When evaluating AI companies, there are new factors to
consider given the unique dynamics of AI, such as data network
effects, and traditional considerations on which to place greater
emphasis, such as management commerciality. The traditional
considerations we emphasise follow our meetings with 250
early stage AI companies in the UK.
Prospects can be assessed against this framework to help
identify strengths and challenges (fig. 54).
Value potential
Value release
Unattractive alternatives
Suitability of AI
A path to technical performance
Suitability of data
Value realisation
Management commerciality
Quantifiability of ROI
Buyer readiness
Benign regulation
Deployment scalability
Defensibility
Distance from monoliths
Domain complexity
Data network effects
Proprietary algorithms
AI talent
Strong capitalisation
16 success factors for applied AI startups
Source: MMC Ventures
Source: MMC Ventures
Fig. 54. 16 success factors for applied AI startups
Value release
Value potential
Value realisation
Defensibility
Management commerciality
Distance from monoliths
Deployment
scalability
Benign
regulation
Buyer
readiness
Quantifiability
of ROI
Proprietary
algorithms
Strong
capitalisation
Domain
complexity
Data
network
effects
AI talent
Unattractive
alternatives
Suitability
of AI
Suitability
of data
Path to technical
performance
Success factors for applied AI companies
119
Source: Office Of National Statistics. Figures extrapolated from average weekly earnings, including bonuses.
Fig. 55. AI's value is greater when alternatives are expensive:
(average annual employee compensation by sector)
AI's value is greater when alternatives are expensive:
(average annual employee compensation by sector)
43,429
60,324
35,949
34,823
32,305
30,268
29,354
29,155
28,045
22,234
21,597
21,112
20,176
16,592
12,896
Finance & Insurance
IT & Communication
Professional Services
Utilities
Construction
Wholesale Trade
Transport
Public Administration
Real Estate Activities
Education
Health and Social Work
Administration
Arts, Entertainment and Recreation
Retail Trade and Repairs
Accommodation and Food Service
Value potential
AI companies with the potential to create significant outcomes
can be identified by: their potential to unlock value and disrupt
business systems; unattractive alternatives; the suitability of AI
to problems they address; a viable path to technical maturity;
and available data to train and deploy their AI systems.
1. Value release
Through predictive success or process automation, attractive
applied AI companies unlock value in business systems.
Investors can assess a supplier's ability to create revenue for
its customers by:
increasing uplifts in conversion, yield, throughput, price,
or other direct drivers of revenue;
reducing churn through improved customer experience,
stronger personalisation, enhanced customer service or
deeper brand loyalty;
enabling additional streams of revenue through the
identification of new customers, up-sell and cross-sell
opportunities, and markets.
Attractive suppliers can also decrease buyers' costs by:
reducing surplus spend, excess resourcing or personnel
requirements through improved predictive efficiency,
process efficiency or process automation;
reducing economic leakage through, for example,
improved compliance.
Companies that 'disrupt', enabling new categories of customer
to use a service, have the potential to create particularly large
outcomes. Companies that automate medical diagnosis, for
example, can deliver primary care at low cost. By making
healthcare affordable, a larger proportion of the global
population can access care, growing the market of healthcare
consumers. Few businesses disrupt and a business need
not disrupt to be attractive. By enlarging markets, however,
disruptive companies can create outsized outcomes.
2. Unattractive alternatives
Scope for value creation is greater when the cost or availability
of alternatives to AI are prohibitive. Typically, the alternative to
AI is investment in human or other resources. When alternatives
are costly, scarce, inaccessible or non-scalable, scope for value
creation is significant.
Human labour is typically the immediate and most expensive
alternative to digitisation. In the UK, more AI startups focus
on the finance sector than any other. Activity in general IT and
Infrastructure is also extensive. finance, IT and utilities are three
sectors where average salaries are the highest (fig. 55). There
are additional opportunities for AI companies focused on
professional services.
120
3. Suitability of AI
Investors can assess the extent to which AI is suited to a
business challenge. AI is well suited to problems that are
arduous, complex or inscrutable.
Arduous problems are those in which people are competent
and could codify a solution into a program, but it is impractical
to do so.
Complex problems are those in which people are
competent, but codifying capability into a program is difficult.
Recognising objects in images is a complex problem.
Inscrutable problems are those in which people are not
competent. People cannot label or organise data to underpin
a predictive engine. Deep learning approaches to AI excel at
inscrutable problems because neural networks can determine
parameters to optimise.
AI is poorly suited to unbounded problems and questions
of causal inference.
Unbounded problems: AI algorithms cannot draw on
knowledge beyond the data provided to them. For AI to be
effective, problems need to be adequately described by the
available data.
Questions of causal inference: AI is rarely suitable when
causal inference is of primary interest. AI describes how data
relate to one another, not the causal mechanism of their
relationship. AI is poorly suited to prediction problems when
the future will be dissimilar to the past, or where prior patterns
will not reflect a new reality.
4. A path to technical performance
AI need not be 100% effective to be valuable. AI solutions
typically need to offer only near-human, or ideally better-
than-human, levels of performance to enable automation and
process scaling. Investors should look beyond the immediate-
term and assess whether a company has a path to a level of
technical performance that unlocks value.
5. Suitability of data
For AI to create value, it needs suitable data sets on which to
be trained and deployed. Investors should evaluate the extent
to which a company can access suitable data. This can be
gauged in the context of two stages of data manipulation
required for AI:
Data selection: data availability; the existence of gaps and
duplicate data; quality of data labelling; existence of bias
in data.
Data processing: data fragmentation; data cleaning
requirements; the need for data sampling; the need for data
transformation, decomposition and aggregation.
Investors should also consider whether data sets will retain
value. Data sets retain value if new iterations of algorithms can
be tested, and improved, using historical data. This may not be
possible. A chatbot provider's new algorithm may change the
prompt shown to a user in a particular situation. A new prompt
will trigger a different reply from a user. A large set of historic
user replies therefore become decoupled, and potentially
irrelevant, to the algorithm being trained.
Value realisation
Successful companies realise their potential for value creation.
Five factors are key for value realisation: management
commerciality; quantifiability of ROI; buyer readiness; benign
regulation; and deployment scalability.
1. Management commerciality
Many founders of AI companies have outstanding technical
expertise. Commercial acumen, however, will significantly
influence the success of their businesses. Most business-to-
business software suppliers will require a direct sales team.
Commercial founders will demonstrate a desire to build a
large business, an urgency to go to market and the ability to
build strong sales teams either directly or by appointing
experienced leaders.
2. Quantifiability of ROI
B2B companies that offer a quantifiable ROI enjoy greater
adoption, benefit from shorter sales cycles and require less
customer education. In select functions, such as sales and
marketing, and sectors including finance, increased conversion
or profits are apparent. In others, such as the human resources
function, ROI must be articulated and linked with departmental
performance indicators.
Chapter 10
An investment framework for AI
121
Investors should assess
whether buyers have the
organisational buy-in to
augment or disrupt existing
workflows with AI.
3. Buyer readiness
Buyer readiness is conceived as a funnel with five stages:
awareness, knowledge, liking, conviction and purchase.
When evaluating buyers' readiness for an AI solution,
investors may wish:
to add to the funnel 'preparedness'. Preparedness is an
assessment of whether buyers possess suitable, accessible data
sets for training and deploying AI systems, and whether buyers
have the organisational buy-in to augment or disrupt existing
workflows with AI.
to consider issues of trust and control. Trust is the ability
to have confidence in the performance of an AI solution
with limited human intervention. Levels of trust required vary
by sector. Travelling in an autonomous vehicle, or using an
automated medical diagnostic tool, require high levels of trust.
Control describes the degree of desire for human involvement
in systems even when trust is high. Where high levels of human
control must be maintained, value release and scalability can
be inhibited.
4. Benign regulation
Deep learning approaches to AI, involving artificial neural
networks, are frequently 'black box' in nature. "It's not always
clear what happens inside you let the network organise
itself, butit doesn't necessarily tell you how it did it."
(Nils Lenke, Nuance).
Investors must consider whether an AI supplier may face
regulatory challenges regarding explainability. Is there a
requirement to understand or explain the basis for a prediction
or decision? If so, can the supplier adequately respond?
It has been reported that the European Union's General Data
Protection Regulation (GDPR), due to become EU law in 2018,
creates a 'right to an explanation', whereby customers can
demand explanations of algorithmic decisions. The legislation
is unclear. GDPR may only require companies to describe
general processes for algorithmic decision-making and the
data sets involved. Nonetheless, the direction of policy-making
is clear, and towards greater consideration of transparency and
bias in AI systems.
In some business-to-business functions (sales, marketing and
business intelligence) explainability may be of secondary
importance. In others including human resources, compliance,
and fraud detection, explainability may be essential given legal
or pragmatic considerations. Similarly, companies operating
in certain sectors, such as financial services, may have greater
compliance requirements than others.
5. Deployment scalability
The pace at which AI companies scale can be inhibited
by challenging deployment dynamics. Data integration
requirements can be extensive. Amalgamating, integrating
and cleansing disparate customer data sets can limit time
to value. Resources required from AI vendors can also be
substantial, limiting capacity for new customer acquisition and
margins. Many early stage AI companies have one third of their
modestly-sized teams involved in deployment (MMC Ventures).
"Given the personnel
required for each
deployment, we couldn't
deal with more sales
even if we had them."
CEO, early stage AI company
AI companies that minimise deployment requirements,
or automate data processing and deployment, can scale
more rapidly.
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An investment framework for AI
Defensibility
How effectively can an AI company defend the value it creates
against competitors? Investors should consider six keys to
defensibility: distance from AI monoliths; domain complexity;
scope for data network effects; proprietary algorithms; AI
talent; and strong capitalisation.
1. Distance from AI monoliths
Google, Amazon, IBM and Microsoft (GAIM) offer cloud-
based AI services in areas including computer vision and
language. The capability and scope of these services will
continue to increase. In 2017 Google extended its computer
vision capabilities into video, offering entity recognition, search
and cataloguing of video. Google's ownership of DeepMind,
effectively Google's AI research department, will enable novel
technologies to filter down into broadly accessible services.
The performance and low cost of GAIM's AI services will
commoditise all but the most sophisticated 'best-of-breed'
competitors in equivalent areas.
Accordingly, AI companies with offerings distant from GAIM
competencies will enjoy greater defensibility. Companies
gain distance from GAIM primarily by developing solutions
for specific business functions and industry verticals, and
technologies in areas beyond generalised computer vision
and language.
At present, GAIM's vertical ambitions are limited primarily to
healthcare (Google, Microsoft and IBM) and transportation
(Google), although in future some will experiment with
vertical offerings in sectors relevant to their data sets and
business models.
Early stage AI companies have the opportunity to enhance,
and then reinvent, myriad processes in sectors ranging from
manufacturing to law and agriculture. In these areas, GAIM
lack the desire, data and domain expertise to compete at
full strength.
2. Domain complexity
While focusing on an industry vertical or business function
minimises competition from AI platforms, the dynamics of an
AI company's chosen domain further broaden or narrow the
'moat' around the business.

Complex domains include those that require extensive industry
expertise, have elaborate regulation, or present particularly
complex technical or go-to-market challenges. Tackling a
complex domain places a greater burden on a business; the
bridge across the moat is narrower. Companies that survive,
however, will enjoy greater defensibility. Companies that tackle
complex domains, and have the capabilities to succeed, enjoy
strong defensibility.
3. Data network effects
Attractive AI businesses create network effects through data,
to develop lasting competitive advantage.
Companies with access to private, domain-specific data sets
have unique training materials to improve their AI algorithms.
A network effect develops. The more data secured, the better
the company's product. With a better product, the company
wins more customers at the expense of competitors. New
customers bring additional data, fuelling a virtuous circle.
Companies with access to
private, domain-specific data
sets have unique training
materials to improve their
AI algorithms. The more
data secured, the better the
company's product.
An AI company offering fraud detection for the financial
services industry will gain access to additional, non-public
data with every customer acquired. A company leveraging
only public data, such as web data, cannot develop the same
defensibility. It may achieve competitive advantage by
moving first, or scaling faster, but ultimately its algorithms
will be replicable.
An AI company need only access and utilise not own a
customer's private data to train its algorithms. Temporary
access to incumbents' data is sufficient to neutralise much
of the incumbent's own data advantage.
Scope for network effects, through access to private data, has
second-order consequences. Early stage AI companies may
sensibly prioritise access to date over short-term revenue. They
may offer software without charge, or accept reduced revenue
from initial customers, given the value of early customers' data.
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4. Proprietary algorithms
While ever-better algorithms are available from open
source libraries including TensorFlow, valuable AI
companies create intellectual property by developing
enhanced, proprietary algorithms.
A proprietary algorithm (in practice, frequently an ensemble
of multiple algorithms) may offer: greater accuracy; broader
functionality; faster performance; lower fragility; greater
explainability; or results from a smaller training data set.
Innovation comes by degree, from 'know-how' to novelty.
'Know-how' is the skilful implementation of existing algorithms
for improved results. Novelty involves devising new
approaches to problems and deploying them successfully.
The value of proprietary algorithms is underappreciated
("in AI, all the value is in the data"). In several areas of AI,
including natural language processing, lack of data is no
longer the primary inhibitor. In other areas, innovative
algorithms are enabling high quality results from smaller
data sets, and greater explainability.
5. AI talent
High quality AI talent is scarce and expensive. Open positions
for even general data scientists grew 32% year-on-year in
1H16 (Procorre), outstripping growth in supply. Among UK
developers, AI specialists also command the highest salaries
(fig. 56).
Compelling companies demonstrate their ability to attract
and retain high quality AI personnel at acceptable cost.
Early stage companies primarily compete with Google,
Amazon, IBM, Microsoft, and leading consumer companies
including Facebook, for top AI talent. Startups cannot, and
need not, compete with the scale, security and pay offered
by large companies. Effective startups market to candidates a
greater opportunity to impact product; increased autonomy;
faster cycles of innovation; greater freedom to publish;
intellectual and technical challenges; and greater potential
long-term financial rewards.
6. Strong capitalisation
AI companies have greater capital requirements given: the
longer period of time required to develop a minimally viable
product; long sales cycles associated with business-to-business
sales; the cost of AI talent relative to other developers;
and the requirement for extensive deployment resources,
including personnel.
Effective AI companies use capital as a weapon to strengthen
competitive advantage. While tackling inefficiencies for
example, by automating deployment leading founders
adequately capitalise their companies to withstand the journey
to monetisation, offer competitive salaries to AI talent, and
maximise their pace of customer acquisition to secure private
data sets and a data network effect.
Source: Stack Overflow Developer Hiring Landscape Report
Fig. 56. AI talent is expensive (average annual salary by technical area)
Average annual slary by technical salry
52,286
56,851
49,587
49,054
46,131
44,763
42,717
42,405
42,313
42,223
41,466
40,209
39,832
35,863
Machine learning specialist
Other
Developer with stats/maths background
DevOps specialist
Data scientist
Quality assurance engineer
Web developer
Embedded apps/devices developer
Desktop apps developer
Mobile developer
Systems administrator
Database administrator
Graphics programming
Graphic designer
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The State of AI 2017
Inflection Point