The top trends in
tech—executive
summary download
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The top trends in tech: Introduction
In the next decade, we’ll experience more progress than in the past 100 years combined, as technology reshapes health
and materials sciences, energy, transportation, and a wide range of other industries and domains. The implications for
corporations are broad. In the following charts—and in the related interactive—we bring to bear a unique methodology for
sorting out the technology trends that matter most for companies and executives.
Unifying and underlying all these trends is the combinatorial effect of massively faster computation, which is propelling new
convergences between technologies, startling breakthroughs in health and materials sciences, astonishing new product
and service functionalities, and an irresistible foundation for the reinvention of companies, markets, industries, and sectors.
Will your organization make the most of these trends to pursue new heights of rapid innovation, improved performance,
and significant achievement? Start by looking through the executive-summary charts that follow—and don’t forget to
explore the more detailed research you’ll find in our related interactive, “The top trends in tech.”
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10.0
100.0
1000
1650
1700
1750
1800
1850
1900
1950
2000
2050
0.1
1.0
Second
Industrial
Revolution
First
Industrial
Revolution
Advancing technology has always spurred
economic development, and now it’s
accelerating even faster.
Technological
advancements
Efficient
steam engine
1769
Internal
combustion
engine
1867
Internet
1970s
China
Germany
Great Britain
World
“In the next
decade, we will
experience more
progress than in
the past 100 years.”
–Peter Diamandis,
Cofounder of Singularity University
1. Estimated global GDP per capita in USD, adjusted to GDP in 1000 AD = 1; not exhaustive; 2. Includes Industry 4.0 (debate exists as to whether Industry 4.0 is
seen as the Fourth Industrial Revolution or simply as the second phase of the Third Industrial Revolution).
Source: Angus Maddison, “Statistics on World Population, GDP & Per Capita GDP, 1-2008 AD,” Maddison Project Database; UBS Asset Management; OECD
Third
Industrial
Revolution2
Fourth
Industrial
Revolution
accelerated
10× by
technological
advancements
Changes in GDP per capita brought about by technological investments, 1000–
2000 AD, by country, indexed1
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High technology-company valuations help fuel rapid growth in tech.
Change in market valuation, 2008–20, by industry,1 %
Source: Bloomberg; S&P
15
11
2008
14
09
10
12
13
16
17
18
19
2020
0
5
10
15
20
25
30
Information Technology
Health
Real estate2
Materials
Energy
1. Top two and bottom three S&P 500 sectors by member weighting; as of end 2020. 2. The real estate sector joined the S&P500 in September, 2016.
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We prioritized more than 40 individual technologies by technical
maturity, industry impact, and momentum.
Cross-cutting
technologies
Multiple
industries or
horizontals
Niche
Industry
applicability
II
III
x Prioritization dimensions
Focus technologies
Industry adoption
Technical maturity
Market-entry stage
Fundamental research
Carbon-neutral energy generation
Smart distribution/metering
Quantum hardware
Biomolecules/-omics
Knowledge graphs
Open Process
Automation
systems
Software
2.0/engineering
analytics
I
High momentum
Medium momentum
Low momentum
Nanomaterials
Quantum computing
5G/
connectivity
Battery/battery storage
Cloud computing
Edge computing
Computer vision
Deep learning
Speech technology and NLP1
Augmented analytics
Supervised classical machine learning
Zero-trust security/cybersecurity
Blockchain
Digital twins
RPA
Robots/cobots2/
RPA3
Autonomous things
Reinforcement
learning*
Industrial IoT5
3-D/4-D printing
VR, AR, MR6
Synthetic data
Hyperscale
data centers
Vertical SaaS4
apps
Neuromorphic hardware
Explainable AI
Biomachines
Cyberphysical systems
Smart spaces
Generative methods
1. Natural-language processing. 2. Collaborative robots. 3. Robotic process automation. 4. Software as a service. 5. Internet of Things. 6. Virtual reality, augmented reality, mixed reality.
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1. Internet of things. 2. Collaborative robots. 3. Robotic process automation. 4. Application-specific integrated circuits.
Industry-specific trends
1
Next-level process
automation…
… and process
virtualization
2
Future of
connectivity
3 Distributed
infrastructure
Industrial IoT1
Robots/cobots2/RPA3
Digital twins
3-D/4-D printing
5G and IoT
connectivity
Cloud and edge
computing
4
5
6
7
Next-generation
computing
Applied AI
Future of
programming
Trust architecture
Software 2.0
Quantum computing
Neuromorphic chips
(ASICs4)
Zero-trust security
Blockchain
Industry-agnostic trends
Computer vision,
natural-language
processing, and
speech technology
Technology trends and underlying technologies
Biomolecules/“-omics”/
biosystems
Biomachines/biocomputing/aug
mentation
Future of clean technologies
Nuclear fusion
Smart distribution/metering
Battery/battery storage
Carbon-neutral energy
generation
Bio Revolution
8
Next-generation materials
Nanomaterials, graphene and
2-D materials, molybdenum
disulfide nanoparticles
9
10
We distilled seven cross-industry and three industry-specific trends
based on prioritized technologies…
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…that could reshape the future of markets and industries in the
next few decades.
Future of
connectivity
2
Distributed
infrastructure
3
Next-level
process auto-
mation and
virtualization
50%
of today’s work
activities could be
automated by 2025
1
Next-generation
computing
4
Trust architecture
~10%
of global GDP could be
associated with
blockchain by 2027
7
Applied AI
>75%
of all digital-service touch
points (eg, voice
assistants) will see
improved usability,
enriched personalization,
and increased conversion
5
Future of
programming
~30×
reduction in the
working time required
for software
development and
analytics
6
Up to 80%
of global
population could
be reached by 5G
coverage by 2030
>$1 trillion
value potential of
quantum-computing use
cases at full scale by 2035
>75%
of enterprise-generated
data will be processed
by edge or cloud
computing by 2025
Bio Revolution
45×
cost reduction for
sequencing the human
genome has been achieved
in the past 10 years
8
9
Next-generation
materials
10×
growth in number
of patents between
2008 and 2018
10
Future of clean
technologies
>75%
of global energy will be
produced by
renewables in 2050
Effects of technology trends up to 2050
Source: McKinsey analysis
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Seven cross-industry technology trends will disrupt company
strategy, organization, and operations...
Tech-trend clusters
Disruptions
1 A. Next-level
process
automation
Industrial IoT1
Robots/cobots2/
RPA3
Self-learning, reconfigurable robots will drive automation of physical processes beyond routine
activities to include less predictable ones, leading to fewer people working in these activities and a
reconfiguration of the workforce; policy makers will be challenged to address labor
displacement, even as organizations will need to rethink the future of work
Digital twins
3-D/4-D printing
B. Process
virtualization
Advanced simulations and 3-D/4-D printing will virtualize and dematerialize processes,
shortening development cycles as ever-shorter product and service life cycles continue to
accelerate, further pressuring profit pools and speeding strategic and operational practices
that tightly correlate with successful digital efforts
5G and IoT
connectivity
Future of
connectivity
With 5G reaching up to 80% of the global population by 2030, enhanced coverage and speed of
connections across long and short distances will enable new services (eg, remote surgeries),
business models (eg, connected services), and next-generation customer experiences (eg,
live VR)
2
Wide availability of IT infrastructure and services through cloud computing could shift demand
for on-premise IT infrastructure and reduce the need for IT setup and maintenance, while
the democratization of infrastructure will help shift competitive advantage away from IT to
software development and talent.
3 Distributed
infrastructure
Cloud & edge
computing
1. Internet of things. 2. Collaborative robots. 3. Robotic process automation.
Disruptions across 7 cross-industry trends
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Seven cross-industry technology trends will disrupt company
strategy, organization, and operations... (continued)
4. Application-specific integrated circuits.
5. DataOps supports and enables better data analytics; MLOps combines infrastructure, tools, and workflows to provide faster and more reliable machine-learning pipelines.
4
Quantum
computing
ASICs4
Next-
generation
computing
High computational capabilities allow new use cases, such as molecule-level simulation,
reducing the empirical expertise and testing needed for a range of applications and leading to
the following: disruption across industries such as materials, chemicals, and pharmaceuticals;
highly personalized product developments, for instance in medicine; the ability to break the
majority of cryptographic security algorithms, disrupting today’s cybersecurity approaches;
and the faster diffusion of self-driving vehicles
6
Software 2.0
Future of
programming
Software 2.0 creates new ways of writing software and reduces complexity; however, as
companies look to scale their software-development capabilities, they will need to master
DataOps and MLOps5 practices and technology to make the most of the future of
programming
Zero-trust security
Blockchain
Trust
architecture
7
Trust architectures help commercial entities and individuals establish trust and conduct
business without need for intermediaries, even as zero-trust-security measures address
growing cyberattacks; countries and regulatory bodies may likely have to rethink regulatory
oversight; distributed-ledger technologies will reduce cost and enable transformative
business models
5
Computer vision,
natural-language
processing, and speech
technology
Applied AI
As AI matures and continues to scale, it will enable new applications (eg, more rapid
development cycles and detailed customer insights), eliminate labor for repetitive tasks (eg,
filing, document preparation, and indexing), and support the global reach of highly
specialized services and talent (eg, improved telemedicine and the ability of specialized
engineers to work on oil rigs from the safety of land)
Tech-trend clusters
Disruptions
Disruptions across 7 cross-industry trends
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…and three industry-specific technology trends can help solve
humanity’s biggest challenges.
Tech-trend clusters
Disruptions
8 Bio Revolution
Biomolecules/“-omics”/
Biosystems
Biomachines/biocomputing/
augmentation
“-omics” enable rapid analysis of genetic materials and open up
possibilities (eg, for rapid vaccine development, personalized medicine, and
gene therapy)
Using biological material for computing purposes can enable a vast
expansion of data storage using DNA as the information medium
By changing the economics of a wide range of products and services, next-
generation materials may change industry economics and reconfigure
companies within them (eg, by allowing for the integration of sustainable
materials and renewable energy sources into processes), even as
innovations in materials science help create smart materials with
programmable properties that respond to stimuli from external factors
Nanomaterials,
graphene and 2-D
materials, and
molybdenum disulfide
nanoparticles
Next-generation
materials
9
As clean technologies come down the cost curve, they become increasingly
disruptive to traditional business models, creating new business-building
opportunities, operational-improvement programs driven by clean
technologies, and new climate-change mandates that could alter the
balance sheet of carbon-intense sectors—all while providing the green
energy needed to sustain exponential technology growth
10 Future of clean
technologies
Nuclear fusion
Smart distribution/metering
Battery/battery storage
Carbon-neutral energy generation
Disruptions across 3 cross-industry trends
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The combinatorial effect of technology amplifies and accelerates
new business models and innovation…
Mutually reinforcing technology leads to exponential growth.
Enabler
Application
Infrastructure and
architecture
Software 2.0 (including MLOps
and DataOps)
6
Future of
programming
IIoT,3 Robots/cobots4/ RPA,5
Digital twins, 3-D/4-D printing
Next-level process
automation and
virtualization
1
5G and IoT1 connectivity
2
Future of connectivity
Quantum computing
Neuromorphic computing
(ASICs2)
4
Next-generation
computing
Computer vision, natural-
language processing, and
speech technology
5
Applied AI
Cloud and edge computing
3
Distributed
Infrastructure
Zero-trust security
Blockchain
7
Trust
architecture
New business
models and
innovation
New programming
modalities to achieve robust
models and build
applications faster (eg,
MLOps, federated learning)
Novel architecture
paradigms focusing on
orchestration of
infrastructure, increasing
resilience, flexibility, and
speed (e.g., decoupled,
microservice-based)
Dedicated hardware
delivering increased
computing power, diffused
through new levels of
connectivity (eg, edge-based
processing or 5G)
Outcomes of 3 levels of combinatorial effects on cross-industry tech trends
1. Internet of things. 2. Application-specific integrated circuits. 3. Industrial Internet of Things. 4. Collaborative robots. 5. Robotic process automation.
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…changing the industry landscape by disrupting the status quo and
creating new opportunities.
Combination of relevant tech trends will have far-reaching impact across the industry
New business
models, products,
and services
Automation and
productivity
transformations
across value chain
Next-generation
customer experience
Transformation in
product/research
development
Search potential “biological space” with machine-learning digital simulations of
molecular properties; develop novel and sustainable materials
Novel-risk scoring and claims processing in insurance using blended data from
multiple new sources (including computer vision over satellites); chat bots to
handle customer acquisition and claims, without humans
New business model in agriculture where underground soil probes monitor
temperature and moisture, then relay data back to server every 15 minutes over
cellular network; data are used to improve yield, develop fertilization plan,and
optimize irrigation
“Grid sharing” technology to create a virtual power plant1 powered by tens of
thousands of EV batteries, where cloud platform manages individual batteries
and AI system manages loads across them
Seamless customer experience in the “retail store of the future,” which gathers
and connects data, including RFID,2 keeping an eagle eye on replenishment and
providing data-lake-enabling analytics
Use AI to empower credit-card sales team; the sales-advisory tool determines
the best product for the customer
1
Next-level process
automation and virtualization
5
Applied AI
5
Applied AI
2
Future of connectivity
2
Future of connectivity
1
Next-level process
automation and virtualization
3
Distributed infrastructure
5
Applied AI
6
Future of programming
1
Next-level process
automation and virtualization
4
Next-generation computing
Relevant trends
Disruptions
Cross-industry horizontal
Example trends and disruptions across industry horizontals
1. A virtual power plant is a cloud-based distributed power plant, in which power from distributed energy resources (eg, solar power from individual households) is stored in batteries and can be
distributed in the grid. 2. Radio-frequency identification.
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Tech trends affect all sectors, but their impact varies by industry.
Next-level process
automation
Next-generation materials
Applied AI
Future of clean technologies
Future of connectivity
Bio Revolution
Next-generation computing
Trust architecture
Distributed infrastructure
Future of programming
1. Based on the average impact across different industries.
Enabler sector
Mobility sector
Healthcare sector
Industry 4.0 sector
Pharma-
ceuticals
Health
Transport and
logistics
Automotive
Advanced
industries
Chemicals
Electronics
Information
Telecom-
munications
Source: Expert interviews; McKinsey analysis
Major influence
Moderate influence
Limited influence
Estimated effects of tech trends across select sectors
1
9
5
10
2
8
4
7
3
6
Tech trend
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Executives must think through three primary questions as they
consider where and when to invest, while getting the timing right.
Mature
High
growth
Nascent
How fast do you need to react?
Is it the right time to scale any of the
technologies given their stage and
speed of maturity?
Technical maturity
Poor
Good
Good
(sustaining
innovation)
Poor
(disruptive
innovation)
Fit with organization’s
business model
Fit with organization’s capabilitiesHow do you approach the technology
implementation?
How should you operationalize
technologies to capture value?
Fit with the organization
How important is this trend for a given
industry or company?
Will this technology fundamentally disrupt
existing value pools?
Which technologies matter most for any
given company?
Will implementing these technologies
give the company a competitive
advantage?
Scale of impact
Evolution
Transformation
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Front runners in tech adoption capture the most benefit.
Relative changes in cash flow, Percent change per cohort, cumulative
82
122
135
77
Output
gain/loss
from/to peers
Economy-wide
output gains
18
Capital
expenditures
Transition
costs
Total
11
-23
19
Economy-wide
output gains
49
Output
gain/loss
from/to peers
4
Transition
costs
Capital
expenditures
Total
Front-runner breakdown,
Percent change
Laggard breakdown,
Percent change
2030
20
18
2017
19
-50
25
21
27
22
23
24
26
28
29
0
50
100
150
Front runners (adopting
between 2017–22)
Laggards
(do not adopt by 2030)
Followers
(adopting by 2030)
Note: Numbers are simulated figures to provide directional perspectives rather than forecast.
Source: McKinsey Global Institute analysis
Economic gains by AI-adoption cohort, front runners, followers, and laggards (simulation)
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As you implement new technologies, pay attention to fit with
business model and organizational capabilities…
Archetypes of technology implementation
Description
Forming a rollout plan for the
entire company with
widespread ownership—often
with different degrees of
customizations, such as road-
map and training-program
alterations, depending on the
nature of the technology
Establishing, growing, or
launching initiatives with a
more radical character to the
core business within the
context of a stand-alone entity;
the entity is still feeding of the
resources from the mother
organization and can be built,
acquired, or a joint venture
Forming a special unit to
execute projects with a
powerful team, preferably
composed of individuals
from both within and
outside the existing
organization
Creating a centralized way of
providing the organization with
a specific technology
Criteria for
selection
Having a company-wide effect
early in the implementation
process
Existing culture and processes
can handle the change, and
the technology is easy to
understand
Is seen as a hygiene factor in
terms of customer requirement
Easy to recruit the needed
capability
Nature of the technology is
disruptive/radically innovative
from current products/service
offerings
Processes required differ from
those of the existing
operations
Cultural requirements differ
from those the existing
operations
Good access to capital
Technological maturity of
the organization is
generally low, and it
belongs to a traditional
industry
The resistance towards
change is relatively high
Experimentation and
iteration required
Scarcity of talent
Important to the core
business offering
No specific development
needed
Mid level complexity and
maintenance
Technology is possible to
distribute and control from a
central unit
Privacy and security
regulations might have a
severe effect
Ownership
Each business-unit boss
CEO of the separate
unit/company
CEO
Chief technology officer/head
of IT
I Lightweight or
functional team in
organization
III Heavyweight
team in separate
entity
Heavyweight
team within
organization
II
IV Development within
organization,
commercialization
in separate entity
Poor
Good
Good
(sustaining innovation)
Poor
(disruptive innovation)
Fit with organization’s business model
Fit with organization’s capabilitiesB
C
A
D
Use a heavyweight team
within the existing
organization
Use a heavyweight team
in a separate spinout
organization
Use a lightweight or
functional team within
the existing
organization
Development may occur in-house
through a heavyweight team, but
commercialization almost always
requires a spinout
III
IV
II
I
Ecosystems can complement these strategies and help
speed up technology adoption
Collaboration with other players reduces risks and increases breadth of
technology adoption
Ecosystems help companies access talent and technologies in the
market at a faster speed
Taking the current organization into consideration increases the likelihood of success.
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…while taking into account five primary areas of risk.
Source: McKinsey analysis
Compliance
Ensure compliance with a
data-driven culture,
regulations, and internal
policies
Policy design in accordance
with regulatory requirements
Information, education, and
advice for business on
regulatory and policy
requirements
Continuous surveys and
monitoring of activities and
reporting/managing of
incidents due to
noncompliance
Legal
Proactively advise
business lines and rest of
organization with legal
matters
Expert advice in
transactions
External counsel
Advice on general internal-
management issues
requiring legal expertise,
(eg, data-access
restrictions)
Operational risk
Establish robustness of
processes and control
and mitigate operational
risk
Monitoring of processes
and controls to
operationalize data-ethics
codes
Measuring and prioritizing
operational risks
Leading enterprise-wide
activities to reduce risk
through an appropriate
data-driven culture (eg,
avoid biases)
Business
Taking primary
responsibility for
soundness and application
of data ethics and
maintaining a data-driven
culture
Developing appropriate
processes and control
Adhering to regulatory and
policy requirements
Fostering a culture of data-
driven decision making and
ethics as an enabler and not
as an inhibitor of business-
value creation
Society
Safeguarding of societal
values from business
actions and maintaining
internal awareness about
societal duty of organization
Engaging actively in societal
development and local
communities
Proactively waterproofing
business actions to be in line
with societal norms and
promoting inclusion
Openly embracing diversity
5 areas of risk for new technology implementation
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About the research
This research examines a range of factors to identify the technology trends that matter most to top executives and the companies they lead.
For every trend, we calculated a momentum score based on the growth rate the technologies underlying the trends, which we derived from
an in-depth analysis of six proxy metrics: patent filings, publications, news mentions, online search trends, private-investment amount, and
the number of companies making investments. We then rolled the scores of the underlying technologies into a single composite score for the
trend itself. Comparing composite momentum scores will help executives recognize how much disruption a trend is likely to cause and how
soon that disruption will have business implications.
The underlying metrics are diverse, the better to account for the varied perspectives each represents. The number of research publications
within a field provides a leading indicator of trends as they emerge. Patent filings give a measure of the importance placed on a particular
trend by corporations. The quantity of private investment, as well as the number of companies making investments, indicates whether a clear
financial interest exists for a specific trend. Finally, search trends and news coverage reveal the level of public interest in a trend. Combining
early indicators with measures of public and financial interest creates a holistic view of each trend and provides a good way to rank and
compare their potential impact. Using the growth rate as the basis of the momentum score differentiates areas that are merely large from
those that are on their way to massive.
Finally, we syndicated our analytical results with external experts on McKinsey’s Technology Council, leading to a unique perspective that
combines research analysis with qualitative insights from some of the leading thinkers of our time.