AI & MACHINE LEARNING LANDSCAPE
DFIN + eBrevia: A case study of automation in
data extraction and contract analytics
Data provided by
DFINsolutions.com
2 AI & MACHINE LEARNING L ANDSC APE
Executive Summary
Contents
Executive Summary
2
Industry
Developments
3
Q&A
4-5
AI & ML
Landscape
6-7
Dealmaking
Landscape
8-10
About Us
11
"DFIN had a lot of
foresight and now
has taken a head
start in this area."
N ED G A N N ON
PR E S ID E NT, E B R E V I A
The pace of U.S. M&A activity in artificial intelligence & machine learning (AI &
ML) ramped up to record levels last year, soaring at a near linear rate since 2016
to 145 completed deals while value eclipsed the prior high of $8.5 billion to close
on $21.3 billion. As recently as 2015, just 27 transactions closed on $2 billion in
aggregate. The steep uptick in both value and volume follows several years of
increased venture investment into the space.
Propelled by the need to stay on top of technological innovation, buying effective
startups over building bespoke internal systems has quickly emerged as a
preferred approach for sponsors and strategics alike looking to add AI- & ML-
enabled capabilities to their platforms. Since the start of 2008, half of all acquired
AI & ML companies in the U.S. had VC backing. With few exceptions, the others
had raised no institutional capital prior to purchase.
Dealmakers expect improved automation to drive down the time taken to conduct
due diligence while improving accuracy and controlling costs. As M&A strategies
shift in scope to capture emerging tech verticals, contract analytics could quickly
become indispensable to the review process for buyers that need to add new
capacities outside of their core competencies. That dynamic has the global
market for AI & ML poised to grow by 150% this year alone. Analysts estimate the
market will reach $191 billion in value by 2025 driven primarily by solutions for
the enterprise.
AI & MACHINE LEARNING L ANDSC APE 3
Industry Developments
37% CAGR
The global AI & ML market
is expected to expand at a
compound annual rate of 36.6%
from $21.5 billion to $191 billion
between 2018 and 2025. The use
of AI & ML for the enterprise
will fuel this growth, with
manufacturing poised to lead
the way as the Industrial IoT
comes online replete with the
cybersecurity challenges it poses.
Cybersecurity
A growing area of application for
AI & ML is cybersecurity defense.
Firms can continually monitor
a wider range of potentially
malicious behaviors and close
the gap between detection and
response with intelligent threat
detection enabled by automation
and deep learning.
Big Tech
The most active strategic
acquirers in the AI & ML space
represent the biggest of Big Tech.
Companies like Alphabet, Amazon
and Apple regularly purchase
promising startups, frequently
before they have raised any
institutional capital.
Tasks, not jobs
While some peg job replacement
figures at 40% from automation,
researchers from MIT's Sloan
School of Management anticipate
that AI & ML will more likely
eliminate a subset of tasks from
among the 20 to 32 that comprise
the 964 types of job the U.S. Labor
Department tracks.
$2B
The field of legal analytics is
projected to grow at a compound
annual rate of 32.7% from $451
million to $1.9 billion between
2017 and 2022. Although North
America appears poised to
lead that expansion, Europe is
anticipated to grow at a slightly
steeper clip.
$437B
Legal services in the U.S.
represents a $437 billion
market. With junior associates
charging anywhere from
$300 to $500 per hour to
run contract reviews during
due diligence, the legal fees
in M&A pile up quickly and
annually top $1.5 billion.
4 AI & MACHINE LEARNING L ANDSC APE
Q&A
Ned Gannon
President, eBrevia
As a co-founder and President of eBrevia, Ned Gannon drives the strategic direction of the company and is
responsible for overall management. eBrevia uses machine learning technology to improve the efficiency and ac-
curacy of contract review. Ned brings a broad range of legal and business experience to the company. While prac-
ticing corporate law with Paul, Hastings, Janofsky and Walker LLP and LeBoeuf, Lamb, Greene & MacRae LLP (n/k/a
Dewey & LeBoeuf LLP), Ned represented private equity funds, strategic investors, venture capital funds and startup
companies in mergers, acquisitions, financings and general commercial matters. Prior to practicing corporate law,
Ned worked in a sales and business development role at Survey Sampling, Inc., a privately-held data collection
company. Ned was selected by Capterra as one of five Legal Tech entrepreneurs to watch and speaks frequently
on machine learning technology's impact on the legal industry. Ned holds a Juris Doctor from Harvard Law School,
a Master in Public Administration from Harvard's Kennedy School of Government and a Bachelor of Science from
Boston College.
The combination of eBrevia and DFIN
emerged from an existing relationship
between the two companies. How did
that initial partnership take shape?
Gannon: It really came from both of us
selling to global law firm customers
simultaneously and encountering each
other in that space specifically. DFIN
was selling a new virtual data room
and eBrevia was selling our contract
analyzer software that focuses on
accelerating the due diligence process.
Venue is used very heavily in M&A, so
there were a lot of natural synergies
there given a common element in
our customer base, which has since
evolved and diversified quite a bit. In
addition to global law firms, we also
work frequently with three of the big
four consulting firms, corporate legal
departments, financial institutions,
legal process outsourcers, and
commercial real estate firms. DFIN also
has its own longstanding relationships
with many of these client types.
How did DFIN's role as an early
investor factor in as the relationship
developed over time and as the
partnership grew from those initial,
natural synergies?
Gannon: After we signed a partnership
sometime in the summer of 2015, we
got a chance to work closely with the
DFIN salesforce. As the relationship
continued to evolve, we reached a
point where we were going to be
raising another round of financing
and DFIN expressed interest in
participating. In the end, they led
the round in September 2016, which
further cemented the connections
between the companies. From there,
we did some additional training of
their salesforce and started to focus
more on the integration of some of
the existing productssomething that
we're continuing to focus on today.
How has DFIN folded eBrevia into
Venue? Is it more of a discreet
component of an overall process?
How do you view the offering from a
product suite stand point?
Gannon: Our product will continue to
be sold on standalone basis, as it's
also an enterprise tool that doesn't
necessarily have to be used within
Venue. For a lot of consulting firms, law
firms, and corporate legal departments,
eBrevia makes sense on an enterprise
basis while on a transactional basis the
contract analytics tool complements
Venue's value proposition. In addition
to pulling data out of contracts with
what we call pretrained provisions,
nontechnical users can also train the
system themselves to extract custom
information to meet their specific
needs. For instance, a nontechnical
user at an energy company tasked with
extracting data from power purchase
agreements can train our analytics tool
to do it for them. As a result, eBrevia
is a really nice fit for the enterprise.
People can tailor it to meet their
specific needs. But another part of
that strategy is to help clients access
contracts and company data wherever
it might reside, whether that's in a
contract management system or, on a
transactional basis, perhaps the buyer
or seller have selected a data room
other than Venue, we still want to be
able to access that data. As a result, we
have worked with DFIN on integrations
with contract management systems
and other virtual data rooms as well.
When you've come back to existing
customers, how have you narrated
the combination with DFIN to them?
What questions have they asked? Is
there a lot of nuance to the offering or
their understanding or is there a lot of
teaching and explaining that goes on?
Gannon: Most understand that the
software can help to analyze contracts
more accurately and efficiently during
diligence, and there's no software
out there that can also store the
documents within the context of
a deal. Combining those functions
together in Venue creates a lot of
value for customers. Meanwhile, we
haven't found that we need to do a
tremendous amount of explaining of
how the products work in tandem.
People are already comfortable with
contract analytics. They see it, they've
AI & MACHINE LEARNING L ANDSC APE 5
heard about it, many have used it. The
same holds true of virtual data rooms
like Venue. We've reached a point
in the development of this market
where, in the grand scheme of things,
although it's still very early days yet
in terms of contract analytics and the
value that they can create, as products
like ours evolve, that will only increase.
Our customers have done a lot of
bespoke self-training, helping them
to create a lot of upsides from our
combination with DFIN for themselves.
We have very close relationships with
many of them and, in fact, some of
our best features have come directly
from client suggestions. In the past,
people might have requested we build
a particular function to help with
their workflow and it may have taken
us awhile to allocate the engineering
resources. Now, we're able to move
a lot faster as part of a much larger
entity with a lot of levers to pull and
that allows us to focus more on the fun
stuff for clients.
In addition to greater scale, what
are the most compelling drivers of
dealmaking in this space?
Gannon: When we first started
eBrevia, we partnered with Columbia
University's data sciences institute
back in 2012 and spun the company
out of the university. Back then, AI &
ML weren't very hot topics. In fact, we
had some early angel investors and
VCs that we pitched that tended to
shy away from us really because they
weren't convinced that AI could be
sophisticated enough to accurately
analyze contracts. Obviously, times
have very much changed. A lot of that
is driven by the enthusiasm of large
corporates for adopting this type of
technology, as they've really witnessed
firsthand the benefits for their own
work processes. As a result, that's
sparked a lot of interest from acquirers,
whether it's strategic acquirers looking
to complement their own products
or private equity funds. My general
sense is, though, that we're still in the
early days of AI-related acquisitions.
DFIN had a lot of foresight and now
has taken a head start in this area so
it can focus on deploying this type
of technology throughout its product
suite, and that'll be a gradual process
over time. Meanwhile, other folks who
haven't been quite as quick to move
are still looking at the market or are
trying to build something themselves.
That could place them a bit further
behind because building in this space
is also uniquely challenging.
How are you finding industry experts
classifying the various categories of
AI and machine learning relative to
analytics software overall?
Gannon: At this point in time, the
most successful AI & ML offerings are
the ones that complement human
expertise. That's very much how
eBrevia has been designed, namely,
to assist the attorney in their job
more accurately and efficiently. That's
also what folks should look at in an
acquisition. When you start talking
about companies that are saying, "Hey,
invest in us because we're going to
totally eliminate the need for, X, Y, Z," I
think that particularly with professional
positions, given the kinds of knowledge
bases required for them, that's
sometimes a bit overblown. There are
certain tasks that can obviously be
automated. And that's really where I
think the most successful AI companies
play and leave the higher-level creative
tasks to the humans.
So, you feel like there's still a solid
divide between what the human and
what the machine can provide?
Gannon: I think you'll continue to see
the machines moving up the value
chain. But for the foreseeable future
there's always going to be a very
significant need for the human to be
engaged in the higher-level tasks and
higher-level analysis.
6 AI & MACHINE LEARNING L ANDSC APE
AI & Machine Learning Landscape
The current expansion
of M&A activity in U.S.
AI & ML has targeted
startups that scaled
quickly following
several years of
increased venture
investment into the
space.
$5.7$3.5$7.4$8.5$7.4$21.316
13
13
13
29
27
49
96
146
2010
2011
2012
2013
2014
2015
2016
2017
2018
Deal value ($B)
Deal count
Acquirer Name
# of Acquisitions
Alphabet
8
Apple
7
Intel
6
Microsoft
5
Nuance
4
Amazon
4
Yahoo
4
Oracle
3
PTC
3
Salesforce
3
Facebook
3
Accenture
3
The dealmaking activity of tech giants like
Alphabet, Amazon and Apple illustrates
the central role played by M&A to maintain
a competitive edge in AI & ML.
Source: PitchBook
Top strategic acquirers of VC-backed AI
& ML companies, 2010-2018
U.S. M&A activty in AI & ML
19
combined deals for Alphabet,
Amazon, Apple
Investor Name
# of Investments
Vista Equity Partners
5
Insight Venture
Partners
3
The Blackstone Group
2
Lead Edge Capital
2
GCP Capital Partners
2
Warburg Pincus
2
Most active PE investors in AI & ML companies, 2010-2018
AI & MACHINE LEARNING L ANDSC APE 7
U.S. M&A activty (#) in AI & ML by target company backing status
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
2010 2011 2012 2013 2014 2015 2016 2017 2018
Acquisitions
Buyouts
0
20
40
60
80
100
120
140
160
2010
2011
2012
2013
2014
2015
2016
2017
2018
No backing
Publicly held
PE-backed
VC-backed
Source: PitchBook
Private equity interest
has crept up considerably
over the past decade,
with sponsored deals
representing 17.5% of all
transactions closed since
the start of 2008.
U.S. M&A activity (#) in AI & ML by deal type
8 AI & MACHINE LEARNING L ANDSC APE
Dealmaking Landscape
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
2010 2011
2012 2013 2014 2015 2016 2017
2018
5B+
1B-5B
500M-1B
250M-500M
100M-250M
Under 100M
Source: PitchBook
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
2010 2011
2012 2013 2014 2015 2016 2017 2018
$5B+
$1B-$5B
$500M-$1B
$250M-$500M
$100M-$250M
Under $100M
With headline grabbing achievements
to its credit, AI & ML have captured the
public's imagination. But dealmakers
have largely looked beyond the hype
generated by Watson's "Jeopardy!"
performance, focusing instead on
enterprise applications to fuel forms
of automation that can cut costs
and free employees to concentrate
on more advanced projects. "There
are certain tasks that can obviously
be automated. And that's really
where I think the most successful AI
companies play and leave the higher-
level creative tasks to the humans,"
says Ned Gannon, Co-Founder &
President of eBrevia.
Keeping pace with these
developments drove M&A activity in AI
& ML to record levels in the U.S. last
year, with 145 deals closing on $21.3
billion in value. Just 27 transactions
closed on $2 billion in aggregate only
three years earlier. But the types
of automation ushered in by AI &
ML can range from IT operations
to self-driving vehicles, with single
transactions capable of keeping
aggregate values up even if the
volume of disclosed deal sizes levels
off or drops. And it's not just strategics
driving M&A in AI & ML.
Last year, KKR completed its largest
acquisition since the global financial
crisis with the purchase of BMC
Software for $8.5 billion. The deal
accounted for $6.9 billion of the $7.4
billion in disclosed value that year,
which only registered 13 completed
transactions in AI & ML. It also
highlights the potential for value
creation that PE's buy-and-build
strategy can realize in this space.
U.S. M&A activity (#) in AI & ML by deal size
U.S. M&A activity ($M) in AI & ML by deal size
Source: PitchBook
AI & MACHINE LEARNING L ANDSC APE 9
Founded: 2011
Founders: Ned Gannon, President, Adam
Nguyen, SVP, and Jake Mundt, CTO
Venture capital raised: $4 million
Acquisition date: December 27, 2018
Deal size: $23 million
History: DFIN's purchase of eBrevia at
the end of 2018 not only capped the
busiest year on record for U.S. M&A
activity across the AI & ML space, it
also cemented a partnership that
first emerged from the pair selling
their complementary products to
global law firms. Two of eBrevia's
founders, Ned Gannon and Adam
Nguyen, graduated from Harvard Law
School and practiced commercial
law prior to launching the analytics
platform. eBrevia's third founder,
Jake Mundt, received his master's
degree in Computer Science from
Columbia University and previously
worked for a company extracting
data in the medical industry. In 2014,
they secured a $1.5 million round
that brought eBrevia's total seed
funding to $2.1 million raised from
investors including Connecticut
Innovations and Rothenberg Ventures.
DFIN + eBrevia Case Study
"For the foreseeable future, there's always going to be a very
significant need for the human to be engaged in the higher-level
tasks and higher-level analysis."
N ED G A N N ON , PR E S I DEN T, EB R E V I A
After entering a formal partnership
in 2015, DFIN's salesforce started to
train on eBrevia's analytics platform.
In 2016, DFIN led eBrevia's Series A
round, which brought in another $2
million in funding. It also signaled a
deepening of the pair's partnership,
with eBrevia's AI & ML tools becoming
more fully integrated with Venue.
The companies have been working
very closely together since 2015 and
that really goes to multiple levels of
the organization. The global capital
markets salesforce at DFIN has been
trained on the product. The Venue
salesforce has been demoing eBrevia
for at least 18 months now. In a lot of
ways, we have always felt like part of
the broader organization.
Ned Gannon, President of eBrevia
Product: Gannon, Nguyen and Mundt
created eBrevia based on AI & ML
licensed from Columbia University,
spinning the company out of the
university in 2012. The analytics
platform leverages NLP technology
to extract data from contracts. The
company's software identifies and
extracts legal provisions and other
data from legal documents regardless
of the vocabulary used or where the
information might be buried within the
document. In addition to pre-trained
concepts, users can also teach the
system to read and extract any number
of custom categories of information
from contracts to meet their specific
needs. Integration with Venue allows
eBrevia's Contract Analyzer to operate
within DFIN's virtual data room though
the software also continues to be sold
on a standalone basis. Parties on the
buy side of a deal, for instance, can use
eBrevia in Venue to identify and extract
all the change of control provisions
or assignment provisions in the target
company's contracts to prepare the
due diligence memo. In addition to
being used on the buy and sell side
of transactions, eBrevia's software is
also leveraged for compliance, audit/
consulting work, lease abstraction and
to enable corporations to identify and
track risks and obligations within their
company contracts.
10 AI & MACHINE LEARNING L ANDSC APE
Since 2Q 2016, no quarter for U.S. M&A
in AI & ML activity has fallen below
$1 billion in value. But the recent
YoY uptick in overall deal activity
really got its start in 3Q of that year,
which accounted for 10 completed
transactions representing $1.4 billion
in value.
With strategics under pressure to stay
abreast of innovation, the alternatives
to M&A in order to add AI- & ML-
enabled capabilities can represent
a losing proposition, as building a
bespoke system could comprise a
multiyear commitment to an uncertain
process for companies that risks
putting them behind acquisitive
competitors. The top dealmakers in
this space highlight the essential part
played by M&A to keep pace with
innovation and maintain a leading
position in the market.
The relative lack of talent in AI &
ML engineering can make this a
8
6
4
11
8
7
7
5
9
15
10
15
16
24
26
30
30
42
32
42
0
5
10
15
20
25
30
35
40
45
$0
$2
$4
$6
$8
$10
$12
1Q
2Q
3Q
4Q
1Q
2Q
3Q
4Q
1Q
2Q
3Q
4Q
1Q
2Q
3Q
4Q
1Q
2Q
3Q
4Q
2014
2015
2016
2017
2018
Deal value ($B)
Deal count
Source: PitchBook
U.S. median and average deal size ($M) in AI & ML
challenge, however. "The acqui-hire
piece is a big part of it. Who is the
team of the company you're looking
to acquire? Would they fit within your
company's culture? Could their skillset
be leveraged in other areas within
the organization as well as to further
product integration?" says Gannon.
As a consequence of acquisition
targets frequently comprising small
teams of highly specialized engineers,
though, deal sizes have generally
proliferated at the lower end of the
range. For years with more robust
deal value data disclosed, median
transaction sizes ranged between $43
million last year and $50 million in
2017 on 31 and 22 deals, respectively.
The precipitous rise of U.S. M&A
activity in AI & ML is still striking;
however, given the relative immaturity
of the AI & ML ecosystem alongside
continued investor interest and
strategic need, deal data over the past
decade could well pale in comparison
to future figures. The volatility of
transaction sizes to date also suggests
that valuation models and the process
of pricing AI & ML assets may remain
a challenge for some time to come.
"I would say you'd want to look at
companies that have an AI-based
product that complements an existing
product or service you have," Gannon
says.
$50M
median deal size in 2017
AI & MACHINE LEARNING L ANDSC APE 11
About Us
Donnelley Financial Solutions (DFIN)
DFIN is a leading global risk and compliance solutions company. We provide domain expertise, enterprise software and data
analytics for every stage of our clients' business and investment lifecycles. Markets fluctuate, regulations evolve, technology
advances, and through it all, DFIN delivers confidence with the right solutions in moments that matter.
Learn about DFIN's end-to-end risk and compliance solutions
Visit DFINsolutions.com | Call us +1 800 823 5304