DFIN + eBrevia: A case study of automation in data extraction and contract analytics

AI & Machine Learning Landscape

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.

Dealmaking Landscape

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.

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DFIN + eBrevia: A case study of automation in data extraction and contract analytics

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