How AI is democratizing M&A for buyers and investors

Technology has already lowered the barrier to entry for deal origination and screening. Bain has found that about one in five companies were already using generative AI in M&A in 2025, with more than half expecting to integrate it completely into dealmaking by 2027. KPMG’s 2025 M&A Pulse also found respondents expected AI to have its greatest impact on search and screen (as well as on integration and separation execution).

But with every new report, the proportions increase: Deloitte’s May 2026 cross-sector survey of the deal-doing C-suite put any use of AI in deals at 90%, with 37% saying it’s actively used across multiple stages of the M&A life cycle. 

In practice, that means more parties can build target lists, analyze patterns, compare deal precedents and prepare internal investment cases earlier than before.

But it also reduces the odds that any attractive approach will remain genuinely proprietary. If one private equity house has picked out five high-probability acquisitions by trawling publicly available data for targets, there’s a good chance others have, too. Equally, the cost to vendors of converting a proprietary approach into a contested auction is much lower: just search which acquirers – anywhere in the world – have been active in their vertical and widen the process.

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How AI is widening access to M&A

This ‘democratization’ of M&A has two implications. The first is that process credibility becomes a real competitive advantage. Buyers with clear playbooks, efficient diligence capabilities, a library of standard term sheets, and a reputation for execution may win, even if they are not the top bidder. Lawyers are a key part of that credibility. A buyer that can move quickly through confidentiality arrangements, information requests and draft documentation looks more executable than one whose advisers are improvising. 

The second is that confidentiality and data-use rules become more important as technology gets embedded into sourcing and diligence. For example, if there is an expectation that confidential information shared in diligence may be put through an AI tool for analysis, appropriate guardrails are vital; restrictions on such use are now appearing in NDAs. 

If buyers are using AI-enabled review tools, sellers and their advisers need to think carefully about where data is processed, whether it leaves secure environments, whether prompts are retained, and how confidential information is protected. Law firms increasingly have to advise not just on the NDA itself, but also on the permitted methods of analysis covered by it.

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Managing accuracy, confidentiality and liability in AI-assisted diligence

The use of AI in due diligence also raises liability and insurance questions for advisers. While AI can dramatically speed up diligence, W&I/reps and warranties insurers still want human oversight and will not simply accept a purely AI-generated diligence product. Without that cover, many deals will fail.

According to the International Bar Association, “The use of AI does not reduce a lawyer’s obligation to ensure accuracy. If humans define the objectives, it is equally the human’s duty to validate the accuracy of the results. It is key – and cannot be overstated – that firms establish robust verification protocols, continuous evaluation methods and human leadership: AI-generated output must be treated strictly as a preliminary starting point for analysis, and never as a finished work product.”

The same might be true of law firm professional indemnity cover if AI-enabled reports become more common. That matters for clients. The potential to deliver faster and more efficient legal processes may open smaller deals to more buyers, but only if the legal market, the insurance market and buyer governance processes are comfortable with the level of reliance being placed on AI-assisted outputs.

A key finding of PwC’s AI jobs report, issued in June 2026, was that far from killing off jobs and work in professional services, AI was increasing the demands on expert advisers by removing bottlenecks at the most basic level – the collating of information. 

“We’re beginning to see a new divide emerge between different models for talent and value creation,” said the firm’s Global Chief AI Officer, Joe Atkinson. “The companies seeing the greatest returns on AI are using it to amplify human expertise, accelerate innovation and create entirely new sources of value. As a result, they are pulling further ahead on productivity and growth than companies that focus primarily on automation.”

Interestingly, while the firm issued a familiar caution about AI limiting exposure to deal mechanics for junior professionals, we’re already seeing more firms use this opportunity to accelerate ‘senior skills’ development. 

Lazard CEO Peter Orszag told the Wall Street Journal in July that he thinks AI will enable “smaller deal teams, with fewer junior bankers under each managing director. We think that gives our junior bankers an opportunity to assume more responsibility more rapidly.” With more deals possible, each demanding higher-level human judgment, the market actually expands. 

That’s likely to continue to be true even as AI capabilities improve. These tools represent augmentation, often of quite specific elements of professional roles, not replacement. One client complained to us recently that while the bigger investment banks were clearly using AI to develop high-quality investment theses, at smaller banks with fewer senior staff to review, the AI output was more obvious.

For example, Eversheds Sutherland has developed an AI-enabled M&A project management tool, DealMaster, that helps ensure deal details are properly recorded and tasks undertaken. Better workflows and document control do not obviate the need for expert intervention. But they can accelerate and expand it.

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Why human judgement remains essential in technology-enabled deals

This is already evident in the M&A work Eversheds Sutherland undertakes. For example, during the DD stage of a recent deal, a US client wanted to look at around 14,000 contracts – the target’s entire book – rather than random sampling or picking apart a few of the largest agreements. That’s possible today in a way it wasn’t before, in part thanks to AI. But the sheer scale of that analysis – and the need for an experienced review of the exceptional items it reveals – results in greater overall legal workload, not less.

“Even if you got to a point where the diligence product is entirely AI driven – which you won’t –  there will still be a need to address issues in the share purchase agreement [SPA], where clients will need an informed view and where the real level of risk must be assessed in light of the broader deal rationale,” says Lizzy Tindall, Partner, M&A and Private Capital, in the UK, at Eversheds Sutherland. “Even if the diligence report is 300 pages instead of 100, and is compiled much faster than it is today, time will still be needed to evaluate and address the issues raised.”

This is why ‘democratization’ does not mean legal simplification. It means more people can reach the starting line, but the firms that can combine technology with disciplined legal execution are likely to win more often. 

As Wade Stribling, Partner, M&A and Private Capital, in the US, at Eversheds Sutherland, puts it: “You could just take the risk, and cut the cost, by asking Copilot to assess the risks and write your mitigations. But that’s never going to happen because, for example, a private equity partner would still have to explain that decision to their investors. And that’s a hard sell.” Reuters’ columnist Liam Proud spoke to several investment banking MDs who were clear: LLMs and other AI tools might be a great head-start on a pitch deck or a financial model, but they’re “more like a performance-enhancing drug than a replacement” for high-performance bankers, analysts and lawyers.

Lawyers are essential to deal-doers maintaining their edge. By standardizing process, tightening confidentiality controls, ensuring insurer-compatible diligence and helping buyers look credible early, they make it easier for clients to compete in a more crowded field.

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