From Adoption to Governance: The Next Phase of AI in M&A

New benchmark research shows how AI is being embedded across M&A workflows — even as security concerns intensify.

From pilot to embedded infrastructure

The question of AI’s role in facilitating M&A transactions has effectively been answered. Our research shows nearly nine in ten deal teams have moved beyond pilot programs, and close to half report that AI is already fully integrated across most stages of their deal process.

This is a meaningful shift in a relatively short period of time, reflecting how quickly AI has become embedded in the core of how deals are sourced, analyzed and executed.

However, integration does not always mean consolidation. In practice, most deal teams are operating with multiple AI tools simultaneously — often three to five, and sometimes more. This reflects a broader trend toward specialized solutions addressing specific use cases, rather than a single, unified platform. For many firms, this multi-tool environment introduces new layers of complexity. Data fragmentation, inconsistent workflows and limited visibility across tools can create inefficiencies — and more importantly, risk.

This shift is also changing how firms think about competitive advantage in dealmaking:

“[AI] is like the ATM. The first bank to have an ATM won customers with it, but now there’s no advantage to the ATM. I think AI is pushing the floor up for what good looks like.” -- Amias Gerety, Partner, Head of U.S., QED Investors

The governance gap is widening

One of the most important findings in the report is the growing disconnect between AI adoption and AI governance. While usage has scaled rapidly across the deal life cycle, governance capabilities have not always evolved at the same pace. In fact, nearly all firms report the need for having some form of AI policy or compliance framework in place. The challenge seems to be not the absence of governance on paper but rather how consistently those policies are applied in day-to-day deal activity.

This gap becomes particularly pronounced in M&A environments, where sensitive information is shared across multiple stakeholders, systems and jurisdictions. For deal teams, this raises an important consideration. Putting effective AI governance in place means ensuring that rules are not only set, but also consistently applied in everyday work and embedded directly into the architecture of the tools and platforms that support the deal process. Translating policy into practice better positions firms to scale AI with confidence while maintaining the control and transparency required in high-stakes transactions.

Leading institutions are already taking a more structured approach to managing AI risk:

“I think the concept of guardrails and responsible AI Is a big part of what we do. We have a process by which we look at 16 different dimensions of risk.” -- Hari Gopalkrishnan, Chief Technology and Information Officer, Bank of America; Speaking at Reuters Events: Momentum AI Finance, November 2025

Where AI is delivering value today

AI’s impact is already tangible across the deal life cycle, particularly in areas that are data-intensive and time-sensitive. Due diligence stands out as one of the clearest examples. More than one-third of dealmakers surveyed report saving between 21 percent and 30 percent of their time in diligence-related tasks. From document review to financial analysis and anomaly detection, AI is helping deal teams process large volumes of information faster and with greater consistency. Similar patterns are emerging in deal sourcing and screening, where AI is being used to identify targets, extract market signals and prioritize opportunities. In these early stages, the ability to automate search and analysis is allowing teams to expand their pipelines without proportionally increasing resources.

That said, the current generation of AI tools tends to excel at accelerating individual tasks rather than transforming entire workflows. The next wave of value will likely come from connecting these capabilities into more continuous, end-to-end processes.

Trust is growing — but so are the risks

As AI becomes more embedded in deal workflows, trust in its outputs is steadily increasing. A majority of dealmakers report relatively high confidence in AI across key workstreams, including valuation, diligence and execution.

However, that growing reliance is being shaped by a parallel reality: risk is rising alongside adoption. Eighty percent of firms report having experienced AI-related security incidents or near misses in the past year, including access control lapses and inaccurate outputs driven by hallucinations — a startling statistic which underscores that while AI is delivering value, it is also introducing new exposures that must be actively managed.

This tension is also reflected in how AI is perceived across the deal life cycle. The areas where AI is most widely used — such as due diligence and deal execution — are also where dealmakers say it contributes least to human judgment. While AI can surface insights, flag anomalies and process data at scale, the final interpretation still depends on human experience, context and accountability, reinforcing the importance of maintaining a human-in-the-loop approach.

Organizational dynamics are shifting

AI adoption is also changing how deal teams operate and how they think about risk. While usage is increasing across all levels of the organization, our report highlights a growing tension between junior professionals, who are often leading AI adoption, and senior leaders, who are responsible for oversight and client accountability. More than half of respondents report increased resistance from senior leadership over the past year, driven by accuracy and compliance concerns, explainability, and fiduciary and reputational risk.

At the same time, many organizations acknowledge a capability gap. While most teams describe themselves as regular users of AI, only a small percentage of firms (10 percent) have dedicated AI or machine learning specialists embedded within their teams. As AI becomes more central to dealmaking, closing this gap will be essential.

Preparing for the next phase of AI in M&A

Looking ahead, dealmakers are already anticipating a more advanced role for AI. Many expect to see increased use of autonomous systems capable of executing multi-step workflows, as well as more predictive analytics to inform deal strategy and outcomes. At the same time, preferences around technology are becoming clearer. Nearly half of respondents favor a best-of-breed approach, using specialized tools for specific tasks, while more than half expect AI capabilities to be embedded into core deal platforms.

This points to an important shift. The focus is moving away from simply adopting AI toward integrating it more seamlessly into the broader deal ecosystem. For organizations, this creates both an opportunity and a responsibility. The firms that will lead in the next phase of AI-enabled dealmaking are those that can bring together speed, accuracy and transparency in a single, controlled environment. The challenge now is ensuring that AI is applied in a way that is secure and aligned with the realities of high-stakes transactions.

For the full findings, read AI in M&A Dealmaking 2026: A Benchmark Study.