Governing ai in m&a: navigating the next frontier in dealmaking

The question of whether AI has arrived in M&A is no longer up for debate. The real conversation has shifted to something far more complex: how do we govern it, trust it, and bridge the gap between what AI can do and what our organizations are actually ready for?

A comprehensive study conducted by SS&C Intralinks in partnership with Reuters Insights surveyed 400 senior deal professionals across private equity firms, investment banks, corporate acquirers, venture capital firms, and law firms worldwide. The findings reveal an industry at an inflection point, where adoption has outpaced governance and technological capability has surged ahead of organizational readiness.

AI has crossed the adoption tipping point

The data tells an unambiguous story. Nine in ten deal teams have moved beyond the pilot phase:

• 49% report AI is fully integrated across most deal stages
• 41% describe partial integration across their workflows
• Only 10% remain in experimental or non-adoption phases

As Kim Posnett, Goldman Sachs' global co-head of investment banking, stated in Fortune this January: "Global enterprises have moved past the pilot phase and into a period of deep structural transformation."

However, a critical nuance emerges in the data. While 49% claim full integration, only 28% describe AI as well integrated within their core deal platforms. This gap reveals that most teams are using AI through a patchwork of general purpose tools layered on top of existing workflows rather than embedded within them. This distinction matters enormously for security and governance.

The adoption rates vary significantly by firm type:

• Advisory firms and investment banks lead at 69% full integration
• Private equity firms follow at 58%
• Law firms report 39%
• Corporate acquirers trail at 29%

Measurable time savings across the deal lifecycle

AI is delivering real, quantifiable efficiency gains across every phase of dealmaking.

In deal sourcing and screening, 87% of respondents report time savings of at least 11%, with the largest cohort citing 20–30% reductions through AI‑automated financial signal extraction and company discovery.

In due diligence, where AI's impact is most pronounced, 35% of dealmakers report 21–30% time savings. The top applications include:

• Financial analysis (69%)
• Document review (61%)
• Cybersecurity diligence (53%)
• Legal and compliance work (49%)

In deal execution, 81% report time savings of at least 10%, with AI handling Q&A, red flag detection, and transaction coordination.

John Stecher, CTO at Blackstone, captured the transformation succinctly: "AI gives you the ability to see a lot more deal flow than you were able to do before. Before you were pretty much bounded by analyst time and you had to open up a deal room, read through hundreds of documents. It reduced the friction to transact."

That phrase matters. AI isn't replacing judgment. It's removing the bottlenecks that slow judgment down.

Notably, fewer than 6% report savings exceeding 50%, indicating significant headroom for efficiency gains as integration deepens.

Trust is high but not uniform

Dealmakers broadly trust AI outputs, with a majority rating their confidence at 70 out of 100 or higher across every workflow measured.

Trust levels by function:

• Valuation and financial modeling: 62%
• Post-merger integration: 58%
• Due diligence: 56%

Perhaps most striking, four in five dealmakers are comfortable with AI executing multi-step workflows autonomously. This represents a remarkable signal of where the market is heading.

Yet a paradox exists within this data. When asked where AI aids human judgment the least, respondents pointed to deal execution and due diligence, the very phases where AI is most heavily deployed.

This isn't a contradiction. It reflects the reality that these phases involve the most complex, contextually nuanced, and legally consequential decisions in any transaction. AI can surface patterns and flag anomalies, but the step from insight to action, particularly in high-stakes moments, demands human judgment. Increasingly, that means not just a human in the loop, but a human in the lead.

The implication is clear: the next wave of efficiency gains won't come from adding more AI tools to individual tasks. They will come from integrating these tools into connected, continuous workflows with agentic capabilities.

The security gap: 80% have experienced AI incidents

Four in five firms have already experienced AI security incidents. This isn't a fringe risk. It's an operational reality affecting the overwhelming majority of dealmakers today.

The most prevalent incident types:

• Access control lapses (48%): AI agents given permission over too much sensitive information or making unwanted changes to digital assets
• Hallucinated AI outputs (40%): Incorrect or fabricated information presented as fact

Here's the irony that makes this finding particularly sharp: 95% of these same firms operate at least one formal AI governance framework. The problem isn't an absence of policy. The gap is between policy and practice, and it's widening as AI adoption accelerates faster than governance structures can mature.

Harpal Krishnan, Bank of America's CTIO, put it this way: "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."

The structural issue creating much of this risk is data movement. When deal teams move documents in and out of data rooms to plug into external AI platforms, they create data exposure with every transfer. A virtual data room with AI embedded natively, where data never leaves the secure environment, is architecturally different in ways that matter enormously at scale.

Senior resistance is rising, not falling

Fifty-three percent of dealmakers report senior-level resistance to AI has increased in the last 12 months.

As AI proves its value, you might expect resistance to decline. The opposite is happening, and it's not irrational.

The top drivers of senior resistance:

• Accuracy concerns
• Explainability and transparency
• Fiduciary risk
• Client perception

These aren't technophobes. They are accountable leaders increasingly being asked to put their name behind analysis they don't fully understand.

Consider this seniority split: 67% of C-suite and partner-level respondents consider AI fully integrated, but only 29% of associates and analysts say the same. Senior leaders may be significantly overestimating AI maturity within their firms.

Byron Minerd, Chief Operating Officer of Apollo, stated: "Our gate is not money. The gate is the organization's ability to handle all of the change. We are investing as much in people and operating model behind the digital infrastructure as we are in the AI itself."

The solution isn't converting skeptics into evangelists. It's building structured AI literacy at the leadership level so the people who sign off on AI-driven outputs understand how those outputs are produced. That's a governance investment, not a technology investment.

The 2030 deal team: automated, specialized, and AI-native

When asked which AI capability would be most disruptive by 2030, respondents pointed to fundamental transformations:

• Automated quality of earnings (43%): Fully automated, comprehensive, full-population analysis rather than sampling
• Fully autonomous digital agents (41%): Click-and-execute capabilities for entire diligence workstreams
• AI-generated SIMs (39%): Automated creation of confidential information memoranda
• Predictive deal outcomes modeling (39%): Forecasting transaction success probability

These aren't incremental improvements. They represent step changes in how deals get done.

The team composition will shift accordingly:

• 64% expect ML and AI specialists embedded directly in deal teams by 2030
• 53% anticipate a shift toward AI-literate generalists, replacing siloed specialists

The message is clear: knowing how to use AI will be as fundamental as knowing how to build a financial model.

The market wants specialization, not consolidation

Forty-nine percent of dealmakers prefer multiple specialized AI tools purpose-built for sourcing, diligence, and valuation over a single all-in-one platform (20%).

However, 28% want all tools well integrated into their core deal platforms. This reveals a gap: teams are running AI alongside workflows, not inside them.

Fifty-four percent expect AI to be standard infrastructure in dealmaking, not a plug-in or bolt-on. The next battleground won't be which firm has the longest AI feature list. It will be trust, security, output reliability, and how seamlessly AI is embedded in the platforms where deals actually get done.

Closing the gap between capability and readiness

The industry has crossed the AI adoption tipping point. The question is no longer whether to use AI. The question is whether your organization's governance, security architecture, and leadership literacy are keeping pace with the technology.

Three critical priorities emerge:

Governance versus practice. Ninety-four percent of firms have AI governance frameworks, but 80% experience security incidents. The gap between what's written and what's practiced is where the risk lies.

Leadership literacy. Senior leaders are signing off on AI-assisted outputs, but they need to understand how to evaluate them. This isn't a technology problem. It's a fiduciary one.

Building the 2030 team. With 64% expecting ML and AI specialists on deal teams and a shift toward AI-literate generalists, the question becomes: who is building this capability now?

The firms that will lead in AI-enabled dealmaking will be those that close the gap between technological capability and organizational readiness deliberately, measurably, and before their competitors do.

The future of M&A isn't about having AI. It's about governing it well, trusting it appropriately, and embedding it so deeply into your infrastructure that it becomes invisible. That's when AI stops being a tool and becomes a competitive advantage.