Today's mergers and acquisitions (M&A) deal teams are managing three to five AI tools simultaneously, applying them across sourcing, diligence, valuation and execution. That operational reality shows that AI has moved beyond the pilot phase in dealmaking. According to our new benchmark study, AI in Dealmaking: A Benchmark Study, produced by Reuters Insights in partnership with SS&C Intralinks, nearly half of M&A professionals now describe AI as fully integrated across most stages of their deal process, and a further four in ten report partial integration. Just one in ten remains in pilot or experimental mode. Deal teams have shifted from deciding whether to adopt AI to how well they can implement it.
AI Across the Deal Life Cycle
The data reveals that AI has been distributed across the entire life cycle, from initial sourcing through post-merger integration, with varying degrees of penetration and measurable impact at each stage.
In deal sourcing and screening, automated extraction of financial and market signals is the most common application, deployed by 57% of respondents. Pipeline scoring, a more analytically demanding task, follows at 35%. Time savings in this phase are material: 87% of respondents report efficiency gains of at least 11%, with the largest cohort saving 21-30% of their time.
Due diligence represents AI's most pronounced point of impact. Financial analysis spanning statements, transactions, anomaly detection and public record searches is the leading application, used by nearly 70% of respondents, followed by document review, cybersecurity diligence and legal and compliance review. More than eight in ten professionals are using AI in early-stage diligence, and adoption in confirmatory diligence, including Q&A processes, sits at 78%. In terms of time savings, more than a third of respondents report gains of 21-30% in this phase. For deal teams where time and accuracy are the limiting factors, those are not marginal gains.
That efficiency extends to deal marketing, where AI is most commonly applied to executive summaries and marketing strategy, and to deal execution, where 81% of respondents report time savings of more than 11%. Historical deal analytics and portfolio company management round out an AI footprint that, in aggregate, now touches virtually every decision-relevant stage of the transaction process.
Yet efficiency gains across the life cycle have largely concentrated in the 11-30% range, with fewer than 6% of respondents reporting savings exceeding 50% in any single phase. The current generation of tools has accelerated discrete tasks well. What it has not yet achieved is the integration of those tasks into continuous, connected workflows, which is precisely where the profession's expectations for the next phase of AI are focused.
The Human Element Remains Non-Negotiable
Human judgment remains central to the deal process, and experienced professionals are keeping it that way.
A striking seniority gradient emerges in how AI is being used for valuation and financial modeling. Nearly two-thirds of analysts and associates use AI regularly for these tasks, compared with 44% of VPs, directors, partners and managing directors. Senior partners and MDs are the most likely to avoid AI for valuation entirely, with 12% reporting no use at all. This shows a rational pattern. AI is being absorbed most completely at the levels where the analytical workload is heaviest, with senior professionals maintaining the interpretive and fiduciary layer above it.
When asked where AI aids human judgment the least, respondents identified deal execution and due diligence, which are the two phases where AI is simultaneously most heavily deployed. This apparent contradiction is, in fact, coherent. These phases involve the most complex, contextual and legally consequential decisions in any transaction. AI can process documents, flag anomalies and surface patterns, but the strategic and interpretive judgment required to act on those outputs demands human involvement.
What is perhaps more revealing is dealmakers' appetite for autonomous AI despite this awareness. Four in five respondents express comfort with AI systems that plan and execute multi-step workflows with minimal human intervention. That signals where the profession expects to be, even if today's governance infrastructure is not yet ready to support it.
Governance: The Gap Between Policy and Practice
This is where the research becomes most urgent. In the past 12 months, 80% of firms experienced an AI-related security incident or near miss. Access-control lapses were the most common incident type, reported by 48% of respondents. Hallucinated outputs leading to inaccurate diligence followed at 40%.
What makes these findings particularly difficult to dismiss is that they are not occurring in organizations that have neglected AI governance. Nearly all respondents, 94%, report operating under at least one formal AI policy or compliance framework. The problem is not an absence of policy. It is the distance between policy and practice; a gap that is widening as AI adoption penetrates faster and deeper than organizational controls can mature.
Senior leadership has taken notice. More than half of dealmakers report that senior-level resistance to AI has increased in the past 12 months. Accuracy of outputs, explainability, fiduciary risk and client perception are the primary concerns. Advisory firms and investment banks report the most pronounced internal pushback, with nearly three-quarters noting that senior resistance has grown.
The path forward requires simultaneous investment in the technical architecture of AI deployment, particularly around data security, access controls and platform integration, and the organizational infrastructure to govern it, including structured AI literacy for senior decision-makers who bear ultimate accountability for AI-assisted outputs.
The competitive advantage in AI-enabled dealmaking will not accrue to firms that adopt the most tools or move the fastest. It will belong to those who close the gap between technological capability and organizational readiness. SS&C Intralinks' DealCentre AI addresses both dimensions, embedding AI across the full transaction life cycle on a security architecture as a foundation.
Download our full report to learn more about AI adoption in the dealmaking process.