AI’s Productivity Impact in M&A — And the Cost of Falling Behind

By now, most mergers and acquisitions (M&A) professionals across banks, corporates, private equity firms and advisory teams have at least experimented with artificial intelligence (AI). AI increasingly continues to transition from the test phase into daily dealmaking workflows — from data room prep and discovery to Q&A and redaction. As the early gains add up, an important question is coming into focus: Is AI meaningfully driving productivity for deal teams?

Increasingly, the answer is yes — and the results are difficult to ignore. M&A deals involve vast volumes of documents that need to be reviewed quickly, accurately and securely. This is where AI delivers an advantage, helping teams surface relevant information in seconds and streamline the manual, repetitive workflows that typically slow them down.

A before-and-after look at how AI is changing dealmaking

To fully understand AI’s impact on the deal process, let’s look at the “before” and “after” moments that illustrate how deal workflows are evolving.

Virtual data room prep: Preparing a data room has traditionally required significant organization, as files often arrive inconsistently named, misfiled or out of order. AI streamlines this process by classifying documents as they’re uploaded, applying consistent naming conventions and placing them automatically into the correct folder structure. This creates a diligence-ready data room from day one, reducing friction and administrative cleanup.

Document discovery: Using traditional search methods, analysts may spend days combing through financials, contracts and operational files to pinpoint the details that could influence valuation or risk. AI accelerates discovery by summarizing documents, answering targeted questions and pointing users directly to the areas that require the most attention. Reducing time to insight enables teams to concentrate their expertise on interpreting findings and shaping deal strategy rather than getting stuck on document review.

Due diligence Q&A: Without AI, managing Q&A involves manually sorting buyer questions, routing them to the right experts and chasing down overdue responses — precision work that can slow a deal’s momentum. Today, advanced AI systems can perform much of that orchestration by automatically categorizing inquiries, identifying duplicates and routing questions to the appropriate stakeholders. The result is a faster, more repeatable Q&A process that keeps teams aligned and prevents coordination bottlenecks from stalling progress.

Redaction: Manual redaction once required line-by-line review to identify personally identifiable information (PII) and other sensitive data. AI significantly accelerates this work by detecting sensitive terms across large document sets and applying redactions consistently at scale. As a result, deals move forward with fewer repetitive review cycles and less risk of costly human errors.

What productivity looks like in real transactions

Every hour saved — whether setting up a data room, reviewing financials, answering buyer questions or finding specific contract clauses — creates immediate value. Over the course of a full transaction, those accumulated hours translate into a significant productivity lift.

A more organized data room enables a smoother transition to due diligence. Faster document discovery allows teams to make strategic decisions more quickly and confidently. Streamlined Q&A workflows reduce bottlenecks and improve collaboration. Dealmakers can spend less time searching and indexing documents — and more time interpreting data. Senior leaders can get insights in hours instead of days.

The cumulative effect is a smoother deal process — one marked by fewer delays, miscommunications and surprises. In this context, productivity isn’t just about speed. It’s about gaining confidence, greater control over your workflow and more time to focus on the decisions that matter.

How SS&C Intralinks helps: AI that’s built for dealmaking

Generic AI tools can help with general tasks, but M&A is a highly specialized application of the technology. Effective AI models need to understand the anatomy of a data room, the logic of a corporate structure, the nuances of document redaction and the language of financial and legal documents.

That’s why DealCentre AI^TM is powered by proprietary large language models (LLMs) tuned to recognize the patterns, terminology and workflows unique to strategic financial transactions. This specialization enables our end-to-end dealmaking platform to deliver fast, reliable results — built on an understanding of how real deals operate.

Powered by Link, our AI engine, DealCentre AI gives deal teams an added edge by organizing data rooms, summarizing complex documents, answering specific questions and streamlining diligence cycles. It’s your on-demand, personal AI assistant embedded directly into the deal process.

The bottom line

AI has moved past experimentation and is now a key competitive differentiator. It’s reshaping workflows, accelerating timelines and allowing deal teams to operate with greater clarity and control. As the technology evolves, the productivity gains continue to grow.

The next strategic advantage will belong to the teams that adopt early and align with the right technology partners, leveraging AI to continually elevate the everyday workflows that define a successful deal.