Artificial intelligence is quickly becoming the lifeblood of M&A.

Adoption of AI tools more than doubled in 2025, according to our recent survey of more than 300 M&A executives, with 45% of respondents now relying on the technology. And they’re now using it in tangible ways across more touchpoints in the M&A life cycle. Just a few years ago, AI was largely limited to sourcing, screening, and diligence. Those remain the highest areas of AI deployment, but more companies have begun relying on AI for later stages of the deal cycle—namely, transaction execution, integration, and learning.

About one-third of dealmakers say that they deploy AI systematically or are redesigning M&A processes to take advantage of AI, and more than half tell us that they expect AI to significantly impact how deals are done.

Companies on the leading edge are using AI in five ways to get more value out of M&A: dynamic pipeline, enhanced accuracy in outside-in intelligence, faster path to greater synergies, minimizing integration prep work, and earlier and deeper stakeholder insights.

Dynamic pipeline

When an India-based global healthcare company increased M&A’s role in future growth, it knew it needed to update its screening capability. The company’s traditional approach was to manually maintain a short list of high-potential targets, infrequently refreshing the list when capacity allowed. It evaluated new targets as they came to market, and many were not on the screened targets list.

The company converted to AI-enabled software that speedily tracks and develops a list of potential targets. The company now depends on the technology to continuously search a larger universe for potential targets, and it dynamically refreshes the list based on a broad set of data before the formal bidding process begins. It is now much better positioned to win priority assets.

Enhanced accuracy in outside-in diligence

With so much pressure to get the most out of every deal, acquirers can no longer settle only for confirmatory diligence aimed at finding the financial, legal, and regulatory red flags—nor can they take a seller’s commercial and product assertions at face value. Savvy acquirers now use AI tools to quickly size up a target’s cost base, scraping and analyzing public sources to map workforce structures and spending profiles well before any formal bid.

AI recently allowed a media acquirer to make an outside-in assessment of the target’s cost base early in the process—for example, scraping publicly available sources such as LinkedIn to understand the workforce structure and spending profile. After the close, the forecast was within 90% of actuals. The new capability enabled the company to adjust its valuation based on the accurate anticipated cost synergies and gave it higher confidence in the deal.

Faster path to greater synergies

AI-enabled analytics allow companies to process raw data to design a better solution in less than 10% of the time it would take with a manual approach.

Two companies that buy significant amounts of commodities had complementary capabilities: One was best in class at sourcing and hedging; the other led the industry with its proprietary mix of commodities. A major goal of the integration was to quickly develop an optimized purchasing and product strategy for the combined entity.

Historically, companies in this situation relied on a sequential path of cleaning the data, identifying and sizing the opportunities, and building plans to realize the savings. This process would have taken more than 12 months to create the plans. In the merger, the companies fed procurement, hedging, and mixing data of all types (clean or uncleaned, formal or informal) into a third-party AI model to rapidly create an optimized purchasing, hedging, and product recipe model—all in about two months. The company now anticipates generating savings totaling $100 million, an estimated 20% more than what it would have gained by taking a more sequential path.

Minimizing integration prep work

With AI, the best companies now have Day 1 plans and merger integrations that are faster, more efficient, and substantially better tailored to the specifics of the deal. When a leading European bank bought a challenger, it identified nearly €600 million in potential cost and revenue synergies. By relying on AI generative pretrained transformers (GPTs), the company refined synergies; built playbooks for IT, people, and customer integration; and mobilized teams—thus shaving 25% off the time it ordinarily would take while also creating massive credibility across the integration management office and teams.

Where did the time savings come from? Forty hours were saved by drafting an integration thesis and obtaining alignment in a single day. About 60 hours were saved from auto-generating and prioritizing pivotal decisions across work streams. Eighty hours were saved by auto-drafting 13 team charters with accelerated input. And another 60 hours were saved by rapidly producing structured, milestone-based work plans in two days to pressure test key activities and spot gaps.

Earlier and deeper stakeholder insights

A professional services company launched an integration under a compressed timeline and needed to ensure clear, productive communications to all stakeholders. It was a merger of equals, but each legacy organization had deeply rooted cultural norms and ways of working. The challenge was to align both companies with a unified mission, vision, and values.

Rather than only relying on focus groups, the company used AI to speedily build synthetic employee and customer profiles, drawing on surveys and publicly available information. It substantially upgraded and streamlined the process of testing integrated communications and refining the mission, vision, and values for the combined organization.

The result? The company got to a better answer on a clear joint mission faster, which helped people go through the change curve more quickly. It took the company only three months post-close to achieve alignment across the organizations, and the company was able to launch its campaign five months post-close. Critical to the deal’s early success, the company reduced the risk of internal and external communications landing poorly with key stakeholders.

While companies across industries redesign their end-to-end M&A processes to use AI to simplify, automate, and help them integrate, many are just now embarking on the longer task of implementation.

As companies embrace the future, it’s important to pay attention to what AI cannot do for them.

The reality is that as fast as AI can be, it still takes time and consideration to align stakeholders, make critical decisions, inspire an organization, and support sustained change management. People still need time to process, even if they’re given perfect information.