Harnessing AI for competitive advantage in M&A: insights from The Dealist

Artificial intelligence is rapidly transforming how mergers and acquisitions are conducted, creating a widening gap between early adopters and those hesitant to embrace the technology. As organizations navigate this shift, understanding both the capabilities and limitations of AI in M&A has become essential for maintaining competitive advantage.

Debunking common AI misconceptions in M&A

Several persistent myths continue to cloud understanding of AI's role in dealmaking. The most prevalent misconception is that AI will completely replace human professionals in M&A transactions. The reality is far more nuanced.

AI does not understand meaning the way humans do. These models operate based on patterns and probabilities rather than true comprehension. This fundamental limitation means that while AI can process vast amounts of data quickly, it still requires human judgment to interpret context and make strategic decisions.

Another common misunderstanding is that AI is inherently objective and unbiased. In truth, AI reflects the biases present in its training data. Historical inequities or errors in source material can be perpetuated or even amplified through AI systems, making human oversight critical for ensuring fair and accurate outcomes.

The notion that AI is only valuable for complex mega deals also misses the mark. Organizations of all sizes are finding practical applications that boost productivity across routine tasks and sophisticated analysis alike.

How AI enhances rather than replaces human expertise

The question facing M&A professionals today is not whether AI will replace them, but rather how their roles will evolve. Lawyers, bankers, and consultants are all grappling with how to leverage AI to remain relevant and deliver deeper insights faster than competitors.

AI excels at accelerating the initial phases of diligence. What once took two to four weeks to understand about a target's business trajectory can now be accomplished in days. This compression of timeline does not eliminate the need for professionals but rather shifts their focus toward higher value activities.

Current applications of AI in M&A

AI is currently being trusted for several specific functions.

High confidence autonomous tasks:

• Document classification and organization
• Translation services
• Redaction of personally identifiable information
• Contract review and triage

Augmented human decision making:

• Data room querying and analysis
• Identifying duplicate questions in Q&A processes
• Validating business models against contractual obligations
• Synergy analysis and value creation planning

The most sophisticated acquirers are beginning to load their historical deal models into AI systems, enabling them to query past performance against new transactions. This allows them to better predict achievable synergies and underwrite value with greater confidence.

Accelerating deal velocity without sacrificing quality

Speed has become a critical competitive factor in M&A. AI's primary contribution is not just making deals faster overall but rather accelerating data ingestion so teams can spend more time determining how to extract maximum value from a transaction.

On the sell side, AI enables much faster responses to buyer questions and helps identify duplicate or similar inquiries across hundreds of questions in structured Q&A processes. This capability allows sellers to potentially shorten the timeframe they allow for buyer questions.

For buyers, the benefits extend beyond initial diligence. AI can dramatically reduce the time between signing and closing by quickly identifying which contracts require significant work versus those that can simply be assigned. In one healthcare example, a quality system review that traditionally took months was completed in 24 hours using AI to identify differences between the target's and acquirer's systems.

The competitive advantage of frequent acquirers and early AI adopters

Research spanning 40 years consistently shows that companies completing one or more acquisitions per year over a decade achieve two to three times the total shareholder return of those doing less than one deal annually. A new dimension has emerged: early adoption of AI appears to have a similar impact on shareholder returns as frequent acquisition activity.

Organizations that combine frequent M&A activity with early AI adoption are seeing more than twice the advantage in shareholder return compared to infrequent acquirers who lag in AI adoption. This gap is expected to widen significantly as AI capabilities mature and become more deeply integrated into deal processes.

The implication is clear: waiting to adopt AI in M&A is not a neutral decision. It is an increasingly risky position that puts organizations at a growing disadvantage against competitors who are leveraging these tools to drive differential returns.

Real world impact: case studies in AI augmentation

Practical applications of AI in M&A are delivering measurable value across multiple scenarios. In one case, AI diligence identified contaminated open source code in a target that would have impacted the acquirer's ability to ship 50 other products using similar code. This type of critical insight demonstrates how AI can surface risks that might be missed in traditional manual review.

Organizations are using AI to validate revenue models by cross referencing them against contractual obligations buried in data room documents. This process would be prohibitively time consuming manually but provides valuable confirmation or correction of financial assumptions.

Even seemingly simple applications deliver significant value. Identifying duplicate questions across 700 to 800 inquiries in a structured Q&A process ensures consistent responses and eliminates redundant work. These productivity gains accumulate across the transaction lifecycle.

The evolving M&A team: new skills for a new era

The integration of AI into M&A does not eliminate the need for junior professionals, but it does change the skill sets required. The shift mirrors what occurred with robotic process automation in other business functions. Rather than replacing workers, it changed what organizations looked for in talent.

Junior bankers, lawyers, and consultants will increasingly need to understand how to train and configure AI agents as part of their teams. The ability to communicate with AI using natural language and custom prompts represents a new form of technical literacy. These professionals become multipliers, leveraging AI to accomplish what previously required much larger teams.

For senior professionals, the challenge is determining how to fill the time freed up by AI automation with activities that capture more value. When an AI can summarize all analyst reports in an industry in an hour rather than two days, or triage a thousand contracts by complexity in minutes rather than weeks, the question becomes: what higher value work should humans focus on?

The answer increasingly centers on deal strategy, creative value creation, and nuanced judgment calls that require deep industry knowledge and relationship skills. Advisers are excited about spending more time on strategic questions rather than core diligence exercises.

The near term future: what to expect in the next three to five years

The M&A landscape will continue its rapid transformation as AI capabilities expand and adoption deepens. Several trends are likely to shape the next phase of evolution.

Data rooms will evolve from simple secure sharing platforms into comprehensive deal making environments that provide complementary capabilities and services throughout diligence, post merger integration, and the broader deal lifecycle. AI agents will move beyond copilot functions to perform significant work involving chains of thought and action with minimal human intervention.

This does not mean AI will conduct deals autonomously. Human oversight will remain essential, but the nature of that oversight will shift toward exception handling and strategic decision making rather than routine processing.

The pace of deals should accelerate substantially. The current average of seven month diligence processes will compress as AI handles more of the data intensive work. However, this speed will not come at the expense of quality. Instead, it will allow more time for the strategic analysis that drives successful outcomes.

Winners and losers: the growing divide

Perhaps the most significant prediction for the coming years is the widening performance gap between AI adopters and laggards. In just 12 to 18 months since AI gained serious traction in M&A, a substantial dispersion in shareholder returns has already emerged between early adopters and those moving more slowly.

This gap will continue to expand. Organizations that excel at M&A and add AI capabilities to their toolkit will pull further ahead. Conversely, it will become increasingly risky for infrequent acquirers who are late to AI adoption to achieve positive deal outcomes.

The competitive dynamics of M&A ensure this pressure will intensify. Solo bidder situations are virtually extinct for transactions of any scale. Every significant deal involves multiple bidders, typically including both strategic and financial buyers. In this environment, any organization that is not leveraging AI while competing against those who are faces a substantial disadvantage.

The most successful acquirers will be those who use AI to accelerate the routine aspects of transactions, freeing up time and cognitive resources to focus on the critical question: how do we drive differential value from this asset? That focus on value creation, enabled by AI's efficiency gains, will separate winners from losers in the M&A arena.

Taking action: embracing AI for M&A success

The evidence is compelling that AI represents a fundamental shift in how M&A is conducted. Organizations have a choice: embrace the technology and invest in building capabilities, or fall behind competitors who are already capturing the benefits.

The path forward requires acknowledging both AI's capabilities and its limitations. It is a powerful tool for processing data, identifying patterns, and accelerating routine tasks. It is not a replacement for human judgment, strategic thinking, or relationship skills.

Success will come from thoughtfully integrating AI into deal teams, developing new skills among professionals at all levels, and maintaining appropriate human oversight while allowing AI to handle tasks it performs well. Organizations that strike this balance will find themselves with a significant and growing competitive advantage in the increasingly fast paced world of M&A.