How AI Is Changing M&A Deal Sourcing
Artificial intelligence has been around since the 1950s, when researchers first tried teaching machines to think like humans. It took decades of false starts, faster computers, and oceans of data before AI finally lived up to the hype in the 2010s. And in the realm of mergers and acquisitions, AI has quietly embedded itself across the deal process. In fact, AI is now reshaping how deals get sourced, evaluated, and executed at a pace that is outstripping traditional methods.
According to Deloitte’s 2025 M&A Generative AI Study, 86% of organizations have integrated generative AI (GenAI) into their M&A workflows, and 65% of them did so just within the past year. Bain & Company found that AI adoption in M&A more than doubled in 2025, with 45% of executives now relying on the technology. Just two years ago, that figure was in single digits. We’re watching a disruption where firms that moved first are already seeing measurable advantages in deal flow quality, execution speed, and competitive positioning.
The defining characteristic of AI in M&A right now is how quickly it moved from experimental pilot programs to mission-critical infrastructure. According to recent surveys, 21% of M&A professionals were using generative AI tools in transaction processes as of 2025, with expectations that nearly every step of M&A will be AI-enabled within five years.
The transformation started with deal sourcing because that’s where AI’s advantages are most obvious. Traditional deal sourcing relied on networks, inbound flow from bankers, and manual research to identify potential targets. That approach works when you have time, when markets move slowly, and when information asymmetry creates opportunities for those with better personal networks. None of those conditions exist anymore.
What Is the Impact of AI on Private Market Deals?
The impact of AI on private market deals extends beyond just identifying more targets faster. AI is fundamentally changing the economics of deal sourcing by reducing the cost of evaluation, enabling more sophisticated analysis earlier in the process, and allowing firms to pursue opportunities they previously couldn’t justify researching.
In private markets, information asymmetry traditionally created advantages for investors with superior networks and research capabilities. If you knew people who knew management teams or had analysts who could dedicate weeks to researching a niche sector, you could identify opportunities competitors missed.
AI democratizes access to information while creating new sources of differentiation. The baseline data about companies is increasingly available to any firm with modern AI-powered M&A analytics. But the firms that will win are those that use that data most effectively, combine it with proprietary insights, and execute faster than competitors working with the same information.
Technology now represents nearly a third of the US M&A value, underscoring the sector’s outsized role in driving corporate growth. AI-driven acquisitions accounted for more than 20% of megadeals in 2025, with strategic buyers racing to secure control over models, data, and infrastructure.
How Is AI Deal Sourcing Changing Deal Flow?
Traditional deal sourcing assumed you could only evaluate a limited number of opportunities because research was expensive and time-consuming. That constraint shaped everything about how firms approached deal flow. You focused on companies brought to you by bankers, prioritized opportunities in sectors you knew well, and accepted that you’d miss deals simply because you couldn’t afford to research everything.
AI-powered deal sourcing eliminates that constraint. When AI can screen thousands of companies in the time it used to take to research ten, deal sourcing becomes proactive rather than reactive. Digital transformation in deal sourcing involves more than just using technology to do the same things faster. It has become a reimagination of the whole process.
The best-performing firms in 2025 sourced 60-70% of their deal flow proactively through AI-enabled systems rather than waiting for opportunities to be brought to them. That shift from reactive to proactive sourcing creates enormous advantages in terms of pricing, competitive dynamics, and ability to shape transactions around your specific requirements rather than adapting to processes designed by sellers and their advisors.
How Has AI Changed Due Diligence?
AI-enhanced due diligence processes have become essential as deal timelines compress and the volume of information requiring analysis expands. Motivated sellers often want to move quickly due to liquidity needs, meaning the entire process needs to speed up.
AI transforms this by reviewing documents automatically, extracting key terms, identifying inconsistencies, flagging regulatory issues, and surfacing risks that manual review would likely miss. In a transaction involving a target with 10,000+ active contracts, reviewers typically examine 5-10% of the population, missing material obligations and exposures by design.
The accuracy improvements come from AI’s ability to maintain consistent focus across unlimited volumes of documents. Human reviewers get fatigued or overlook connections between documents reviewed days apart. AI maintains the same thoroughness from the first document to the last, identifies patterns across documents that human reviewers can’t hold in working memory simultaneously, and flags issues based on comprehensive analysis.
What separates leading AI software for deal sourcing from the dozens of point solutions that emerged in recent years is integration depth, data quality, and purpose-built design for M&A workflows rather than being generic business intelligence tools adapted for dealmaking.
What Are The Best AI Tools for M&A Deal Sourcing?
Purpose-built deal origination platforms understand that M&A professionals don’t just need lists of companies. Cyndx represents the forefront of how artificial intelligence is positively changing M&A deal sourcing by providing integrated tools purpose-built specifically for investment professionals.
- Scholar creates comprehensive research reports pulling from proprietary databases covering over 32 million companies plus trusted external sources, generating 20+ page analyses in minutes that would traditionally require weeks of manual research.
- Finder uses natural language processing and machine learning to surface companies based on actual capabilities and market activity rather than just how they describe themselves.
- Acquirer applies predictive analytics to identify which companies are likely acquisition targets and when, using proprietary algorithms that analyze hundreds of signals to forecast propensity to transact.
- Raiser targets the investor side of transactions, identifying which financial and strategic investors are actively deploying capital in specific sectors, have appetite for particular deal sizes, and have demonstrated interest through actual investment history rather than just stated criteria.
- Valer provides AI-powered company valuation software that performs sophisticated analysis with adjustable models, comparable company benchmarking, and precedent transaction analysis, delivering defensible valuations faster than manual financial modeling.
The future of deal sourcing with artificial intelligence will be defined by firms that integrate AI capabilities deeply rather than superficially. The firms that moved first on AI-powered deal sourcing are already capturing advantages in deal flow quality, conversion rates, and competitive positioning.
Dealmakers need to figure out if they’re operating with the capabilities that define modern dealmaking or still relying on methods that may seem to work but can’t compete with firms leveraging AI effectively.
Cyndx offers that leverage. Contact us to find out how.