- 64% of executives believe generative AI will be more transformative than any recent technological innovation when it comes to the M&A process¹.
- 70% think AI will assist them in generating more alpha on trades—in other words, higher returns than the market delivers¹.
- 44% of dealmakers state AI has already made their team more efficient².
- And almost a third (29%) cite deal sourcing as the biggest area of AI-driven impact to date².
The early indicators are clear: AI is not simply accelerating, it’s altering what work gets done, how decisions are made, and who spots the next big opportunity first.
Artificial intelligence is no longer a side conversation in boardrooms and deal rooms. It’s becoming central to the way private equity and corporate M&A teams think about sourcing, assessing, and executing deals.
Today, AI is part of real deal flow. It’s assisting with everything from sourcing targets and identifying risks during due diligence to forecasting how a company will perform post-closure. And it’s already paying off.
Only 16% of M&A practitioners currently use AI—principally pre-deal. But this adoption rate is expected to increase significantly, with estimates suggesting that as many as 80% of professionals might use AI in their workflows within the next three years⁵. According to Bain & Company, early adopters of generative AI in M&A are seeing a 79% decrease in manual effort, with 54% seeing accelerated timelines and 33% having reduced costs⁶.
So what does this all mean for private equity?
What’s really changing? Five shifts are happening now—and they’re changing the way smart money is playing the game.
1. Deal Sourcing Is No Longer a Manual Game
The art of private equity deals has always revolved around networks, intuition, and timing. Outbound outreach alone is a past way of doing business for sales firms.
AI-powered platforms now track everything from product launches to talent churn, competitor hiring spikes, patent filings, online sentiment, and supplier activity. Those signals can quietly bring potential targets to the surface weeks — at times months — before traditional methods take hold.
Case in Point:
46% of PE firms are using AI specifically for deal sourcing purposes, which demonstrates an increasing trend of using AI to identify and evaluate potential investment opportunities⁷.
Actionable Steps:
Deploy AI Platforms: Invest in systems that can analyze in real-time across sectors and geographies.
Define Clear KPIs: Metrics such as deal velocity, time-to-close, and IRR impact should be benchmarked and tracked.
Pilot and Scale: Run targeted pilots before scaling across sourcing functions.
Takeaway:
The sourcing paradigm is moving from “who you know” to “what your data sees.” Relationship capital matters now more than ever—but it’s being elevated, not replaced, by signal-driven smarts.
2. Accelerated Due Diligence with Intelligent Automation
Due diligence has historically been resource-heavy and risk-sensitive. AI is not replacing judgment, but it is speeding up precision.
Tools for text analytics now analyze hundreds of contracts in just minutes. NLP models extract risks hidden in indemnity clauses, revenue recognition terms, and regulatory language. Algorithms flag financial anomalies without requiring weeks of manual spreadsheet audits.
What This Looks Like in Practice:
Generative AI is enabling companies to close M&A deals up to 50% faster, according to a 2023 Datasite survey⁸. Piper Sandler and others are already embedding these tools to frontload risk detection and simplify financial reviews.
Actionable Steps:
Integrate AI Tools: Automate NDA, contract, and regulatory document analysis.
SWIM: Quantify time savings and monitor turnaround times.
Expand Usage: Apply AI tools across legal, financial, and compliance tracks.
Why It Matters:
In a market with healthy targets that move quickly, compressing diligence timelines — without adding risk — is a competitive advantage.
3. Enhanced Target Screening through Predictive Analytics
AI’s next frontier isn’t simply finding more deals. It’s finding better ones.
Machine learning models are beginning to surpass traditional screening methods by learning from what worked — and what didn’t. They consume historical deal performance, macroeconomic triggers, customer churn data, and pricing signals. Then they adjust future targets accordingly.
BlackRock integrated predictive analytics into its demand planning workflow. These models now inform M&A plays by modeling post-merger risks and growth potential — a practice rapidly normalized by growth-stage funds⁹.
Actionable Steps:
Data Criteria Definition: Use predictors like revenue growth rate (NRR), digital activity, regional sentiments.
Use Predictive Models: Train algorithms on past deal outcomes to refine target identification.
Track Results: Measure post-deal performance of AI-flagged targets and adjust inputs accordingly.
Bottom Line:
Predictive analytics isn’t about assumptions. It’s about quantifying patterns humans forget and surfacing asymmetrical upside. And it’s already working.
4. Optimized Post-Merger Integration and Synergy Realization
Post-merger integration (PMI) has always been a tricky chapter—where value either compounds or evaporates in spreadsheets, systems, and silos.
AI is helping make integration more predictable. Tools now detect back-office redundancies, forecast cultural clashes, and simulate operating models. Algorithms can map IT overlaps, HR risks, and synergy bottlenecks before problems start eroding value.
What’s Happening on the Ground:
On average, firms deploying AI in post-merger activities report a 25% reduction in integration times. Synergy realization improves by 15% due to automated backend merges and early flagging of operational overlaps¹⁰.
Actionable Steps:
Back Office Focus: Prioritize IT and ops integration early.
Implement Early: Introduce AI tools right after closing, not months later.
Synergy Tracking: Monitor savings, employee retention, and system unification through dashboards.
Why It’s Different Now:
Integration plans used to be theoretical. Now they’re grounded in real-time signals, process mining, and feedback loops. The result? Faster alignment, stronger synergies, and lower execution risk.
5. Robust Governance and Risk Management Frameworks
AI delivers speed and insight — but it also brings exposure. When algorithms start driving decisions, governance can’t be an afterthought. Leading PE firms are putting AI risk management in place: is the training data validated? Is bias being monitored? How does it align with compliance?
What Leading Firms Are Doing:
Wellington Management has created a formal AI governance framework with cross-functional teams reviewing AI use cases, maintaining model integrity, and assessing risk exposure¹¹. This isn’t just about compliance—it’s about resilience.
Actionable Steps:
Form Governance Teams: Bring together compliance, legal, IT, and investment units to co-own oversight.
Create Checklists: Structure AI-related diligence for both acquisitions and internal workflows.
Bring in Outside Counsel: Conduct quarterly or post-deal reviews with legal and cybersecurity experts.
Takeaway:
AI is only an edge if you trust what it tells you. The firms winning at risk management aren’t just coding better—they’re governing smarter.
Conclusion:
Private equity firms that treat AI as a bolt-on tool will fall behind. The ones that build it into every stage of the M&A lifecycle—from sourcing to integration—stand to gain the most.
With generative AI investment surpassing $2 billion in 2023 and real use cases moving from pilot to practice, the future of dealmaking isn’t speculative anymore³. It’s already here. And it’s machine-informed.
