In this article, we will talk about how AI could help to enhance processes, the rate of adoption, the risk of being left out, why now it’s a good moment to start experimenting with AI tools and what risks should we pay attentions to.
AI as a Tactical Enabler in M&A
- The most effective AI applications in M&A are those that help streamline processes, reduce costs, and improve decision-making accuracy. Key areas where AI is proving invaluable include:
- Deal Sourcing & Screening: AI-powered platforms scan global databases to identify acquisition targets that align with strategic objectives, assessing financial health, industry trends, and market positioning.
- Due Diligence Acceleration: AI-driven natural language processing (NLP) automates the review of financial statements, contracts, and compliance documents, reducing human error and speeding up risk assessment.
- Advanced Valuation Models: Machine learning algorithms enhance financial modeling, providing deeper insights into target company performance, synergies, and risk-adjusted returns.
- Post-Merger Integration Optimization: AI assists in workforce integration, IT system harmonization, and operational efficiency, ensuring smoother transitions post-acquisition.
- Unburden the employees: Use of AI tool reduces manual effort, improve performance and retention.
Adoption rate and the risk of falling behind
- According to a Bain survey of more than 300 M&A practitioners about the use of generative AI for M&A:
- The average adoption rate is 21%.
- It increase to 36% amongst the most active acquirers (than than 1 deal per year consistently).
- PE firms are avid early adopter with 60% affirmed using at least one AI tool
- As more and more adopt the technology and see their performance increase, those who don’t will be disadvantaged and may falling behind.
Graphic: M&A practitioners see potential benefits from generative AI tools at various stages of the deal-making process
Testing & Adjusting: The Time to Experiment Is Now
- AI in M&A is still evolving, and firms that proactively experiment with AI tools today will gain a competitive advantage. The current phase is about:
- Pilot Testing AI in M&A Processes: Organizations should trial AI tools in targeted areas—such as document analysis or deal sourcing—before scaling usage.
- Customization to Organizational Needs: AI models should be refined based on industry-specific requirements and company data structures.
- Human-AI Collaboration: AI should augment—not replace—human judgment in deal-making, with experts overseeing and interpreting AI-driven insights.
Managing Corporate Risk & Data Risk
- While AI offers efficiency and deeper insights, it also introduces new layers of risk. Companies must ensure AI tools are:
- Built on Reliable Data Sources: AI outputs are only as good as the data they process. Using inaccurate, biased, or incomplete data can lead to flawed valuations and misguided deal decisions.
- Aligned with Corporate Risk Management: AI should be integrated into broader risk management frameworks to prevent over-reliance on algorithmic decision-making.
- Monitored for Recursive Data Risks: AI models continuously learn from data, but if they rely on flawed historical deal data or biased sources, they can reinforce bad assumptions and systemic errors. Firms must actively audit and validate AI-generated insights.
- Compliant with Regulatory Standards: As AI regulations evolve, companies must ensure their AI-driven M&A strategies adhere to data privacy, anti-trust, and investment screening laws.
Conclusion In 2025, AI is not yet a “plug-and-play” solution for M&A, but firms that start testing and refining AI tools now—while maintaining rigorous oversight—will be well-positioned to leverage its full potential in deal-making.
