Smart M&A processes: the real impact of AI

Generative AI is drawing significant attention. While its long-term potential to reshape entire business models is widely acknowledged, its concrete application in M&A processes remains evolving. So what does its current use look like—and what can realistically be expected going forward?

Although practical use is still in its early stages, initial implementations are already delivering tangible benefits—especially in areas historically marked by manual or repetitive work. Forward-thinking companies are now using generative AI to make critical transaction steps more efficient and structured.

Current applications

Generative AI is especially effective in the early stages of M&A processes: identifying potential targets and analyzing large volumes of information during due diligence. It’s also being used to manage virtual data rooms, categorize documents, and streamline Q&A processes.

Real-world examples show clear efficiency gains—especially when the technology is deliberately and systematically integrated into existing workflows: less manual effort, faster processes, and lower costs.

Recognized challenges and limitations

Despite the advantages, there are limitations. AI-generated outputs still require human review—errors in summaries or inaccurate analysis can lead to serious consequences. Public data sources also pose limits in later deal stages, and issues like data privacy and cybersecurity remain key concerns.

It’s also important to note faster execution doesn’t automatically result in better deals. The real value lies in how newly available resources—time and clarity—are used. Generative AI can support decision-making but not replace it.

Three strategic questions for companies

1. Where is the greatest potential?

Focus on processes with high manual effort or a need for creative analysis—such as sourcing targets or preparing tailored offers.

2. How can we build long-term differentiation?

Start preparing your own internal data. Companies that combine proprietary insights with AI tools can set themselves apart in the long run.

3. How can we manage the risks?

Begin with use cases where outputs are easy to verify, supported by clear controls and human review. A structured governance framework is essential.

Conclusion

Generative AI does not replace comprehensive expertise but is becoming an increasingly important tool for enhancing efficiency and quality in M&A processes—especially in data-intensive and repetitive tasks. Companies that proactively focus on targeted use cases early on strengthen their operational excellence and lay the foundation for sustainable differentiation in an increasingly competitive environment.