When asked how AI is being integrated into the due diligence process, Veenman explains that the technology is being developed across various stages of M&A. At KPMG, their own versions of AI-tools are being developed to run within the KPMG-environment to ensure that no data is shared externally. "A lot is being developed, and all parties have a vested interest", he observes. "We’re collaborating with various partners, focusing on tasks like document reviews, generating reports, and data analysis. These are specific parts of the process, and we are gradually moving toward automating the entire process."

The adoption of AI in M&A is not just theoretical: practical applications are already making a difference in private equity firms and M&A-departments. "For example, we see opportunities for large-scale research, competitive analysis, trend identification and historical data review", says Kolkman. “These tools allow organizations to generate detailed reports within a day, or even faster, saving considerable time compared to traditional methods. We’re also integrating AI into our advisory processes, performing scans for companies and providing AI education.”

Rather than just chasing use cases, KPMG tries to identify where AI can create real value in existing processes. “It's about examining standard processes and understanding how AI can help us solve the challenges within them", says Veenman. “We are helping many investors in discovering this value driven opportunities in pre-acquisition stage. The question is: how much value can AI bring to the organization and where are the wins? By supporting them with detailed outside-in AI Value Assessment, the full potential is made visible on optimized processes and impact on the workforce. AI isn't always the solution, so you need to identify where the real challenges for value creation are within your organization.”

AI's ability to process large amounts of data at high speed is one of its most valuable contributions to due diligence. Traditionally, reviewing vast sets of contracts, financial documents, and other critical records could take weeks. AI, however, accelerates this process significantly.

"In terms of due diligence, AI is really making an impact", says Kolkman. “If you upload 100 contracts, AI can identify important clauses, such as change-of-control provisions, almost instantly. This helps in determining which contracts could affect a deal."

Despite AI’s obvious advantages, both Kolkman and Veenman highlight the importance of human intervention in the due diligence process. While AI can accelerate tasks and identify insights that might be missed by human analysts, it cannot replace human judgment, especially when it comes to understanding the nuances of business deals. "Human intervention is still crucial", Kolkman observes. "Due diligence is fundamentally about researching and understanding information. If AI is used to piece together insights, there’s a risk of losing key connections. The human side – asking critical questions based on experience – remains essential."

Veenman adds to that that the shift toward AI-driven processes is changing the role of professionals. "We’re moving towards intelligence workers, where AI provides conclusions, and the human role is to decide how to act on those conclusions. It’s vital to create a feedback loop where humans validate and confirm the AI’s output." This shift requires a cultural change within organizations, with a greater emphasis on training employees to work alongside AI systems effectively.

AI's potential in predictive modelling is another exciting development, particularly when it comes to forecasting a company’s future performance based on current and historical data. Kolkman explains: “We now ask, 'Where are we now?' and compare it to last year’s trends. Is there a seasonal pattern, or is there a significant deviation from the budget? If we spot a deviation, what will it mean for year-end results? Predictive modelling allows us to look further ahead, considering external factors like geopolitical developments that might impact a company’s future sales."

One of the major challenges with AI in M&A is ensuring the quality and security of the data being analysed. "Data quality is crucial. Without good data, AI cannot function effectively", Veenman stresses. He emphasizes that many organizations are focusing on improving data quality, classification, and integration to get the most out of AI. “In many cases AI itself can also play an impactful role to improve data quality and monitoring”, Kolkman adds: "AI can help, but it also presents a risk; if no one is monitoring what’s happening in the data processing 'black box’, errors may go unnoticed."

In light of the increasing reliance on AI, both experts underscore the need for strong data governance. Kolkman highlights the importance of traceability: "Our role is to validate the data, and if that is AI’s output, ensuring that we can explain how the analysis was derived. It’s crucial to stay grounded in the original data and ensure traceability."

Another important consideration is the danger of over-relying on internet data. “Many AI models have been trained on web data, but such data can be incomplete or unreliable", says Veenman. "If we continually train models on that same data, we risk creating a feedback loop based on a world shaped by AI itself. We now see projects like OpenAI’s SORA, enabling humans to create high quality videos based on text, in which videos pop-up with flying pigs, armadillo bunnies and cats with snail-tails that are so realistic they could fit David Attenborough’s documentaries. This raises questions about what remains true. While AI is powerful, it lacks wisdom, so it’s critical to complement it with our own data. By combining the ‘thinking power’ of AI with verified internal data, we can create a smarter, more reliable foundation for decision-making and boost the digital IQ of our organization.”

Looking ahead, Kolkman and Veenman both see AI playing an even greater role in M&A, with applications evolving alongside technological advancements. "AI will undoubtedly reduce costs and processing time, giving us more opportunities to analyse deeper data points and make better decisions", Kolkman says. However, both acknowledge that AI still has its limits, particularly when it comes to the human aspects of deal-making, such as cultural fit and leadership alignment: areas where human judgment will always be essential.

AI is not a one-size-fits-all solution, and companies must be strategic in how they implement these technologies. As Veenman notes: "It’s about finding the balance between external tools and integrating them with our own data and knowledge. With our clients we are working on developing central platforms turning this knowledge and experience into high quality data sources and combining this with smart, persona-driven internal GPT solutions. This brings the power of AI together with functional and smart applications for better information delivery and analyses." By doing so, companies can unlock AI’s full potential while maintaining a reliable, human-driven approach to deal-making.

In conclusion, the integration of AI into the M&A due diligence process is already delivering significant benefits in terms of speed and efficiency. However, as Kolkman and Veenman emphasize, AI works best when used in tandem with human expertise, ensuring that complex decisions are made with both speed and insight. As technology continues to evolve, the future of AI in M&A holds great promise, particularly in predictive modeling and data-driven decision-making, but its success will always depend on the people behind the tools and the quality of data.