How AI is reshaping M&A due diligence

Is AI simply speeding up dealmaking, or is it transforming the way it is done?

This was one of the central questions I discussed in a recent DealCircle webinar, alongside Philipp Krispin from Nordic Capital, Frederick Krüger from Deloitte, and host Kai Hesselmann. 

Rather than a future vision of fully automated deal execution, a nuanced reality emerged: AI is already deeply embedded in M&A, particularly in due diligence. Yet its impact is less about speed alone and more about transformative changes to work processes.

Here are my key reflections from the discussion.

Due diligence is being fundamentally redefined

Due diligence has always been one of the most labour-intensive phases of transactions. Large teams manually reviewing thousands of documents, extracting data, and responding to buyer questions were standard practice. 

“There are, in principle, massive gains in efficiency,” said Frederik. “A task that used to require a great deal of manual labor but can now be highly automated and standardized. These are the first crucial steps in many analytical processes.” However, he questioned the extent to which AI is firmly established in due diligence or whether it’s still primarily used to accelerate basic tasks.

We also know that due diligence is not becoming simpler. The latest Ideals M&A Outlook 2026 shows that average deal timelines have increased to 264 days, up nearly 30% since 2020, despite technological advancements. The reason is clear: efficiency gains are being eroded by deeper analysis and higher scrutiny from stakeholders and regulators.

AI is therefore not yet reducing the overall workload. It is helping raise the standard.

The data room is becoming the centre of intelligence

One of the most important shifts is the move toward embedding AI directly into virtual data rooms. From our experience at Ideals, we are seeing consistent growth in the use of integrated AI capabilities. 

As I emphasized during the session, the focus is on practical applications integrated into existing workflows, not experimental tools. Purpose-built AI streamlines every phase of the deal, delivering real utility for dealmakers.

The most relevant use cases are already established, ranging from the automated redaction of sensitive data and real-time translation in cross-border deals to AI-assisted Q&A workflows and intelligent search and document summarization. 

Rather than replacing deal teams, these tools enable professionals to shift their focus from manual data handling to interpretation and decision-making.

Security is driving adoption, not holding it back

Data security remains a key concern in M&A, with deal teams recognizing the importance of using secure, closed environments to share information. Within the data room, AI operates under strict principles: it only accesses documents available to the user, does not rely on external data, and does not retain or reuse information.

This is critical, because it enables teams to benefit from AI without compromising confidentiality. As we discussed during the webinar, many deal teams would otherwise use external tools like ChatGPT. Embedding AI directly in the VDR allows them to do so safely and in line with compliance requirements.

Human expertise at the core of AI-enabled dealmaking

Despite rapid technological progress, M&A still relies on human skills and experience. AI can identify risks, summarize contracts, and suggest answers, but it cannot yet replace judgment. 

As I emphasized during our discussion, it’s ultimately dealmakers who are responsible. The guiding principle for all AI applications is that control and responsibility lies with people. Our job is to make the work of AI as safe and transparent as possible.

At the same time, AI is reshaping how deal teams operate. Tasks that once defined junior roles are increasingly automated, raising the bar for skillsets across teams. As reflected in Ideals’ research, while a majority of dealmakers report increased efficiency and faster initial assessments through AI, more than half still see the lack of human nuance and judgment as its main limitation.

The implication is clear: competitive advantage no longer lies in access to data, but in the ability to interpret it more effectively and translate it into well-founded decisions.

A new standard for dealmaking

AI’s impact extends beyond speed. It enhances efficiency, structure, and scalability, while enabling deeper analysis, earlier risk identification, and more informed decision-making. However, many dealmakers are still in the early stages of testing where they can get the most value from the technology.

Notably, a regional divide in AI adoption is becoming increasingly apparent. Our latest research shows that while US deal teams are moving quickly to embed AI into their workflows, European players remain more cautious, often constrained by regulatory and data privacy considerations. 

This divergence is already reshaping deal execution, competitiveness, and ultimately access to opportunities. 

As Philipp noted during the discussion, “AI isn’t something of the future; it’s already here today, and anyone who doesn’t use it now will likely fall behind pretty quickly.”