I became obsessed with detective stories and forensic work as a young person. While my career has taken me into corporate finance and investment, I enjoy the detective aspect of due diligence. Traditional due diligence includes not just checking documents, but also interpreting evidence, providing insights and understanding why things are the way they are. Sometimes due diligence can become very tick-box, meaning no real thought going into why there are risks in place and how they affect the business. From my experience of due diligence within both large corporates and early-stage ventures, I have noticed that the documents and information requested do not always reflect the true quality of the business.
Traditional due diligence is not only about checking documents or completing checklists. It also includes interpreting evidence, understanding context and generating insights that support better investment decisions. In practice, checklist-driven processes can sometimes limit deeper, introspective thinking about why risks exist, how they affect a business and whether they can be addressed. Having assessed large corporates and early-stage ventures across different investment contexts, I have observed that documents and information required do not always reflect the underlying strength of a business.
Many successful ventures start as sole proprietorships, where the entrepreneur is responsible for managing customer acquisition, supplier relations, finances and daily operations. This means they will often focus on keeping cashflow flowing, delivering orders and growing their customer base. The business will often develop into having proper reporting systems and good management teams once they have enough resources. This can mean that although the business may have good growth and cashflow, they do not have all the documents and information needed.
The reverse can be equally concerning. I have encountered established, reputable corporates with substantial financial resources and formal documentation where related-party exposures and intercompany lending arrangements required closer examination to establish their actual financial position.
Although these situations differ, they illustrate the same limitation: relying on a due diligence checklist of documents and information alone cannot establish the economic reality of a business. In one case, inadequate records may obscure commercial potential; in the other, apparently comprehensive records may fail to reveal material financial exposures or the ability to grow and repay. For investors, the challenge is twofold: to distinguish genuine business weaknesses from gaps in institutional readiness, and to identify risks that may not be apparent from the information initially presented.
Artificial intelligence and advanced analytics could help investors analyse larger volumes of information, reconcile data from different sources and identify relationships that warrant closer investigation. More importantly, AI could help move due diligence beyond a checklist-based exercise by prompting investors to explore patterns, test assumptions and ask more fundamental questions about the business.
Using artificial intelligence and advanced analytics can help investors to analyse more information, match up data and understand the relationships within the data. Most importantly, it can help move due diligence away from a tick-box exercise and encourage investors to think about patterns and ask more questions.
Automated matching and anomaly detection can help spot discrepancies between financials, bank statements and transactions. Network analysis can also help look at related party relationships. Financial modelling can help understand the impact of concentration risk, working capital and financing. Having everything in one place can help investors make more informed decisions.
Consider a company reporting strong revenue growth alongside deteriorating cash conversion cycles and increasing dependence on a few customers. An AI-assisted assessment could connect these indicators, examine their underlying drivers and test how different collection policies, operating assumptions, or financing arrangements might affect future cash flows. The resulting analysis could inform an investor’s valuation assumptions, financing structure or investment conditions. It might also identify operational improvements that management would need to implement before or after an investment.
This is where due diligence begins to contribute to a growth strategy. The process does not merely identify a weakness; it evaluates its financial consequences, whether it can be corrected and how addressing it could strengthen the business and support improvements in governance policies.
From looking at international investment practices, we can see that AI is becoming a key tool used within deal analysis. According to a study by Datasite and FT Longitude in 2026, Half of those surveyed (1000 senior dealmakers in 27 countries) use AI regularly as part of their due diligence. Tools such as Datasite Diligence can help analyse information using AI within virtual data rooms. DealRoom can help by providing AI-supported due diligence tools and analysis.
Litera’s Kira helps with legal due diligence by using machine learning to identify and extract contractual clauses. AlphaSense can help with commercial due diligence by providing AI-assisted research. Investors can use these tools to understand the competitive landscape, industry news, and validate the commercial assumptions of the target company. The platforms provide different analysis but show how AI can help investors to understand more about the evidence behind an investment.
The broader industry discussion is also shifting towards value creation. Alvarez & Marsal’s 2026 European due diligence research reported that private equity investors were increasingly considering AI as a potential source of operational and commercial value within prospective investments.
There is a big difference between using AI for due diligence and looking at how AI can create value within the business. Whilst both are relevant, the true value within due diligence comes from using the financial, legal, operational and commercial information to make decisions on terms, risks and future performance.
For me, the most important question is not whether AI can make due diligence faster, but whether it can make the resulting assessment more useful. AI could support this process by connecting information across different areas of due diligence and helping investors evaluate alternative scenarios. It could also challenge checklist-driven thinking by surfacing relationships, inconsistencies and questions that may not emerge from a conventional review. For businesses that are commercially promising but not yet investment-ready, such assessments could provide a clearer indication of the improvements required before financing becomes viable.
However, AI can’t validate the authenticity of documents, ensure they haven’t hidden any obligations or replace expert judgement. Legal, environmental and other analysis will still need an expert and reliable evidence.
Overall, this isn’t replacing traditional due diligence but improving it. Due diligence shouldn’t just tell you if the business is a good investment but also give insight into what risks can be mitigated, what improvements can create value and what would make it a more investable business.
This is where I feel AI could really add value to the process by helping investors to look past the red flags.
The views expressed in this article are solely those of the author
The writer is a corporate finance and investment professional
