Limitations of Generative AI for Due Diligence Research

In the previous part of this series, we explored how Generative AI (genAI) is transforming due diligence, enabling real-time risk monitoring, enhancing internal research tools, and even launching new investigative products. AI has made due diligence faster and more scalable, but it is not without limitations.

While AI can rapidly process large amounts of data, it lacks the ability to assess source credibility, interpret reputational nuances, and navigate non-digital intelligence. Investigative due diligence requires more than just automation; it demands human expertise to validate findings, uncover hidden risks, and provide context that AI alone cannot grasp.

In this part, we examine where the limitations of AI for due diligence research.

Understanding Sources

AI has transformed how we gather intelligence, rapidly extracting and categorizing risk events across multiple sources and languages. This efficiency allows investigators to cover more ground while reducing costs. However, AI struggles with a fundamental challenge: understanding the credibility and context of its sources.

Not all sources are reliable. Some carry biases, and AI, no matter how advanced, can’t always distinguish between credible and misleading information. AI can flag articles, reports, and social media posts, but it lacks the discernment to assess hidden agendas, outdated data, or misinformation. Investigative work depends on accuracy, and while AI can surface information, human judgment is still necessary to determine its trustworthiness.

Nuanced Judgment

Despite concerns about AI replacing investigators, the reality is the opposite. AI shifts professionals' focus to areas requiring deeper analysis, strategic thinking, and real-world interactions.

AI can summarize facts, but it lacks the ability to fully interpret human behavior or reputational nuances. Furthermore, AI cannot replace the expertise developed through years of investigative experience—the ability to connect seemingly unrelated pieces of information, read between the lines, and anticipate risks that are not explicitly documented.

Investigators must weigh conflicting pieces of evidence, apply professional skepticism, and make informed decisions based on a mix of hard data and human insight.

Anything Non-Digital

AI is powerful when it comes to scanning online records, news reports, and databases, but it has blind spots. Many critical insights exist outside the digital realm, in personal networks, private conversations, and confidential sources. It cannot conduct interviews, verify a subject's credibility through personal interactions, or recognize red flags that might be apparent in tone, hesitation, or nonverbal cues.

The Future: A Hybrid Approach

The future of due diligence isn’t AI vs. human intelligence—it’s both, working together. AI’s ability to process massive amounts of data is a powerful asset, but human oversight ensures accuracy, ethical judgment, and contextual understanding.

The best approach is a hybrid one—using AI for efficiency while still relying on human judgment. By striking this balance, investigators can harness the best of both worlds: the scale of AI and the insight of human intelligence.

How to Get Started

DiligenAI is our AI-driven solution that helps due diligence firms streamline their research process. By combining large language models (LLMs) with open-source data, DiligenAI automates the search for risk intelligence, providing faster, more accurate, and more cost-effective results.

With DiligenAI, firms can scope investigations more efficiently, deliver timely client updates, and ensure that no critical information is missed—all while reducing costs. It’s not about replacing human expertise but enhancing it, allowing investigators to do more with less and focus on higher-level analysis. DiligenAI is designed to make due diligence smarter, faster, and more comprehensive