AI in Due Diligence: What It Means for M&A and Beyond

Due diligence and deal velocity in investment firms

Let’s start by examining the current state of AI implementation among investment firms.

Although overall adoption remains limited, large firms are leading the way. 29% of these firms are actively implementing AI tools to enhance their due diligence processes, while 19% have adopted intelligent data extraction tools to handle document-heavy tasks more efficiently.

In contrast, small and medium-sized firms lag significantly behind, with only 3% adoption for both AI and IDP technologies. This gap highlights the resource and scalability challenges smaller firms face when trying to incorporate cutting-edge tools into their operations.

This stark disparity highlights the resource and expertise gap. But, let’s get back to the core issue at hand.

Due diligence, a cornerstone of mergers and acquisitions (M&A), has long been an exercise in patience, precision, and endurance. Traditionally, the process usually stretches across a minimum of 60 days, demanding a painstaking review of financial statements, contracts, tax records, and regulatory documents. EY points out that many companies still store key documents in physical formats, adding manual effort and delays to what is already a high-stakes exercise.

The costs are equally daunting. With M&A expenses typically ranging between 1% and 4% of the deal size, due diligence alone can rack up millions in fees, reflecting not just the complexity of the work but the inefficiencies inherent in current practices.

But the challenge runs deeper than inefficiency. As Structize explains: due diligence today demands a shift from exhaustive document reviews to strategic inquiry—asking the right questions and uncovering pivotal insights faster. This shift isn’t about cutting corners but rethinking priorities to focus on the factors that can make or break a deal.

The modern M&A landscape also demands analysis beyond financials and legal compliance. Evaluating a target’s technology, cybersecurity, and ESG commitments has become just as critical as reviewing its balance sheet. Research shows that while buyout firms meticulously perform tech due diligence for software companies (representing just 15% of deals), only 9% of general buyouts receive the same scrutiny. This despite the fact that 31% of all buyouts last year involved technology companies.

Companies note that these dimensions are now central to assessing value, making traditional methods increasingly unfit for purpose. The complexity of modern due diligence extends beyond just document review. "Just as the tech function itself has moved out of the back room to become a strategic centerpiece for many companies, the most effective way to evaluate a target's technology capability has had to evolve as well"  (Bain & Company). This evolution also demands new tools and approaches that can handle the increasing sophistication of modern business transactions.

AI extracting data from documents in V7 Go

Here’s where AI enters the picture. Far from being a gimmick, AI addresses the core friction points in due diligence: speed and accuracy. It can perform AI contract reviews, flag risks, and surface anomalies in hours, not weeks, enabling teams to focus their expertise where it counts most. AI has evolved from being experimental to essential.

This is not just a trend but a response to real pressures. In a competitive market where delays can mean lost opportunities, AI provides the edge that firms need to act decisively while maintaining rigor. For firms unwilling to adapt, the cost isn’t just inefficiency—it’s the risk of fading into irrelevance in an accelerating deal-making landscape.

Benefits of using AI to increase deal velocity

Integrating AI into due diligence transforms deal execution by improving speed, accuracy, and scalability while reducing risk. Deloitte research highlights how AI can slash timelines that traditionally exceed 2 months. Centerline is a good example of boosting productivity with AI. By leveraging GenAI tools like V7 Go for data extraction and automated document analysis, the company increased their productivity by 35% in just one month.

AI also enhances accuracy by systematically analyzing vast datasets, spotting risks and anomalies that manual reviews might overlook. It ensures consistency across documents and flags patterns indicative of deeper issues, providing firms with a clearer risk landscape. For complex transactions, AI scales effortlessly, handling cross-border regulatory analysis, multiple deal streams, and diverse document formats, making it indispensable for modern M&A.

Beyond efficiency, AI’s advanced analytics delve into areas like fraud detection, regulatory compliance, and cybersecurity vulnerabilities. By uncovering these risks early, firms not only accelerate their processes but also mitigate potential deal-breaking issues. Together, these advantages make AI a critical asset in executing faster, more informed, and safer deals.

How AI and automation are simplifying due diligence

The transformation from traditional to AI-driven due diligence represents a fundamental shift in how investment firms approach deal evaluation. AI's ability to mine large amounts of documents, contracts, and financial data isn't just about speed – it's about identifying patterns, anomalies, and inconsistencies that might otherwise go unnoticed.

Key AI capabilities in modern due diligence:

The technology has evolved far beyond simple document scanning. As Corporate Compliance Insights reports, modern AI systems can now analyze patterns across various data sources, providing deeper insights into potential risks and opportunities. This comprehensive analysis helps firms make more informed decisions while significantly reducing the time and resources required for thorough due diligence.

Can all of these tasks be fully automated?

To some extent.

Can due diligence be automated with AI?

Let’s take a look at due diligence in a different industry—logistics.

AI plays a pivotal role in modern supply chain due diligence, but its capabilities must be complemented by human expertise. As Samuel Logan explains, while AI efficiently identifies surface-level risks—such as spotting potential "red flags" in vast multinational supply chains—it struggles with interpreting complex relationships and accessing critical local data. For example, understanding the connections between supply chain actors often requires nuanced, contextual insights that AI cannot provide alone. To address these gaps, a hybrid approach is essential. AI streamlines the process by scanning and organizing data, but human investigators bring essential local knowledge, cultural understanding, and on-the-ground access.

Similarly, in M&A and investment due diligence, AI can quickly identify potential risks, such as financial irregularities or regulatory concerns, by analyzing vast datasets. However, interpreting these findings—such as understanding how a flagged financial trend impacts broader investment strategy or uncovering subtleties in market conditions—requires the critical thinking and judgment of experienced professionals.

As you can see, human oversight of the process is as necessary as ever, even if only for the final review:

If traditional due diligence is like trying to find a needle in a haystack, modern AI-powered due diligence is like using a metal detector. The tools make the process much faster and more efficient, but you still need to understand what you're looking for and confirm whether what you’ve found is the right match.

But where do you actually start?

How to automate due diligence with AI

AI isn’t about replacing human judgment. It’s about enhancing it. At its core, AI addresses the fundamental bottlenecks in due diligence: speed, accuracy, and scalability. AI due diligence platforms like V7 Go integrate traditional tools with generative AI technologies—such as Optical Character Recognition (OCR), Large Language Models (LLMs), Python code, and workflow automation—to transform deal evaluation processes.