Artificial intelligence is accelerating due diligence in M&A, helping deal teams process more data, flag risks earlier, and maintain momentum in competitive processes.
AI is now included in core workflows, from financial modeling and contract analysis to market sensing and operational benchmarking.
According to EY, it’s not just improving efficiency; it’s also uncovering insights that traditional methods might miss. In people- and service-driven sectors like financial advice, where value relies on teams, platforms, and process quality, buyers are even using AI to assess qualitative dimensions.
Still, the fundamentals haven’t changed: context, discretion, and human judgment remain central. As founders prepare for exit and dealmakers negotiate complex transactions, understanding how to balance AI tools with experience-led insight is becoming essential.
AI due diligence: What is it?
AI due diligence is the use of artificial intelligence, especially generative AI and machine learning, to support how deal teams assess a target company. At its core, it’s about helping people work faster and smarter by analyzing large volumes of information that otherwise would take days or weeks to review manually.
Instead of replacing analysts, AI tools assist them. They can organize documents, flag anomalies in financials, surface regulatory risks, and spot trends in customer data. That means teams can focus less on sorting through information and more on interpreting what matters.
In practice, AI is becoming a layer built into the diligence process, as it adds speed and scale.
Read: AI rollups in 2025: What founders need to know
Use cases of AI in the due diligence process
Below are four key areas where AI tools are reshaping how teams analyze and synthesize information under pressure.
Financial and operational diligence
AI can quickly parse financial statements to identify trends in revenue quality, margin stability, and cost structures. What once required hours of spreadsheet analysis can now be done in minutes, allowing analysts to focus on implications rather than mechanics.
More advanced tools can also support scenario modeling, like adjusting for one-time expenses or non-recurring revenue, to help teams build cleaner, management-adjusted EBITDA models and quickly test how the business performs under different assumptions, like slower growth or higher churn.
Commercial diligence
On the commercial side, AI helps surface customer insights from data that’s often overlooked like online reviews, CRM logs, and support tickets. Natural language processing (NLP) can extract sentiment signals, spot churn risks, and identify gaps in the value proposition.
It also supports customer segmentation and pricing analysis, helping teams understand product-market fit and go-to-market efficiency more clearly.
Legal and compliance
In legal diligence, AI-powered NLP tools can summarize key contract terms, flag indemnity clauses, and surface change-of-control provisions across thousands of pages. This accelerates review without sacrificing accuracy.
Cross-border deals also benefit from automated scanning for regulatory red flags and jurisdiction-specific risks, areas where manual review can be both slow and error-prone.
Cultural and organizational analysis
Culture fit is often difficult to quantify, but AI is starting to help. By analyzing public employee sentiment from platforms like Glassdoor or LinkedIn, AI can offer early indicators of morale, leadership effectiveness, and turnover risk.
It can also map organizational structures, assess policy consistency, and evaluate whether internal processes align with what’s being represented during the deal.
How the diligence process is changing with AI tools: 5 examples
As deal timelines tighten and information volume expands, AI-powered tools are redefining how diligence gets done. From parsing contracts to modeling valuations, these platforms help deal teams move faster, reduce risk, and uncover insights that manual workflows often miss.
Below are 5 examples of tools that sellers and buyers can use for the due diligence process.
1. Valutico: Automate valuation analysis
A valuation and benchmarking platform that uses AI to automate company analysis, risk diagnostics, and peer comparisons. Deal teams use it to accelerate financial modeling and scenario planning in early-stage assessments.
2. Kira by Litera: Review contracts at scale
Uses NLP to extract key clauses and risks from thousands of contracts, accelerating legal review while reducing human error.
3. AlphaSense: Extract market insights from filings
Searches earnings calls, investor reports, and regulatory documents using natural language AI to surface competitive and sector signals.
4. Grata: Source private company targets
Enables targeted sourcing with filters like tech stack, headcount growth, and GTM model—ideal for proprietary deal flow.
5. Dealroom.co: Track private markets and sector trends
Combines structured data and predictive analytics to identify startups, fast-growing sectors, and ecosystem dynamics.
Looking ahead: AI due diligence as standard practice in future deal execution
AI is on track to become embedded in every stage of the M&A process. According to a survey from Bain in February 2025, about one in five companies currently use generative AI in M&A processes, and more than half expect to integrate it into their dealmaking by 2027.
If you are looking to sell your company or buy one, consider the following:
1. AI will be standard in deal execution
Private equity and corporate buyers are rapidly integrating AI to streamline diligence, identify risk faster, and evaluate targets with greater depth. It will soon be a baseline requirement, not a differentiator.
2. Buyers will expect AI-ready targets
Founders can expect increased scrutiny on data hygiene, process documentation, and system visibility. If a business lacks structured, machine-readable information, it risks being undervalued or overlooked entirely.
3. Founders must prepare for AI-enhanced diligence
Expect workflows that combine traditional legal, financial, and operational review with AI-driven tools that surface red flags and benchmark performance at scale.
4. Human insight remains the differentiator
AI can accelerate discovery, but interpreting results, assessing strategic fit, and navigating negotiation still rely on human judgment. The advantage will belong to teams that combine both.
Prepare for AI due diligence with the right mindset
AI might be changing how diligence gets done, but the fundamentals still matter. Speed and scale are only useful if your data is clean, your processes are documented, and your team knows how to act on what the tools reveal.
For founders, being AI-ready means having disciplined operations and audit-ready data that can stand up to deeper, faster scrutiny.
AI brings speed and scale. But the real value comes from how you use it. At L40, we apply it where it matters, so we can focus on what still requires human insight. Contact us.
