AI Due Diligence: The New Must-Have Section in Every M&A Information Memorandum

Target companies are now being scored on their AI maturity. Here's what acquirers are asking, and how to prepare a target's AI posture for scrutiny.

If you're preparing a company for sale in 2025 and your information memorandum doesn't address AI, you're walking into a management presentation with a gap that sophisticated buyers will find in the first hour.

AI due diligence has moved from a niche concern in tech deals to a standard line item across private equity, infrastructure, and mid-market M&A. Acquirers aren't just asking whether a target uses AI — they're scoring it. And the answers are directly affecting valuations, deal structures, and whether earnouts get triggered.

Here's what's driving the shift, what buyers are actually asking, and how to get a target positioned before the process launches.

Why AI Posture Is Now a Valuation Input

The deal market is unambiguous on this. Global M&A deal value hit a record $4.9 trillion in 2025, with nearly half of all technology deals carrying an AI component — up from roughly one in four just a year earlier.

The valuation gap between AI-mature and AI-lagging targets is widening fast. In 2025, 63% of targets that buyers evaluated had only limited AI use — a chatbot, predictive scoring, or a narrow automation feature. Just 26% qualified as genuinely AI-driven. Yet those same buyers expect 61% of companies they evaluate in 2026 to be AI-driven. That is a 35-point expectation gap.

The consequence for sellers is direct: companies that can't demonstrate credible AI integration are facing valuation discounts, heavier earnout structures, and increased reps and warranties exposure. Higher valuation of companies with mature human-AI collaboration frameworks and growing importance of ethical AI governance in acquisition targets are now explicit new due diligence categories.

For non-tech targets — manufacturers, infrastructure operators, professional services firms — the question is no longer "are you an AI company?" It's "how AI-ready is your operating model, and how quickly could an acquirer extract value from it?"

What Acquirers Are Actually Asking

The AI diligence questionnaire has evolved significantly from a year ago. Based on current deal practice, here are the five areas where buyers are now applying structured scrutiny:

1. Data Infrastructure and Proprietary Data Assets

Buyers are asking targeted questions: What proprietary datasets does the target own or have rights to, and how permissioned and traceable is that data? How were the models trained, and how does performance hold up under adversarial stress-testing and edge-case evaluation?

For non-tech targets, this translates to: how clean is the operational data, how accessible is it via API, and does it have the structure required to train or fine-tune AI systems post-acquisition?

2. AI Tool Inventory and Shadow AI Exposure

Buyers are running shadow AI audits as part of technical diligence. Untracked LLM usage in finance, operations, or customer-facing workflows creates IP, data privacy, and regulatory liability that will surface in rep and warranty negotiations. A target with no AI governance documentation is a higher-risk asset.

3. Model Dependencies and Vendor Lock-In

AI companies face unique due diligence scrutiny around inference costs, model dependencies, and compute commitments. Buyers will examine gross margins with AI costs fully loaded and want clarity on which models the target depends on, how cost structure changes at scale, and whether there is vendor lock-in risk with cloud or model providers.

4. Workflow Integration Depth

A chatbot bolted onto a website is not AI integration. Buyers are distinguishing between surface-level AI adoption — where AI tools sit alongside existing workflows — and deep integration, where AI outputs are embedded in decision-making, reporting, or operational processes. The McKinsey AI maturity framework scores targets on this spectrum, and deal teams are increasingly using structured scoring tools to quantify it.

5. AI Governance and Compliance Posture

Transactions involving AI, sensitive data, and critical compute infrastructure increasingly face heightened regulatory scrutiny and extended review timelines. Buyers want to see a documented AI policy, an AI tool registry, output review protocols, and evidence that the governance framework will survive integration. The absence of these is flagged as a post-close integration risk — and priced accordingly.

The Deal Structure Implications

AI due diligence findings aren't just informational — they're directly shaping transaction economics.

Common structuring tools now include earnouts tied to AI-related metrics, with additional consideration payable only if the target achieves defined performance benchmarks, deployment milestones, revenue thresholds, or compute-efficiency goals. Buyers may also hold back a portion of the purchase price through escrows, to mitigate the risk of technical underperformance or data rights issues that surface post-closing.

For infrastructure and services businesses where AI isn't core to the value proposition, the risk is different: being perceived as AI-unprepared creates a narrative that post-acquisition value creation will require heavier investment than the acquirer modeled. That perception compresses the entry multiple.

How to Prepare a Target's AI Posture Before Process Launch

The good news: AI posture can be materially improved in 60 to 90 days with the right focus. Here's what should be in the IM and the VDR before the first buyer call:

Build an AI Tool Registry — Document every AI system in use across the business: vendor, use case, data inputs, output review process, and governance status. This is the single most important artifact a buyer wants to see. Its absence suggests either disorganization or unacknowledged risk.

Write a One-Page AI Policy — Drafted, signed, and dated by a senior executive. It doesn't need to be comprehensive — it needs to exist and be specific about data handling, approved tools, and human review requirements for reportable outputs.

Document Your Proprietary Data Assets — AI valuation isn't about how much data you have or how many models you've trained — it's about use-case clarity, market pull, and integration value. For non-tech businesses, this means structuring the data narrative around what an acquirer could build with your operational data post-close.

Clean Up Shadow AI Exposure — Run an internal audit before buyers do. Any material unapproved AI usage touching customer data, financial records, or regulated workflows should be formalized or discontinued before process launch.

Frame the AI Opportunity Section — The IM should include a dedicated section positioning the target's AI roadmap as a value creation lever for the acquirer. Forty percent of respondents in a McKinsey survey report that gen AI enabled 30 to 50 percent faster deal cycles, and respondents using gen AI in M&A report an average cost reduction of roughly 20 percent. Buyers want to see that the target management team understands where AI can accelerate the business — not just what AI they use today.

The Bottom Line

AI due diligence is no longer a checkbox for tech acquisitions — it's a standard section of the buy-side diligence playbook across industries. Targets that can demonstrate AI maturity, documented governance, and a credible roadmap will command better multiples, cleaner deal structures, and faster process timelines than those who treat AI posture as an afterthought.

The information memorandum is the first place buyers form an opinion. Make sure yours doesn't leave the AI section blank.

At Cell Fusion Solutions, we help finance and operational teams prepare AI posture documentation — tool registries, governance frameworks, and AI narrative sections — for companies entering M&A processes. Built for the deal timeline, not the strategy offsite.