AI Risk Is Becoming a Diligence Issue

Companies do not need perfect AI governance overnight. But if they are using AI in the business, product, or operations, they should be prepared to answer basic questions from investors, buyers, customers, and strategic partners. 

AI is no longer just a product or technology issue. 

It is becoming a diligence issue. 

Companies are using AI across product development, customer support, marketing, sales, engineering, operations, recruiting, legal, finance, and internal workflows. Some companies are building AI into their core product. Others are using third-party AI tools behind the scenes. Many are doing both. 

That creates real business opportunity. 

It also creates questions. 

Investors, buyers, customers, strategic partners, and enterprise procurement teams are increasingly asking how companies use AI, what data is involved, what controls exist, and whether the company can support the claims it is making about its technology. 

For many companies, the issue is not that they are using AI. The issue is that their AI use has grown faster than their documentation, policies, contracts, and governance. 

That gap can become visible during diligence. 

AI diligence is not only for AI companies 

AI diligence is easy to think of as something that only applies to companies building AI models or selling AI-native products. 

That is too narrow. 

A company does not need to be an AI company to have AI diligence issues. 

A software company may use AI features inside its product. A services company may use AI tools to analyze customer information, summarize calls, draft documents, or support internal operations. A marketing team may use AI to generate content. A customer success team may use AI to process support tickets. An engineering team may use AI coding tools. A leadership team may use AI tools to evaluate data, strategy, or financial information. 

Each use case may be reasonable. 

But during diligence, the questions are usually practical: 

• What AI tools are you using? 

• Who approved them? 

• What data goes into those tools? 

• Are employees allowed to input customer, confidential, personal, or proprietary information? 

• Do your contracts allow that use? 

• Do your privacy policy and internal practices match? 

• Are your AI-related product claims accurate? 

• Do you own or have rights to the outputs you rely on? 

• Are third-party AI vendors subject to appropriate contract terms? 

• Do you have a policy governing employee use? 

• Can you explain how the technology works at a level appropriate for customers, investors, or buyers? 

If the company cannot answer those questions, the issue may shift from innovation to risk. 

The real risk is not AI use. It is unmanaged AI use. 

Most companies do not need a complex AI governance program on day one. 

But companies do need enough structure to know what is happening inside the business. 

Unmanaged AI use creates several categories of risk. 

First, there is data risk. Employees may input customer information, confidential business information, source code, personal data, financial information, or proprietary materials into tools that were never approved for that purpose. 

Second, there is contract risk. Customer agreements, vendor agreements, data processing agreements, confidentiality obligations, or industry-specific requirements may limit how certain data can be used or shared. 

Third, there is intellectual property risk. If AI tools are used to create code, marketing assets, product materials, written content, or other business outputs, the company should understand whether it has sufficient rights to use, modify, commercialize, or protect those outputs. 

Fourth, there is product and marketing risk. If the company describes itself as AI-powered, proprietary, automated, intelligent, secure, unbiased, or uniquely trained, those claims should be accurate and supportable. 

Fifth, there is security risk. AI tools can introduce new vulnerabilities, especially if the company does not understand what systems are connected, what data is shared, or how outputs are reviewed. 

Finally, there is governance risk. If no one owns AI oversight, no one may be responsible for making sure the company’s AI use aligns with its contracts, policies, privacy obligations, security posture, and business claims. 

Why this matters in financings and M&A 

Diligence is where informal practices often become visible. 

A company may have adopted AI quickly because the tools were useful, accessible, and inexpensive. Teams may have moved faster than leadership. Different departments may be using different tools. Some use cases may be approved. Others may be informal. Vendor contracts may not have been reviewed. Employees may not know what data they can and cannot input. Product and marketing claims may have evolved without legal or technical validation. 

That may not create a crisis in day-to-day operations. 

But it can create friction when an investor, buyer, customer, or strategic partner starts asking diligence questions. 

In a financing, investors may want to understand whether AI creates legal, regulatory, IP, data, or cybersecurity exposure. 

In an acquisition, a buyer may want to know whether the company’s AI-related assets are actually protectable, transferable, and supported by adequate rights. 

In an enterprise customer negotiation, a customer may ask whether its data is used to train models, whether AI vendors have access to its information, or whether human review is required. 

In a strategic partnership, the other party may want to understand whether AI outputs, data rights, and ownership are clearly allocated. 

If the company is not prepared, diligence can slow down. The company may need to reconstruct AI use across teams, review contracts under time pressure, update policies, revise product claims, or negotiate additional protections. 

That is exactly the type of avoidable friction companies should try to address early. 

What companies should be able to answer 

A practical AI readiness review does not need to start with a complicated framework. 

It can start with basic questions. 

• What AI tools are currently used across the company? 

• Which tools are approved, and which are informal? 

• Who owns AI oversight internally? 

• What types of data may employees input into AI tools? 

• Are customer data, personal information, confidential information, source code, or proprietary materials restricted? 

• Do customer contracts, vendor contracts, or data processing agreements limit AI use? 

• Are any AI tools embedded in the company’s product or service? 

• Does the company make AI-related claims in sales materials, investor decks, marketing copy, customer contracts, or website language? 

• Can those claims be supported? 

• Does the company rely on AI-generated code, content, analysis, or other outputs? 

• Are there human review procedures for important AI outputs? 

• Are third-party AI vendors reviewed for security, privacy, confidentiality, and data-use terms? 

• Do employees have a written AI use policy or practical guidance? 

• Are AI-related risks addressed in the company’s broader privacy, cybersecurity, IP, and commercial contracting processes? 

These questions are not designed to stop AI adoption. 

They are designed to make AI adoption more disciplined. 

AI claims deserve special attention 

One area that deserves particular care is how the company talks about AI. 

Companies are under pressure to show that they are innovative. Investors, customers, and strategic partners want to understand whether AI creates a competitive advantage. That can create a temptation to use broad language before the company has fully validated what the technology actually does. 

But vague or overstated AI claims can create risk. 

If a company says its product is AI-powered, proprietary, automated, secure, unbiased, trained on unique data, or capable of delivering a certain result, it should be prepared to explain what that means. 

The company should also make sure its public statements, investor materials, customer-facing materials, and internal technical reality are aligned. 

This does not mean every statement needs to be loaded with caveats. It means leadership should know which claims are supportable and which claims need to be tightened before they appear in diligence materials, investor decks, customer contracts, or acquisition discussions. 

The goal is practical readiness, not perfection 

AI governance can sound overwhelming. 

It does not need to be. 

For many growth-stage companies, the right starting point is practical visibility. 

Know what tools are being used. 

Know what data is involved. 

Know which contracts matter. 

Know what claims the company is making. 

Know who owns oversight. 

Know which issues need to be cleaned up before a financing, acquisition, major customer negotiation, or strategic partnership. 

That level of readiness can help the company move faster, answer diligence questions with more confidence, and avoid turning manageable AI issues into late-stage transaction friction. 

AI risk is becoming a diligence issue. 

Companies that treat it as a business readiness issue, rather than just a technical issue, will be better positioned when investors, buyers, customers, and strategic partners start asking harder questions. 

How CGL can help 

CGL helps companies evaluate AI-related legal and business readiness in the context of financing, M&A, commercial contracting, privacy, cybersecurity, product strategy, and governance. 

A focused AI readiness review can help identify how AI is being used, where documentation or contract gaps may exist, whether product and marketing claims are supportable, and what should be addressed before diligence begins. 

If your company is using AI in its product, operations, customer workflows, or internal systems, CGL can help assess the legal and strategic issues that may matter before investors, buyers, customers, or partners start asking.

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