Is Your Data and AI Strategy Ready for Due Diligence?

Buyers and investors are no longer satisfied with tax returns, a cap table, and a stack of key contracts. An emerging key question is how your business collects, uses, and protects data, and where AI shows up in that picture. This does not just apply to software or tech companies; even “offline” businesses like manufacturers, professional services firms, and trades now rely on customer data, operational data, and AI-enabled tools in ways that can materially affect risk and value.​

For Michigan companies, informal or undocumented data and AI practices are increasingly treated as a risk factor that can show up as a price discount, a holdback, or tougher indemnity language in the purchase agreement. This is just as true for a plumbing company using AI to handle customer inquiries or screen resumes as it is for a SaaS platform training models on user behavior, because in both cases the buyer is inheriting whatever data, AI, and compliance risk the seller has built into the business.​

Data and AI are now core value drivers, not technical footnotes. If a material part of your product, service, or operations depends on data or AI tools, buyers will probe whether those foundations are stable, compliant, and transferable, or whether they carry regulatory, IP, or cybersecurity baggage that they will have to fix later. A company that can show disciplined, documented practices around data and AI looks more “deal ready” than a similar company that relies on verbal rules and scattered emails.​

What buyers and investors look for

In practical terms, due diligence teams are asking three sets of questions.​

AI usage and risk

They want to know which AI tools you use (internal and third-party), what data flows into those tools, and whether that use could leak confidential information, weaken IP protection, or create bias or compliance issues. They will ask whether anyone has evaluated these tools from a legal and risk perspective, or whether they were simply adopted by individual teams on an ad hoc basis.​

Data sources and ownership

Buyers will dig into where your core datasets come from, what rights you have to use them, and whether any licenses, consents, or terms of service could limit ongoing use after closing. If your product or analytics depends on scraped, purchased, or user-generated data, they will test whether that use is clearly authorized and supported by contracts and privacy disclosures.​

Cybersecurity and contractual safeguards

Due diligence now routinely includes questions about security controls, incident history, and how those practices are reflected in your contracts with customers and vendors. Gaps between what your contracts promise and what your internal practices actually look like are seen as potential liabilities that affect both valuation and deal structure.​

For many growing Michigan businesses, none of this is about bad intent. It is about speed. Teams adopted AI because it helped them move faster, and data practices grew organically with the business. Meanwhile, the legal and governance work never fully caught up and often remained an afterthought, which is exactly what buyers and investors are now starting to notice and question.​

How casual practices cost you

Informality means running important data and AI practices in a loose, undocumented, or ad hoc way instead of through clear policies, contracts, and oversight. From a buyer’s or investor’s perspective, informality equals uncertainty, and uncertainty usually turns into a price deduction. If it is unclear whether you actually own or can lawfully use the data that drives your product, they have to assume the worst-case scenario and adjust price or protections accordingly. If your team relies on AI tools without clear restrictions or oversight, the acquirer may worry about trade secret leaks, privacy leaks, undisclosed third-party rights, or regulatory exposure that will surface after closing.​

On the other hand, a business that can show:

• a clear map of its AI tools and data flows,

• written policies that employees know and follow, and

• contracts that match those practices

  
  

gives diligence teams more confidence that they are buying a stable asset rather than a bundle of unknowns. That confidence translates directly into smoother negotiations and a stronger position on valuation and terms.​

A three-step legal cleanup checklist

You do not need a 200-page manual to get ready. What you need is a focused, credible baseline that can stand up under questioning. This three-step “legal cleanup” framework is designed to be practical for a mid-market Michigan business that wants to be exit- or capital-raise ready.​

1. Upgrade your contracts with AI and data clauses

Start with the agreements that third parties will actually read in diligence: your customer contracts, vendor contracts, and key independent contractor agreements.​

Clarify data ownership and permitted uses

Spell out who owns what data (customer data, derived data, analytics) and how each party may use it, including whether you can use aggregated or de-identified data to train models or improve services. Make sure any rights you rely on in your business model (such as using customer data for analytics) are clearly granted in the contract and aligned with your privacy disclosures.​

Address AI usage expressly

Add provisions that cover if and how AI tools may be used in performing services, processing customer information, or generating deliverables. This can include restrictions on feeding confidential or personal data into public AI systems, requirements that vendors disclose material AI use, and confirmation that AI-assisted outputs do not infringe third-party rights.​

Align confidentiality and security with your actual practices

Ensure your confidentiality and security clauses reflect what you actually do (and can continue to do at scale), rather than generic promises that are impossible to meet. Buyers will compare what your contracts say with your policies and technical controls; visible alignment builds trust, while gaps invite deeper probes.​

2. Put written data-security and AI-use policies in place

When you mention a policy, people’s eyes tend to glaze over. Teams often dislike policies, but they serve an important purpose here. Written policies serve two audiences: your team, which needs clear, practical guardrails, and future diligence teams, which need evidence that your controls are real.​

Data-security and privacy policy

Create a concise internal policy addressing access controls, encryption, incident response, vendor management, and retention or deletion practices. It should be consistent with any commitments you make in customer contracts and on your website, and it should be realistic enough that your team can follow it without constant exceptions.​

AI acceptable-use guidelines

Issue short, plain-language guidelines telling employees what they can and cannot do with AI tools:

• what types of data may never be pasted into public models,

• how to handle client or customer information, and

• when to seek approval before adopting a new AI tool.

  
  

This not only reduces day-to-day risk but also gives buyers confidence that your AI use is not a free-for-all.​

Training and acknowledgment

Have employees acknowledge these policies and provide periodic refresher training, even if it is short and integrated into existing meetings or onboarding. In diligence, being able to show that policies are communicated and followed is more persuasive than a beautifully drafted policy that no one has seen.​

3. Build basic AI and governance documentation

Governance does not have to be complicated, but it does have to be visible.​

Create an AI and data inventory or system register

There is likely more AI in your business than you would think. In addition to obvious tools like ChatGPT, many everyday systems now have AI “inside,” such as Copilot in Word and Outlook, AI features in your CRM or scheduling software, and chat or phone assistants built into your website or phone system. For example, in a typical small plumbing business that has started modernizing, it is common to see roughly 3–7 AI-enabled tools in active use, even if the owner would not always label them as “AI,” and that number is likely to grow over time.​

A business needs to get a handle on what is being used. Start by maintaining a simple register listing:

• each AI-enabled tool and which part of the business uses it,

• the specific tasks it performs and what data it touches, and

• any limits on use and safeguards (such as “no payment data,” “no confidential client information,” or “human review required before sending or deciding”).

  
  

Even a short, working register like this gives leadership and future buyers a clear snapshot of where AI shows up in the business and how it is being controlled.​

We have created a sample AI Systems Register template for an AI tool based on real small-business scenarios. You can download it as a PDF to adapt for your own business.​

AI_Systems_Register.pdf

Download PDF • 168KB

Document key decisions and approvals

Keep short memos or meeting notes for major decisions, such as adopting a new AI vendor, changing your data-retention approach, or rolling out an internal AI policy. Even a one-page decision record shows that leadership considered legal, security, and compliance implications rather than making choices casually.​

Align board and management oversight

If you have a board or advisory group, add AI and data risk as a recurring agenda item, and reflect that in minutes. Buyers and investors increasingly expect to see that data and AI governance are part of ordinary-course oversight, not just an occasional hot topic.​

Positioning this with your CFO and leadership team

For most growth-stage businesses, the CFO is already managing the tension between innovation and risk. Framing this work as a valuation and deal-readiness project, rather than pure compliance, helps leadership prioritize it and allocate budget. The message is simple: a modest investment in contracts, policies, and documentation today can prevent painful price chips, expanded escrows, or even broken deals tomorrow.​

For Michigan companies that anticipate a sale, recapitalization, or strategic investment in the next three to five years, starting this cleanup now lets changes settle into the business before anyone begins asking questions. By the time diligence arrives, you are not scrambling to retrofit governance, you are demonstrating that your data and AI strategy has been part of how you run the business all along.

Want to know how your data and AI practices would look in diligence? A targeted review can help identify issues before they affect price or terms. Click here to schedule a brief data and AI diligence review.