Artificial intelligence has entered M&A conversations from two directions. Buyers want to understand whether AI can improve a target company, and they want to know whether AI has introduced new legal, operational, security or competitive risk.
Owners should prepare for both questions. A credible AI strategy is grounded in workflows, data, controls and measurable outcomes—not a list of software subscriptions.
Start with the work, not the model
The best opportunities usually sit inside repetitive, information-heavy workflows: lead qualification, customer support, document review, forecasting, quality checks, scheduling, knowledge retrieval, content operations and anomaly detection.
Map the workflow before selecting a tool. Identify time, cost, error rates, handoffs and sensitive data. Then determine whether AI should assist a person, automate a bounded task or remain outside the process.
This prevents “AI adoption” from becoming disconnected experimentation.
Measure economic outcomes
For each material use case, establish a baseline and track results. Useful measures include hours saved, response time, conversion, defect rate, gross margin, retention, throughput and avoided cost.
Do not rely on theoretical productivity. If employees save time but the company does not redeploy it, increase capacity or improve service, the economic benefit may be limited.
Buyers will give more credit to a modest use case with verified results than to an ambitious roadmap without ownership or data.
Know where company data goes
Employees may paste customer data, contracts, code, financial information or personal information into public tools without understanding retention and training settings. That creates diligence questions around confidentiality, privacy, intellectual property and security.
Maintain an inventory of approved AI tools, use cases, data types, owners and vendors. Establish rules for prohibited information, human review, access and output verification. Review vendor terms and data-processing practices.
The National Institute of Standards and Technology organizes AI risk management around four functions—govern, map, measure and manage. The framework is voluntary, but its logic is useful for companies of any size: assign accountability, understand context, evaluate risk and respond deliberately.
Protect intellectual property
AI can accelerate product development and creative work, but ownership and provenance must be understood. Document which tools are used, who reviews outputs and how the company protects proprietary inputs. For software, maintain code-review and dependency controls. For content and design, preserve evidence of human direction and rights to source materials.
Contracts with employees, contractors and vendors should address confidentiality, inventions and permitted technology use. Transaction counsel can determine whether specific representations or remediation are appropriate.
Avoid automation without controls
AI outputs can be wrong, inconsistent or difficult to explain. The appropriate control depends on impact. A draft internal summary may require ordinary review. Pricing, credit, hiring, regulated advice or customer commitments may require stricter validation, approval and logging.
Define where a human must remain accountable. Record material decisions and monitor performance over time. A process that worked in testing can drift as inputs, vendors or business conditions change.
Make AI transferable
If an automation depends on the founder’s personal account, undocumented prompts or one employee’s private workflow, it creates a new form of key-person risk.
Use company-controlled accounts, role-based access, versioned prompts or configurations, documented data flows and backup ownership. Integrations should have error handling and monitoring. Critical processes need a fallback when the service is unavailable.
Transferability is part of technology value.
Be clear about proprietary advantage
Many companies describe ordinary use of third-party AI as proprietary technology. Buyers will distinguish between tool usage and a defensible asset.
A genuine advantage may come from unique data rights, embedded workflow, domain-specific feedback, distribution, customer integration, cost position or intellectual property. Explain what competitors can reproduce, what they cannot and why the advantage should persist.
Honesty improves credibility. A well-run company that uses widely available tools effectively can still create substantial value.
Prepare an AI diligence file
Before a sale, assemble:
- AI tool and vendor inventory
- Approved-use and data-handling policy
- Material contracts and terms
- Data-flow and integration diagrams
- Access and security controls
- Use-case owners and performance measures
- Incident, error and complaint history
- Intellectual-property analysis
- Business continuity and fallback procedures
The objective is not to create bureaucracy. It is to show that management understands a technology with real operating consequences.
The valuation question
AI readiness can influence value in three ways. It can improve current earnings, support a credible growth plan or reduce the risk that the company will be displaced. It can also reduce value when important processes are uncontrolled, data rights are unclear or the business proposition can be replicated cheaply.
Buyers will not pay for the word “AI.” They may pay for better economics, better data, faster execution and a durable position built with it.
Frequently asked questions
Does every business need an AI strategy before selling?
Every business should understand whether AI affects its operations, customers and competitive position. Not every company needs to build proprietary AI or automate every workflow.
What will buyers ask about AI?
Expect questions about use cases, vendors, data handling, intellectual property, human review, security, measured benefits and the risk of technology-driven competition.
Can employee use of public AI tools create deal risk?
Yes. Uncontrolled use may expose confidential information, create uncertain output rights or introduce errors. A practical policy and approved tools can reduce the risk.
What is the best first AI project?
Choose a bounded, high-frequency workflow with measurable cost or service impact, accessible data and a human who owns the outcome.
Considering what comes next?
Technology should improve a business, not complicate the explanation of it. United Commerce Group evaluates AI through operating results, risk and transferability. Owners considering a transition can speak confidentially with UCG.
Selected source: NIST Artificial Intelligence Risk Management Framework