What AI governance evidence should a buyer expect to see?

Why AI governance evidence matters in buyer diligence

When assessing a company's AI use, ask for a current inventory, records of material risks and supplier checks, and examples of human review. Match the request to the systems and data involved.

Ask for examples that show how the policy is used in the workflows being assessed.

Suggested evidence requests for buyer diligence

Request a concise pack showing where AI is used, what risks have been assessed, who owns decisions and how outputs are reviewed before they affect customers, staff or regulated work. Adjust the request to the company's uses and the scope of diligence.

Map your AI risk evidence before diligence

Start with an AI use inventory

The first document is a live inventory of approved AI tools and material use cases. It should identify the tool, owner, business process, data types handled, vendor status, and whether the use case is internal, customer-facing or decision-supporting.

A maintained spreadsheet can hold the inventory. Assign someone to update it when tools or uses change.

Show the risk assessment trail

For higher-risk uses, the company should keep a short risk assessment. This should cover confidentiality, personal data, accuracy, bias, contractual restrictions, human review, auditability and exit risk.

Ask for the privacy-review decision and its basis for any personal-data use. For advice, claims, financial decisions or legal work, specify who will review the relevant risks and what evidence they need. Do not assume one assessment format settles every applicable duty.

Evidence vendor and data controls

Ask the company to demonstrate the actual AI data route using the relevant product terms, settings, review and access records. Reconcile those records with its approved-use list and restrictions; the list alone does not prove how every upload was handled.

Show human review and accountability

AI governance evidence should make clear who is accountable for each material use case. It should also show what humans check before AI-assisted work is relied upon. Examples include review checklists, sign-off logs, QA samples, escalation rules and exception records.

Put lightweight AI governance ownership in place

Board reporting and incident readiness

For a PE-backed business, consider a board report covering material AI use, open risks, incidents, vendor exposure and policy exceptions. Agree a reporting frequency suited to the business; monthly or quarterly reporting is one option. Include the resulting reports in the diligence pack where relevant.

The company should also keep an incident route for AI-related errors, data exposure, hallucinated content, customer complaints or unauthorised tool use.

A practical diligence pack

A suggested pack for the uses being assessed includes:

• AI acceptable use policy and data handling rules
• Approved tool and use-case inventory
• Risk assessments for material uses
• Vendor and procurement evidence
• Human review and QA records
• Training or staff guidance records
• Board reporting and exception logs
• Incident response route for AI-related issues

Build an AI governance evidence pack

Conclusion

Use the pack to connect the AI inventory with risk assessments, sensitive-data controls, review records and accountable owners. Check examples against the workflows covered by the diligence request.

Need a safer AI route for your firm?

If this article reflects a live decision in your practice, the useful next step is to map the workflows, confidentiality risks, supplier controls and governance gaps before tools spread informally.

• Map AI risk and opportunity
• Put usable AI governance in place
• Roll out AI with privacy and supplier controls
• See AI support for legal services