What should our AI governance checklist include before teams scale beyond pilots?

Why the checklist matters before AI pilots become normal work

AI pilots often start safely because they are small, visible, and closely managed. The risk changes when a useful tool moves from a few supervised experiments into everyday client work. At that point, a professional services firm needs more than a promising use case. It needs a checklist that shows which AI activity is allowed, what evidence supports the decision, and how the firm will spot problems before clients, regulators, or partners do.

The LexisNexis professional services checklist frames AI governance as a business-specific framework, not a generic policy. That is the right starting point. A law firm, accountancy practice, consultancy, insurer, or FCA-authorised adviser will have different risk tolerance, client confidentiality duties, review obligations, and operating constraints. The checklist should therefore be short enough for teams to use, but specific enough to stand up to partner, client, or regulatory scrutiny.

The safest checklist covers scope, data, approvals, evidence, and ownership

Before teams scale beyond pilots, the checklist should answer five questions clearly: what counts as AI, which use cases are approved, what data can enter the tool, who reviews the output, and what audit trail proves the controls were followed. If those five points are unclear, the firm is not scaling a governed capability. It is scaling informal experimentation.

A practical checklist should include:

• Scope: a clear definition of AI for the firm, covering generative AI, machine learning, embedded vendor AI, transcription, research, drafting, analytics, and workflow automation.
• Use-case register: a list of approved, restricted, and prohibited uses, with owner, business purpose, risk tier, and review date.
• Client and personal data rules: what information can be entered, what must be redacted, and which tools are approved for confidential or regulated data.
• Human review: who checks AI-assisted work before it reaches a client, court, regulator, board pack, or management decision.
• Evidence: logs, prompts where appropriate, source checks, output review notes, vendor assurances, DPIAs, incident records, and policy exceptions.

Check whether your AI pilots are ready to scale

Define what AI means inside the firm

The first mistake is treating AI governance as a policy for ChatGPT alone. Most professional services firms already use AI in search, document review, CRM enrichment, transcription, data extraction, financial modelling, fraud checks, compliance monitoring, or marketing workflows. If the checklist does not define the tools and behaviours it covers, teams will route around it.

The definition should include standalone tools, AI embedded in existing software, vendor-managed models, internal automations, and staff use of public systems. It should also distinguish low-risk productivity support from work that affects client advice, regulated decisions, confidential information, privileged material, or professional judgement.

Classify use cases before approving tools

A tool is not safe or unsafe in the abstract. The same platform might be acceptable for summarising a public article and unacceptable for analysing an unredacted client file. The checklist should classify use cases by data sensitivity, reliance on the output, client impact, legal or regulatory exposure, and reversibility if the output is wrong.

A simple model works well:

• Low risk: internal drafting, public-source research, meeting preparation, or formatting where no confidential data is used and a human remains fully responsible.
• Medium risk: client-context work, matter summaries, diligence support, marketing claims, or operational recommendations that require documented review.
• High risk: legal analysis, financial advice, underwriting, claims, regulatory submissions, employment decisions, healthcare decisions, or anything involving privileged or sensitive personal data.

Each category should have a matching approval route. Low-risk work may need policy acceptance and sample QA. Medium-risk work needs manager or matter-owner approval. High-risk work needs partner, compliance, legal, data protection, or board-level sign-off depending on the firm.

Set hard rules for client-confidential and personal data

Professional services governance rises or falls on data handling. The checklist should state which tools can process client-confidential information, which cannot, and what evidence is needed from the vendor before approval. That evidence should cover data retention, training use, sub-processors, security controls, access management, deletion, location of processing, breach notification, and contractual commitments.

For legal work, the checklist should also cover privilege, confidentiality, client consent, and whether any disclosure to a client or court is needed. For accounting, insurance, financial services, or regulated advisory work, it should cover personal data, customer outcomes, record keeping, and model reliance. The point is to make the safe path easier than the risky shortcut.

Make review and sign-off visible

Human-in-the-loop is not a control unless the loop is defined. The checklist should say what reviewers must check: factual accuracy, source reliability, missing context, hallucinated citations, confidentiality, bias, unfair customer impact, unsupported claims, and whether the final output still reflects professional judgement.

For client-facing work, the review note can be simple: who reviewed it, what they checked, what changed, and whether any AI limitations remain. For higher-risk work, the evidence should include a stronger review record, source pack, exception log, and sign-off by the accountable owner.

Build the operating model, not just the policy

A checklist only works if ownership is clear. Firms should name the accountable sponsor, operational owner, data protection contact, security reviewer, vendor-risk owner, and escalation route for incidents or exceptions. They should also agree how often the register is reviewed, who can approve a new tool, and what happens when a pilot becomes business as usual.

This is where many AI policies fail. They describe intent but do not allocate work. A better checklist connects governance to the actual operating rhythm: onboarding, procurement, client matter opening, delivery QA, training, incident response, and board or partner reporting.

Keep the AI governance register current

Keep an audit trail that proves the checklist was followed

The audit trail does not need to capture every keystroke. It should capture the decisions that matter. For each approved use case, keep the business purpose, risk tier, approved tools, data category, reviewer, sign-off, vendor evidence, policy exceptions, and review date. For high-risk outputs, keep enough evidence to reconstruct how the work was produced and checked.

This protects the firm in three ways. It helps partners see whether AI use is controlled. It gives clients credible evidence when they ask about governance. And it reduces panic when a regulator, buyer, auditor, or insurer asks how AI is being used in practice.

What to do next

Start with the use cases already happening, not a blank policy document. Interview team leads, review vendor tools, look at where staff use public AI systems, and identify the work that touches client data or professional judgement. Then create a one-page checklist for new pilots and a register for approved tools and use cases.

The first version does not need to be perfect. It does need to be usable, owned, and enforced. If a team cannot show the data rule, review route, and evidence trail for a pilot, it should not scale yet.

Turn the checklist into an implementation plan

FAQ

What should be on an AI governance checklist?

Include scope, approved tools, use-case risk tiers, data handling rules, vendor evidence, human review, sign-off, audit trail, training, incident response, and review cadence.

Who should own the checklist?

Ownership should sit with a named business sponsor and operational owner, with input from legal, compliance, data protection, security, procurement, and delivery leaders.

Do we need a separate checklist for every AI tool?

No. Use one firm-level framework, then apply it to each tool and use case. The risk tier should determine how much evidence and approval is required.

Can teams scale AI pilots before the checklist is complete?

They can expand low-risk use cautiously, but client-confidential, regulated, or judgement-heavy work should wait until data rules, review ownership, and evidence capture are clear.

How often should AI governance be reviewed?

Review high-risk use cases at least quarterly and the full register at least twice a year, or sooner when vendors, laws, client expectations, or business processes change.