Plan an AI automation pilot your team can review

Choose rules before adding interpretation

Routing, reminders and clear validation checks can often use agreed rules. AI is worth evaluating where an input needs interpretation, such as a document, an incoming request or a question over approved sources. Define the output you need and what a human must check before it can be used.

Test the uncertain cases

A demonstration should include incomplete inputs, duplicate records, ambiguous wording and unavailable connections. Agree how uncertainty is displayed and which actions must stop for review. An AI suggestion should not silently become an approval or a financial action.

• Who reviews an uncertain output?
• What source evidence can they inspect?
• How do corrections feed back into the business record?

Agree access, costs and operational ownership

Identify the provider accounts and system permissions needed for the pilot. Discuss who pays for hosting, AI usage and third-party subscriptions, whether data may be sent to a provider and who operates the workflow after handover. Use anonymized examples until data access and handling have been agreed.

Write acceptance criteria that can fail

State the tasks that each agreed role must complete, the invalid actions that must be blocked and the failures the team must recover from. Record a baseline before discussing time savings. A first release also needs explicit exclusions, documentation and an agreed next step if the evaluation reveals a gap. Scope changes and ongoing support should be discussed separately from the accepted deliverables.

Discuss this in your own workflow.

Bring the current process, your tools and the result you want. We can use that context to define a focused first conversation.