Implement AI in your business: a practical pilot plan

Choose a task with an owner

Select one recurring question, such as which projects are running above their approved cost budget. Name the manager who needs the answer and the person responsible for its data. Keep the first pilot small enough that both can review every result.

Write down the intended outcome. "Prepare the weekly review with less manual work while preserving agreed totals" gives the pilot a test. "Make the company AI-ready" does not. The AI ROI guide helps establish the baseline and costs.

Check the data before choosing prompts

List the records and fields needed for the task. Confirm that the supported integrations or a configured read-only database connection can provide them. Identify missing information, such as a budget maintained in a separate workbook.

Agree identifiers, dates, included entities and calculation definitions. Check the synchronization schedule and what happens if an update fails. A prompt cannot recover a missing source field or decide which of two conflicting project codes is authoritative.

Restrict the pilot to the required data. Test with ordinary user accounts so the evaluation includes the intended access boundaries.

Prepare an acceptance pack

Select examples from completed reporting periods, including cases that previously caused mistakes. Record the expected numerical results and the permitted filters. Add missing-data cases and a question the system should be unable to answer from the available records.

Define acceptable differences for each calculation. Financial totals might need to match exactly apart from documented rounding. Commentary needs a different check: every material factual claim should have support, and a proposed cause should remain a hypothesis until confirmed. A general accuracy percentage can hide an unacceptable error in a critical figure.

Agree how much total preparation and review time would justify continuing. Use a target appropriate to your process rather than a universal rule about how fast AI ought to be.

Run the pilot alongside the existing review

A hypothetical consultancy chooses three practice leads to test a weekly utilisation summary over four weeks. The existing report remains the reference while both versions use the same reporting cutoff. The duration is an example, not a guarantee that four weeks covers every business cycle.

During the second week, the assistant reports a drop in billable hours. A lead discovers that a new time code was classified incorrectly. The team corrects the mapping and reruns the affected examples. It does not treat the first matching grand total as proof that all filters now work.

At the end, the owner compares preparation time, review effort, corrections and whether the leads actually used the result. A pilot that produces no net benefit can stop without becoming a company-wide rollout.

Define the everyday operating process

Before relying on the AI agent, assign someone to monitor data updates and handle user questions. Record what users should do when figures differ from the source. Keep the original reporting route available until the replacement meets the agreed acceptance criteria.

Explain the boundaries to users. The assistant can help retrieve and describe configured results; it does not make every generated sentence reliable or know facts that are absent from the data. Show users how to request the period, filters and supporting records.

Expand only after the next decision

Review the evidence with the task owner:

• Do the approved examples match, including edge cases?
• Are access restrictions correct for each user group?
• Is preparation plus review measurably better?
• Are unresolved errors visible and assigned?
• Is there an owner for source and definition changes?

If the answer supports expansion, add the next user group or related question. For help assessing the source setup, contact DigiData with the task, required fields and acceptance examples. That makes the implementation discussion concrete.