Reviewed: October 12, 2026 · Format: two-week implementation plan
A small pilot can reveal whether an AI workflow is useful before the business pays for wider rollout or connects sensitive systems. Keep the task narrow, choose an owner, and write the stop conditions before testing.
Days 1–2: define the task
- Choose a frequent, low-risk task with a clear output.
- Map the current steps, owner, exceptions, and manual fallback.
- Record the baseline time, volume, error rate, and quality standard.
- List what data is allowed and prohibited.
Days 3–4: check the tool and controls
Confirm the exact product and account type. Read its current data terms, retention and training settings, user access controls, integrations, and exit process. Start with sample or redacted data. Do not connect a whole inbox or shared drive just to test a single task.
Days 5–8: test representative examples
- Use the same input examples for each test.
- Include an ordinary case, a missing-information case, and an edge case.
- Keep a human reviewer between the AI output and any external action.
- Record corrections, failures, extra review time, and unexpected outputs.
Days 9–11: run a limited live workflow
If the sample tests meet your pre-set bar, use the workflow with a small group and low-risk tasks. Tell staff what the pilot covers, where to report problems, and how to return to the manual process. Do not expand scope mid-pilot without recording the change.
Days 12–14: decide and document
Compare the pilot with the baseline: time saved after review, quality, error rate, maintenance effort, and full cost. Write down the result, uncertainty, data controls, and owner. Choose one outcome: stop, revise and retest, or expand carefully.
Vendor data commitments are tied to specific products and accounts. Check the documentation for the exact service before the pilot; for example, consult OpenAI’s current business-data information or Microsoft’s applicable Copilot enterprise data protection terms.