A small AI pilot should answer one business question before anyone commits to a full system. Choose a workflow with a clear current baseline, a safe test set and a date to stop or continue. A pilot that only produces an impressive demo may not show whether staff can trust, maintain or afford the tool.
Which job should be tested?
For an illustrative eight-person service firm, the pilot might draft first replies to common customer questions for human approval. This firm is fictional. Record the current time per reply, error types and number of handoffs. Define the AI's permitted role: propose wording from approved information, never send directly or invent a price. Our AI chatbot quote guide covers customer-facing acceptance tests; this pilot keeps a human in the send loop. The AI meeting-notes guide shows a different internal workflow with its own data risks.
IMDA's starter kit for testing LLM applications provides a voluntary framework for safety and reliability testing. Its chatbot transparency guidance can inform a later customer-facing design. These guides do not certify a vendor or guarantee a pilot is safe. Decide what wrong answer, privacy leak or missed escalation would cause an immediate pause.
What should vendors price?
Give two vendors the same 100-example anonymised test set, approved knowledge source, user count, review workflow, pilot length and reporting requirements. Ask for setup, usage, integration, support, model changes, data retention, export and exit fees. A fictional Vendor A might be cheaper to start but require manual copying of approved replies; Vendor B may integrate with the existing inbox at a higher price. Compare the full pilot cost and staff review time, then estimate scale only from observed usage. Do not assume free credits continue after the test.
PDPC's data-protection obligations matter if customer data is used. Start with synthetic or suitably altered examples where possible, and assess any real data use before upload. Ask where prompts and outputs are stored and whether they are used for vendor model improvement. The data-processing clauses guide helps frame contract questions. Our CRM guide helps map the approved reply back to the right customer record.
What is the stop-or-scale decision?
At the agreed date, compare response time, factual accuracy, review effort, escalation and customer outcome against the baseline. Have staff inspect both good and bad examples. If quality is unstable, narrow the scope or stop rather than quietly removing human review. Write a short decision note with costs, unresolved risks and the owner of the next test. You can send Methodology a pilot-brief question for editorial guidance. We do not sell an AI platform or guarantee a vendor match.
Have two employees independently score a small set of outputs before deciding that quality improved. If they disagree, clarify the rubric rather than averaging the disagreement away. Keep examples of failed answers and the human corrections. They are useful evidence for a vendor discussion and for deciding whether the pilot scope should narrow.
Do not change the model or prompt halfway through an evaluation without recording the version and date.
A named sponsor should approve the final decision note and own the next review date if the pilot continues.
Common questions
Is a demo enough to approve deployment?
No. Test representative cases, failure handling, data terms and staff workflow.
Should customer data be uploaded to begin?
Start with synthetic or altered cases where possible and assess real-data use first.
What defines a successful pilot?
Set baseline, acceptance measures, cost limit and stop date before testing.
Sources
- starter kit for testing LLM applications imda.gov.sg
- chatbot transparency guidance imda.gov.sg
- data-protection obligations pdpc.gov.sg
- data-processing clauses guide pdpc.gov.sg
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