Run a discovery interview for a fictional client

Discovery is not a disguised product pitch. The goal is to understand work, decisions and constraints well enough to test whether a solution is worthwhile.

Preserve an unknown

The delivery lab records a fictional finance conversation.

from delivery_cases import discovery_case

result = discovery_case()
assert result["unknowns"] == ["volume"]
assert result["answers"]["error_cost"] == "duplicate payment risk"
assert result["solution_promised"] is False
assert result["fictional"] is True

Ask the participant to walk through a recent example: trigger, people, systems, inputs, decisions, exceptions, wait time, errors and evidence. Explore who owns data/access and who accepts change. Quantify volume and baseline when possible; label unknowns and follow-ups otherwise.

Avoid leading questions such as “Would an AI agent help?” Summarize back, distinguish facts from assumptions and ask permission before recording. For a simulation, write personas and constraints in advance so the learner cannot invent favourable answers.

The FDE for Freshers course assesses discovery as a client-facing engineering skill.

Exercise

Run a 20-minute roleplay, produce a current-process map and five unknowns, then identify one reason not to automate.

Continue learning

This article is part of the FDE career entry and client-delivery practice sequence. Use the neighbouring tasks when you need the prerequisite or the next application.

• Review the prerequisite or neighbouring task in Choose a first FDE portfolio project with a bounded client problem.
• Continue with Turn a vague automation request into acceptance criteria.

Reference: GOV.UK moderated research guidance.