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Module 02 / Lesson 05 · Read, apply, check
Replace vague client commentary with specific events, decisions, evidence and follow-up questions.
Choose a useful question before the call: what changed, why did a decision matter, or what did the team learn that others could use? Read the approved background and identify gaps instead of asking the client to repeat its website. Build a short agenda with one main story territory and two alternatives. Ask the client to bring a concrete example and relevant records. AI can help prepare the question map, but the interviewer must listen for unexpected details and follow the evidence beyond the original outline.
When a founder says the market is changing, ask for the last moment when that change affected a real decision. Establish who was involved, when it happened, what alternatives existed and what happened next. Ask for the constraint or tradeoff, not just the success. “What did you stop doing?” often reveals more than “What makes you innovative?” Invite an example that did not work as expected. Specificity creates material another agency cannot reproduce merely by giving AI the same public company description.
During the interview, distinguish a direct observation, an estimate, a secondhand report and an opinion. Ask where a number came from and who can verify it. Note the period, sample and comparison behind an outcome. Capture time markers for potentially useful quotations and record any restrictions on use. When recording is appropriate, establish consent and intended handling first. Do not promise the client that every comment will become a quote; the story may need additional reporting or a different angle after review.
Summarize the strongest candidate story and the facts still missing. Ask for a named owner and delivery date for each item rather than requesting every available document. Send a concise follow-up that distinguishes factual checks, quote approval and new questions. Preserve the source recording or notes according to the agreed handling rules. An AI summary can accelerate organization, but compare its important passages against the original. The interview is successful when it produces a usable evidence trail and a clear next editorial decision.
Fictional logistics interview, annotated as an editor would work it.
Follow-up: Which queue, which dates and which records would let us check that?
A useful complication| Material | Next action |
|---|---|
| Operational decision | Record who changed the workflow and why |
| Queue counts and dates | Request anonymised records from operations |
| Customer experience | Obtain permission for an independent conversation |
The article angle changes from a generic AI celebration to a specific operating lesson: resolve exceptions before scaling automation.
“Our operations have changed.”
Which decision, date, person and constraint made the change real?
What record, example or independent source supports the account?
Choose the useful angle and assign the missing evidence.
In a fictional interview, a logistics founder begins with “AI has transformed our service.” The interviewer asks for the most recent operational decision that changed. The founder describes rerouting a specific warehouse workflow after discovering that a manual exception queue was delaying urgent orders. Follow-up questions reveal that the first automation trial failed because addresses were inconsistent. The operations lead can provide dates, anonymized queue counts and the revised process. Instead of drafting a generic AI success story immediately, the agency builds a story about handling exceptions before scaling automation. It records the founder’s exact description, requests the supporting figures and identifies a customer who may discuss the experience separately. The result is a reportable sequence of decisions, limitations and evidence, with a sharper lesson for readers.
Another interviewer asks only broad questions about vision, leadership and innovation. AI turns the transcript into a confident article filled with claims about transformation and customer delight. There are no dates, decisions or examples to support the language. When an editor asks what actually happened, the agency has to schedule another call. The first interview produced polished sentiment but failed to collect the material needed for a distinctive story.
A good interview is not a checklist completed at speed. Use the questions as prompts and pursue a surprising answer when it exposes a real decision or tension. Do not force a success narrative onto mixed results. A limitation, reversal or failed trial can make the eventual lesson more credible and useful than a story that pretends everything worked immediately.
Respect agreed recording, attribution and confidentiality arrangements. If a speaker introduces sensitive or restricted material, clarify what can be used before adding it to a draft. Do not treat a private client interview as automatic permission to publish every detail or identify a customer who has not agreed to participate.
Do this today · 30 minutes
Turn the logistics interview into a ten-question reporting agenda.
A document or spreadsheet and the illustrative case in this lesson. Use synthetic or already approved material for practice; no paid account or real client data is required.
Before: “Our operations have changed.”
After: Choose the useful angle and assign the missing evidence.
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Test this worksheet on one bounded assignment. Record the reviewer’s findings, revise the method and assign the next review date.
Before you move on
A founder tells you that a new process “changed everything” and wants an article this week. The transcript contains enthusiasm but no dates, examples or measured outcomes. What should you do before asking AI to draft the central story?
Next lesson →Associated Press: newsroom standards for AI
PRSA: professional ethics resources
Lesson edition 1.0 · References reviewed 16 October 2026 · Practical exercise reviewed 16 October 2026. Examples are illustrative teaching scenarios.
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