Q01
Question — Which AI tools, assistants, chatbots, document tools, coding tools, transcription tools, or automation features have people in the firm actually used for work in the last 90 days?
Uncovers — Actual AI usage, tool diversity, bottom-up adoption, recency.
Why it stays clean — It asks for recent behavior and named tools, not beliefs about AI.
Follow-up probes —
- Who used each one?
- Was it paid, free, personal, or company-approved?
- What work output did it touch?
Polite-answer trap — Useless/evasive: “People are experimenting with AI.” Redirect: “Which named person or role used which named tool last, and for what task?”
Q02
Question — What has the firm paid for around AI or automation so far, including subscriptions, pilots, training, consultants, integrations, or internal time?
Uncovers — Committed spend, seriousness, budget owner, AI investment history.
Why it stays clean — It asks for money and time already spent rather than AI importance.
Follow-up probes —
- Who approved the spend?
- What was the rough amount or band?
- What was delivered or produced?
Polite-answer trap — Useless/evasive: “We have not spent much yet.” Redirect: “Has any invoice, licence, training fee, consultant day, or internal project time been attached to AI or automation?”
Q03
Question — Tell me about the most concrete AI or automation trial the firm has run. What data went in, what output came out, and what happened to the output?
Uncovers — Pilot realism, data access, output usefulness, adoption depth.
Why it stays clean — It demands the mechanics of a real trial, not a forecast.
Follow-up probes —
- Who ran it?
- Which dataset, document set, or workflow was used?
- Did anyone act on the output?
Polite-answer trap — Useless/evasive: “We played around with a few things.” Redirect: “Which trial had the clearest input, output, and owner?”
Q04
Question — What was the last AI-generated output someone trusted enough to use in firm work?
Uncovers — Trust threshold, output acceptance, practical use cases, risk tolerance.
Why it stays clean — It asks for a specific accepted output rather than perceived promise.
Follow-up probes —
- Who reviewed it?
- Was it edited before use?
- Where did it end up: email, report, spreadsheet, code, meeting pack, or client document?
Polite-answer trap — Useless/evasive: “AI can be useful for drafts.” Redirect: “Which draft or output was actually used, and who received it?”
Q05
Question — What was the last AI-generated output someone rejected, corrected, or stopped using?
Uncovers — Failure modes, trust barriers, quality bar, risk perception.
Why it stays clean — It asks for a concrete rejection event.
Follow-up probes —
- What was wrong with it?
- Who caught the problem?
- Did the incident change anyone’s usage?
Polite-answer trap — Useless/evasive: “You have to check the output.” Redirect: “What is the clearest example where checking revealed a real problem?”
Q06
Question — Which AI or automation usage is company-approved, and which usage happens informally through personal accounts or unapproved tools?
Uncovers — Governance gap, shadow AI, security risk, practical demand.
Why it stays clean — It asks about observed approval status and behavior.
Follow-up probes —
- What policy or guidance exists?
- Who enforces it?
- What work do people still do outside approved routes?
Polite-answer trap — Useless/evasive: “People know to be careful.” Redirect: “What is the last example you heard of someone using a personal account for work?”
Q07
Question — Who inside the firm has built or configured a script, macro, chatbot, AI workflow, spreadsheet automation, or low-code tool for their own work?
Uncovers — Internal builder capacity, forward-leaning users, latent automation demand.
Why it stays clean — It asks for actual builders and artifacts, not interest in innovation.
Follow-up probes —
- What did they build?
- Who else uses it?
- Did it remain personal, spread, or get shut down?
Polite-answer trap — Useless/evasive: “We have a few tech-savvy people.” Redirect: “Which person has built something that others rely on?”
Q08
Question — What AI training, policy session, webinar, vendor workshop, or internal presentation has the firm attended or paid for, and what changed afterwards?
Uncovers — Learning spend, action after education, seriousness, internal momentum.
Why it stays clean — It asks for observable follow-through after an event.
Follow-up probes —
- Who attended?
- Was any pilot, policy, purchase, or workflow change created afterwards?
- What was forgotten after the session?
Polite-answer trap — Useless/evasive: “We keep up to date with AI.” Redirect: “Which event led to the most concrete next action?”
Q09
Question — Which client, project, contract, data, or confidentiality concern has limited AI use in practice?
Uncovers — Data constraints, legal blockers, client sensitivity, adoption ceiling.
Why it stays clean — It asks for actual limits encountered rather than abstract risk views.
Follow-up probes —
- What data was considered too sensitive?
- Who raised the concern?
- Was usage blocked, changed, or approved with conditions?
Polite-answer trap — Useless/evasive: “We are cautious with data.” Redirect: “Which specific dataset or document was not allowed into an AI tool?”
Q10
Question — Which AI or automation effort has been abandoned, paused, or left unused, and what happened?
Uncovers — AI churn, bandwidth limits, quality failures, implementation barriers.
Why it stays clean — It asks for a real stopped effort.
Follow-up probes —
- Who started it?
- At what stage did it stop?
- Was the cause data, accuracy, time, cost, politics, or ownership?
Polite-answer trap — Useless/evasive: “Some experiments naturally fade.” Redirect: “Which experiment faded most clearly, and what was the last visible activity on it?”