How to Spot Them
Signature behaviours:
Always asks for a demo – Won’t touch it until they’ve seen it work.
Over-analyses accuracy – Breaks down results, errors, and edge cases.
Slow to adopt – Waits for proof before even small trials.
Voices AI’s limits – Brings up flaws and risks in every discussion.
Cautious with input – Selective with data shared due to privacy concerns.
What this means for you:
- They protect the team from poor-quality tools and decisions.
- They flag issues others ignore, from security to accuracy.
- But they can stall progress and lower team confidence if their scepticism spreads.
- With the right approach, they become your most thoughtful advocate for trusted AI.
The Challenges They Create
⚠️ High bar for adoption – Needs full proof before trying anything new.
⚠️ Slow to trust – Hard to convince without detailed transparency.
⚠️ Privacy-first mindset – May avoid using AI entirely if data feels unsafe.
⚠️ Fixates on flaws – One error can turn into a full rejection.
What to do
Don’t sell, show
- Share verified case studies tied to real outcomes.
- Use live demos to walk through results and edge cases.
- Invite them to test AI in safe, controlled environments with real data.
Break down how AI works
- Explain step-by-step how decisions are made.
- Make limitations clear, don’t hide them.
- Share how errors are caught, corrected, and improved over time.
Let them test before they trust
- Pilot AI in low-risk tasks they can evaluate.
- Offer review checkpoints so they stay in control.
- Encourage side-by-side comparisons of AI vs human output.
What Success Looks Like
✔️ They endorse AI with confidence, not just compliance.
✔️ They raise the bar for reliable, accurate AI use.
✔️ They help teams use AI critically, not blindly.
✔️ They shift from blocker to believer, without losing their standards.
