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PH1 core capbility
YEARS EXPERIENCE
9 to 10 years
TYPICAL CLIENT
VP Product, Director of UX Research, VP Customer Success
NECESSARY TIMELINE
Less than 2 months
Our POV
Trust is not a brand claim, it is a behaviour. If people double-check every output, keep a manual copy, or stop using a feature for anything that matters, confidence has failed — and none of that shows up as an error.
It can happen while the output is technically fine. The model is accurate and the person still does not rely on it, usually because nothing told them when it could be trusted and when it could not.
What We Do
Find where reliance stops. The moments people hesitate, verify, abandon, or quietly route around the AI — observed rather than self-reported.
Diagnose the cause. Unclear expectations, missing feedback, inconsistent behaviour or presentation that oversells certainty, each of which needs a different fix.
Weight by segment. Which groups have withdrawn trust, how far, and what it would take to recover it, so the response is targeted rather than generic.
What We'll Deliver
Map of where trust and reliance break down in the workflow
Causes diagnosed and separated by type of fix required
Segment weighting showing where confidence has withdrawn
Specific interface and content changes to rebuild reliance
Trust indicators you can monitor after the changes ship
Validation approach to confirm confidence actually improved
When This is Essential
People try the AI once and do not come back to it
Feedback is vague: unclear, or I do not trust it
Adoption has plateaued despite the model improving
Users keep a manual process running alongside the AI
Credibility concerns are rising internally before customers raise them
Combine With These Services
AI Evals & Output Fine-tuning — test whether the output deserves the trust
Customer AI Readiness Research — understand attitudes before deployment
Generative AI Product Design — design the trust signals the research calls for
Part of AI Product Evaluation and Customer Research.
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Submissions