For CPOs, VPs of Product and enterprise AI leaders
Rigorous quantitative and qualitative research uncovering why users and employees adopt or abandon your AI systems. No psychological tricks, just demonstrably trustworthy product design.
“Trust is going to be the biggest issue for us.”
88%
of enterprise AI pilots fail to reach production due to trust, agency and risk control gaps.
Source: MIT and Gartner Enterprise AI Benchmarks
3.2x
higher workflow performance when AI tools feature human controls.
Source: Gartner AI Workplace Benchmarks
9%
of workers trust AI for business-critical decisions, versus 61% of executives.
Source: WalkMe, State of Digital Adoption 2026
The framework
Every engagement is evaluated against three pillars of calibrated trust that lead to increased adoption and retention.
User sovereignty and control
Risk mitigation and guardrails
Utility and system predictability
Services
Trust in AI fails in two directions. Too little, and the promised value never arrives. Too much, and errors flow straight into documents, code and decisions. Each service detects specific behaviors, so you learn which failure you have and what to change.
What the numbers show about trust, at scale.
We map your analytics and event logs to the behaviors that signal undertrust: rerolling outputs, rejecting good suggestions, keeping the AI away from consequential work, and usage that collapses after one visible mistake.
A scale-validated survey that baselines trust across nine components, from competence and reliability to recoverability and institutional backing. Repeat waves track change over time.
The trust behaviors and reasons the numbers don't see.
Ten in-depth sessions that uncover how users judge your AI and what they do as a result: doing the work twice, asking a colleague to double check, holding back context, hiding AI use.
We observe people using your AI in their real work setting, revealing the checking, workarounds and handoffs people rarely mention in an interview.
A researcher plays the AI behind the scenes, so we can test behaviors you haven't built yet, such as admitting uncertainty or acting on its own, before you commit engineering time.
Over two weeks, users record their experience as it happens. You see the moment a visible error changes behavior, and how team opinion about the tool spreads.
We evaluate your key flows against our trust heuristics across Agency, Safety and Quality, and pinpoint the design decisions that lead users to override or refuse the AI.
Conversations that surface the governance, accountability and cultural issues behind trust, including whether accountability is clear when the AI gets it wrong.
Users complete realistic tasks while we plant known mistakes in the AI output. Catch rate shows whether people check what matters; behavior after the first error shows whether one slip drives them away.
Ways to start
Combine services from the menu, or start with one of these. Every engagement ends with specific design fixes across Agency, Safety and Quality.
2 weeks
An expert heuristic review plus a light telemetry read, with a readout naming your top three trust failures.
Best for: Teams that sense a trust problem but can't name it.
4 weeks
Seeded error testing, Wizard of Oz testing and an expert heuristic review, before your product reaches users.
Best for: Consumer AI products approaching launch.
6 to 8 weeks
Telemetry audit, depth interviews, trust index survey and leadership interviews.
Best for: Enterprise AI programs with stalled usage.
Quarterly
A recurring survey wave, telemetry refresh and executive readout.
Best for: Teams tracking progress after a diagnostic.
Free sample AI Trust Review
Schedule an initial 45-minute AI Trust Review. We'll evaluate your AI product against our 3 Pillar Trust Framework.