AI TrustArc®

The AI Trust & Adoption Risk Compass is Behavieural's proprietary behavioral risk framework. It identifies where AI-related trust is most likely to fail—and how costly that failure will be. It does not evaluate models, data, or technical performance. It diagnoses the human conditions that determine whether people will actually use AI in real workflows—and whether organizations can scale it without triggering the failure modes that governance frameworks, technical audits, and change management programs aren't built to see.

Behavioral dimensions

5

Each one a distinct failure mode with a distinct root cause and a distinct set of interventions.

Operating modes

2

Deployment Mode for live AI and Readiness Mode for organizations preparing to go live.

Weeks for a typical engagement

4–6

Typical engagement timeline from kickoff to leadership debrief and prioritized action plan.

A leading indicator, not a lagging one.

Technical audits confirm whether a system was built correctly. The AI TrustArc identifies whether it will succeed once humans interact with it. The distinction is not semantic—it is the difference between a clean audit and a deployment that actually gets used.

An excellent model can fail if users distrust it, override it, or route around it. Those vulnerabilities don't appear in performance metrics. They appear in behavior—and behavior follows different rules than code.

Typical frameworks ask:

Was the model built correctly?

Is the data clean?

Does the model perform?

Is it compliant?

Is it secure?

Can we deploy it?

AI TrustArc goes beyond:

Will people actually use it?

Do users trust the outputs?

Are workarounds already forming?

Does the system feel fair to the people using it?

What happens to trust when it makes a mistake?

Can we scale it without triggering resistance?

5 underpinning dimensions.

  • 1. Cognitive Alignment

    Whether users' mental models of AI capabilities match how the tool actually works—and where misalignment produces distrust or over-reliance.

  • 2. Autonomy Safety

    Whether users experience AI as a threat to professional judgment—and how that perception drives avoidance, override, and workaround behavior.

  • 3. Fairness Comprehension

    Whether users believe AI outputs are fair, unbiased, and applicable to their patients or cases—and how fairness perception shapes trust at the point of use.

  • 4. Interaction Effort

    Whether the cognitive and workflow effort required to use AI creates friction that accumulates quietly—until adoption plateaus or workarounds become the default.

  • 5. Failure Recovery Intelligence

    Whether users know how to respond when AI makes an error—and whether the absence of that knowledge is generating silent risk at handover and escalation points.

Academic rigor—practiced.

  • Mental Model Theory

    People trust systems that match their internal expectations. When AI violates those models, users reduce reliance even when the system is accurate. Maps to Cognitive Alignment.

  • Self-Determination Theory

    Autonomy is a core psychological need. When AI constrains agency, users resort to resistance and workarounds. Maps to Autonomy Safety.

  • Procedural Justice Theory

    People judge fairness by whether a process feels consistent, transparent, and respectful. When AI can't provide that, users see it as unfair—regardless of outcomes. Maps to Fairness Comprehension.

  • Cognitive Load Theory

    Unnecessary mental effort signals poor design and weakens trust. When AI adds friction or complexity, users read it as organizational indifference to their workflow. Maps to Interaction Effort.

  • Trust Violation & Repair Theory

    Different failures require different repair strategies. Competence failures and integrity failures demand entirely different responses—a mismatch deepens the damage. Maps to Failure Recovery Intelligence.

Who is it for?

  • Operational leaders deploying AI into live workflows

    COOs, CMIOs, VPs of Digital Health, and operational executives who need clarity on whether people will actually trust, rely on, and integrate AI into their day-to-day decisions—and where the gaps are before they become safety or liability events.

  • Governance and risk leaders

    Executives and boards responsible for AI oversight who need behavioral risk intelligence that technical audits cannot provide—to exercise meaningful oversight of adoption, not just deployment compliance.

  • AI vendors and healthtech firms

    Organizations that need to understand how their products will be interpreted, adopted, or worked around inside client organizations—before those behavioral failure modes become churn, support burden, or reputational exposure.

  • Investors and due diligence teams

    PE, VC, and strategic acquirers evaluating AI companies or AI-dependent portfolio companies in regulated sectors—where adoption risk, trust stability, and scaling viability are not visible in standard technical diligence.

What does it produce?

  • Composite Trust Score

    A single weighted score summarizing the organization's behavioral trust posture—either a Trust Integrity Score (deployment) or Trust Readiness Score (pre-deployment). Sufficient for executive decision-making, risk committee reporting, and scaling decisions. Benchmarked against sector norms.

  • Dimension-Level Findings

    Five scored dimensions showing precisely where trust is strong, brittle, or at risk—and why. Each finding is grounded in behavioral evidence: interviews, workflow observations, override analysis, friction mapping, and micro-survey data. Not assumptions. Observable behavior.

  • Behavioral Risk Map

    A visual map of the most consequential vulnerabilities—friction hotspots, fairness concerns, autonomy threats, shadow-workflow risks, and recovery gaps—ranked by potential for operational, reputational, and safety impact.

  • Prioritized Action Plan

    Specific, sequenced interventions tied directly to findings—ranked by where behavioral risk is highest and adoption recovery is most reachable. Not a list of recommendations. A plan leadership owns and can execute without requiring a follow-on engagement.

  • Trust Roadmap

    In Deployment Mode: trust repair interventions, workflow redesign targets, and recovery protocol updates. In Readiness Mode: trust-by-design safeguards, communication planning, and governance strengthening—before the first user goes live.

  • Leadership Debrief

    A structured walkthrough of all findings with the executive team—designed to transfer ownership of the action plan, not create dependency on Behavieural. Every engagement is designed to end with leadership confident and equipped to move without us in the room.