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Also fails:
"Report material issues to leadership"
"Monitor and respond where warranted"
The primary risk is never a bad model — it is governance failure.
Read the thesis →Tier 03 · Structure · Thesis
Institutions deploying AI fail not because of underperforming models, but because of broken governance structures. The primary risk is never a bad model — it is governance failure.
The Workflow Thesis is the three-layer architecture itself — Governance, Protocols, Work Processes. L.E.A.C. sits inside Layer One as the physical-constraint check that no AI strategy is allowed to skip.
Each framework stands alone. Each framework is sharper inside the system.
Instant structural diagnosis for your organization.
Run free diagnostic →Tier 02 · Assessment · Diagnostic
Most institutions don't have a governance problem because they lack the right software. They have a governance problem because they never built the right structure.
The Trust Gap names what the architecture must close. GASP™ measures whether the architecture exists. Diagnosis before prescription. No tool selection until the diagnostic clears.
Can you name who owns the decision, what the escalation path is, and what accountability exists without a vendor? If not — the structure is absent.
72-control maturity framework for AI governance.
Download the report →Report · Trusted AI Maturity
Governance that has never been measured is governance that nobody can defend.
TAIMScore™, the Trusted AI Model Score, was developed by Taiye Lambo and HISPI. It scores AI governance maturity across 72 controls organized into four domains — Govern, Map, Measure, and Manage — and aligns to NIST AI RMF, ISO/IEC 42001, SOC 2, and the EU AI Act at once.
A maturity score converts "how good is our AI governance" from an exchange of adjectives into a baseline that survives personnel turnover, vendor churn, and the next audit cycle.
Failure File • Air Canada
Accountability & Escalation
Customer Chatbot / Tribunal Liability
Air Canada · Full breakdown
An airline's customer chatbot told a grieving passenger he could claim a bereavement fare retroactively. No such policy existed. When the passenger acted on the answer and sought the refund, the airline denied it and argued the chatbot was a separate entity. A tribunal disagreed and held the airline liable.
GOVERN 1.1: no defined structure governed what the chatbot was authorized to represent. GOVERN 1.7: no mechanism required human confirmation before the system issued definitive answers on refunds and fare policy. MANAGE 1.1: when the refund request arrived, no process recognized a chatbot-generated policy claim as a systemic risk event.
None of these controls is exotic. Their absence in a legally consequential deployment is a structural failure, not a technology failure.
Failure File 08 of 12
AI Validity, Reliability & Generalization Limits
Opaque AI / Child Welfare
Failure File 08 · Full breakdown
The Hackneys took their lethargic infant to the ER — the correct decision. Their screening data was fed into an opaque AI risk-scoring tool. The tool flagged them for parental negligence during a national formula shortage. Their child was taken.
This is a MEASURE 2.5 failure. The AI had never been demonstrated valid outside the narrow conditions it was developed under. Its generalisability limits were undocumented. When a real-world edge case appeared, the system had no mechanism to flag uncertainty or defer to human judgment.
This pattern is active in federal welfare systems, veteran services, and disability determination right now. If your org uses AI scoring in decisions affecting someone's family or benefits, and cannot explain model performance at its edges, this incident is your risk exposure.
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"The team will monitor model outputs for unusual behavior and escalate significant incidents as appropriate."
This sentence failed in production.
Also fails:
"Report material issues to leadership"
"Monitor and respond where warranted"