AI Capital Decision Gate
A one-page instrument for determining whether an AI investment has sufficient evidence, accountability, value, and risk controls to fund, scale, renew, remediate, or stop.
- Value thesis: the specific financial or operational outcome expected, and how it is measured, is written down.
- Attribution method: evidence exists separating this system's contribution from unrelated changes.
- Production evidence: performance data comes from production use in the review period, not pilot results or vendor claims.
- Total cost of ownership is documented: technology, integration, infrastructure, security, monitoring, workflow redesign, training, governance, remediation.
- One named executive owns the total outcome: not the technology, not the vendor.
- Every function with a stake (clinical, technical, security, legal, operational) has provided evidence of its own performance.
- Escalation authority is explicit: who can pause, correct, or stop this system, and how.
- Realized value, not projected value, is measurable against the original thesis.
- The value is attributable to this investment specifically, not general trend, market, or seasonal effects.
- The value trajectory justifies this investment's continued claim on capital relative to alternative uses of that capital.
- Clinical and operational thresholds are predefined and actively monitored, not implicit.
- Cybersecurity and technical reliability controls are evidenced, not assumed.
- Legal, compliance, and liability exposure has been assessed for the system's current scale of deployment.
- Incidents and exceptions in the review period have been logged and addressed.
Read down, not across. Each outcome names the minimum the checked sections above must actually support, not a score to average.
New to why this gate exists? Start with Healthcare AI Has Become a Capital Accountability Problem.
If a section above surfaced real gaps, not just missing paperwork: Someone Must Own the Seams and AI ROI and Patient Safety Are Starving for General Management address what adequate accountability and general-management capability actually require.
If your institution cannot yet answer these questions at all, not just for one investment but structurally: the accredited IEEE/UL 2933 standard is the institutional framework for building this capability. See Standards.
Evaluating the institutional capacity a counterparty consumes, rather than the capital an initiative consumes? See the AI Risk Stewardship Gate: does this counterparty justify the institutional capacity it consumes?
The two gates work together. Capital committed, from this gate, plus capacity consumed and expected downside, from the AI Risk Stewardship Gate, is the full investment and exposure a decision requires.
Read more: Price Alone Does Not Establish AI and Agentic ROI.
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- Published by
- Trustworthy Technology & Innovation Consortium (TTIC)
- Author
- By Sherri Douville, Founder & Chair, Trustworthy Technology & Innovation Consortium (TTIC)
- Originally published
- Last updated
- Canonical resource
- https://trustworthytechnologyinnovation.com/ai-capital-decision-gate
Provenance: This resource is an original practitioner architecture from the Trustworthy Technology & Innovation Consortium (TTIC), developed to operationalize healthcare AI governance standards into institutional practice. External standards and source materials are cited separately.
Cite this resource
Sherri Douville. “AI Capital Decision Gate.” Trustworthy Technology & Innovation Consortium (TTIC), 2026. https://trustworthytechnologyinnovation.com/ai-capital-decision-gate.