Systematic discovery of the metrics that AI makes necessary.
Every AI capability your organization deploys creates new decisions. Every new decision has failure modes. If no existing metric detects those failure modes, the failures are invisible. They accumulate cost, destroy value, and degrade performance in ways that never appear on a dashboard.
The Generative Metrics Institute uses the patent-pending Generative Metrics Discovery Framework to systematically find these invisible failure modes and produce computable KPIs to detect them.
An 8-step discovery method that works in any industry where AI creates unmeasured decisions:
1. Identify new AI capabilities. 2. Map capabilities to the decisions they create. 3. Identify invisible failure modes. 4. Define measurement gaps precisely. 5. Propose a computable KPI. 6. Ground in cross-disciplinary research. 7. Map to operational data infrastructure. 8. Design a validation pilot.
The framework operates generatively, discovering KPIs that should exist but haven't been conceived. This is the opposite of reactive AI monitoring.
1,085 KPIs across 33 industry verticals. 39 universal patterns identified (31 operational, 8 regulatory). Each KPI includes a computable formula, data source mappings, maturity classification, research grounding, and validation pilot design.
Browse all 33 verticals with complete KPI listings.
MRO, IT Operations, Software Delivery, Asset Reliability, Oil and Gas, Pharmaceutical Operations, Facilities Management, Rail Transportation, Water Treatment, Defense Operations, Energy and Utilities, Fleet and Transportation, Nuclear Operations, Renewable Energy, Aviation, Maritime Industries, Data Centers, Telecommunications, Human Capital Management, Manufacturing, Mining, Healthcare, Financial Services, Insurance, Payments and Transaction Processing, Marketing: Pricing and Promotional Optimization, Marketing Data Privacy, Process Industries, ISO Management Systems, MRO Supply Chain, Retail Operations, Corporate Real Estate, and Solo AI Operations.
Free organizational self-assessment measuring AI governance maturity across 5 dimensions. Produces a maturity score, radar chart, gap analysis, vertical-specific KPI recommendations, and a phased implementation roadmap. Take the AI-GPS Assessment.
The flagship volume, The AI Measurement Gap: Why Your Existing KPIs Cannot Govern AI-Influenced Decisions, defines the measurement gap that AI-influenced decisions create and makes the case for governance-grade measurement. Publishing soon on Amazon KDP.
The series also includes AI-Era KPI Reference Catalogs (compact 2-page-per-KPI format, one per vertical) and Practitioner Guides (full implementation depth with operational scenarios, worked examples, and platform-specific data mappings). Developed using the Generative Metrics Discovery Framework. The series publishes through Amazon KDP. Browse publications.
Framework licensing, custom vertical KPI discovery, assessment integration for platform vendors, and training programs. Learn about enterprise engagement.
A patent-pending 8-step method for systematically discovering key performance indicators in operational domains where AI and automation create decisions that legacy metrics cannot measure. The framework maps technology capabilities to decisions, identifies invisible failure modes, defines measurement gaps, and produces computable KPI specifications with formulas, data source mappings, maturity classifications, and validation pilot designs.
An invisible failure mode is a way in which an AI-created operational decision can fail that no existing metric in the organization is structurally capable of detecting. For example, automated remediation that suppresses symptoms while the underlying cause persists, or a predictive model whose confidence scores do not correspond to its actual accuracy. These failures accumulate cost and degrade performance without appearing on any dashboard.
1,085 KPIs across 33 industry verticals, with 39 universal patterns identified (31 operational, 8 regulatory). Each KPI includes a computable formula with defined variables, data source mappings, maturity classification, cross-references to related KPIs, research grounding, and a validation pilot design.
The AI Governance Positioning System (AI-GPS) is a free self-assessment tool that evaluates organizational AI governance maturity across five dimensions: AI Capability and Decision Awareness, Failure Mode Visibility and Measurement Coverage, Data Infrastructure and Computation Readiness, Human-AI Governance and Feedback Loops, and Validation and Continuous Discovery. It produces a maturity score, radar chart, gap analysis, vertical-specific KPI recommendations, and a phased implementation roadmap.
The framework has been applied across 33 verticals: MRO, IT Operations, Software Delivery, Asset Reliability, Oil and Gas, Pharmaceutical Operations, Facilities Management, Rail Transportation, Water Treatment, Defense Operations, Energy and Utilities, Fleet and Transportation, Nuclear Operations, Renewable Energy, Aviation, Maritime Industries, Data Centers, Telecommunications, Human Capital Management, Manufacturing, Mining, Healthcare, Financial Services, Insurance, Payments and Transaction Processing, Marketing: Pricing and Promotional Optimization, Marketing Data Privacy, Process Industries, ISO Management Systems, MRO Supply Chain, Retail Operations, Corporate Real Estate, and Solo AI Operations.
Created by Jason Brock. The Generative Metrics Discovery Framework is patent-pending.