The readiness gap is real, and documented
The world's leading research institutions, standards bodies, and regulators have independently reached the same conclusion: what separates organizations that win with AI isn't which model they use, it's whether they're ready to use it responsibly, and whether they can prove it. Below are the sources behind that claim, each linked to its primary publication. METRIS™ is the instrument that measures the gap they describe.
Sources referenced denote published research and standards cited for context. They do not indicate any partnership with, or endorsement of, SANJEEVANI AI or METRIS™.
Boston Consulting Group, "The Widening AI Value Gap" (Build for the Future, 2025 Global Study)
In a study of more than 1,250 firms worldwide, BCG found that only about 5% are achieving AI value at scale, while roughly 60% report little to no material value despite substantial investment. The difference, BCG concludes, is not the technology, it is the organizational capability to turn AI into value.
METRIS™ measures that capability: readiness, not the model.
U.S. National Institute of Standards and Technology, AI Risk Management Framework (AI RMF 1.0), 2023
The U.S. government's voluntary framework for managing AI risk, organized around four functions: Govern, Map, Measure, and Manage. NIST is explicit that the framework provides no maturity model or scoring approach. Assessing how far along an organization actually is is left to the user.
METRIS™ provides exactly that missing measurement, scored against NIST's own structure.
McKinsey & Company (QuantumBlack), "The State of AI in 2025: Agents, Innovation, and Transformation"
Drawing on nearly 2,000 respondents across roughly 105 countries, McKinsey found AI adoption is now near-universal, yet roughly two-thirds of organizations have not begun scaling AI across the enterprise, stuck between pilot and production.
METRIS™ measures readiness before the pilot, so the gap McKinsey documents is visible before it costs you.
Cisco, AI Readiness Index (2023 to 2025, annual)
Cisco's global index, based on a double-blind survey of roughly 8,000 senior leaders across 30 markets, scores organizations across six pillars (strategy, infrastructure, data, governance, talent, culture). Across three years, only about 13% of organizations qualify as fully AI-ready 'Pacesetters', readiness has stayed essentially flat even as adoption surged.
Cisco measures the market's readiness as a survey. METRIS™ measures a specific organization's readiness against the governance frameworks, with evidence, and remeasured over time.
Stanford Institute for Human-Centered AI, AI Index Report 2025
The AI Index recorded 233 documented AI incidents in 2024, a record high and a 56.4% increase over the prior year, while noting that standardized responsible-AI evaluation remains rare. The 2026 edition reports incidents rose further to 362.
Rising incidents with no standard measurement is the exact gap METRIS™ closes: a quantified, external reference for organizational AI readiness.
KPMG International, "AI Governance Principles for Boards" (with INSEAD, April 2026)
KPMG's framework for board-level AI oversight, built on five principles including independent assurance and outcome-based reporting. KPMG's Global AI Pulse Survey found nearly three-quarters of boards are seen as having only moderate or limited AI expertise.
METRIS™ is the independent, evidence-based measurement boards need, not management self-reporting.
INSEAD Corporate Governance Centre, co-author, "AI Governance Principles for Boards" (with KPMG, April 2026)
INSEAD's academic research grounds the board principles, emphasizing that boards must move from being informed to being able to challenge, from narrative to evidence.
METRIS™ supplies the evidence that makes board challenge possible.
ISO/IEC 42001:2023, Artificial Intelligence Management System (AIMS)
The first certifiable international standard for AI management systems, defining the documented controls and management practices an organization should maintain for responsible AI, and the basis for third-party certification.
METRIS™ scores an organization against ISO/IEC 42001's controls, measuring whether the management system actually operates, not just whether documents exist.
European Union, Artificial Intelligence Act, Regulation (EU) 2024/1689
The first comprehensive, legally binding AI regulation, classifying AI systems by risk tier (unacceptable, high, limited, minimal) with specific obligations for high-risk systems.
METRIS™ measures readiness against the EU AI Act's obligations, so accountability is provable, not assumed.
METRIS™ measures the readiness these institutions describe, and shows you how to improve it
Sources referenced denote published research and standards cited for context. They do not indicate any partnership with, or endorsement of, SANJEEVANI AI or METRIS™.