Assess organization's AI maturity, uncover critical gaps, and gain access to world-class resources to build and roll-out your AI GRC roadmap.
Aligned with ISO 42001, NIST AI RMF, OECD/WEF AI and industry best practices.
Organizations are under pressure to scale AI but most lack the operating model, data foundations, and governance to deploy it safely and effectively.
The AI Governance Maturity Assessment™ shows you exactly where you stand and what needs to happen next.
Get a clear view of where your organization stands today across leadership, culture, operating model, data, infrastructure, and governance.
Understand what slows AI velocity and what is required to progress confidently.
Translate your diagnostic results into focused priorities.
From AI CoEs to secure data pipelines, we help you identify the levers that create real enterprise value: use cases, KPIs, and system upgrades that accelerate impact.
Close the gap between ambition and execution with a unified roadmap.
We align leadership, IT, and delivery teams around a clear plan anchored in measurable outcomes and enterprise-grade governance.
Because AI transformation isn’t a technology race. It’s an operating model redesign. The AI Governance Maturity Assessment gives you the blueprint.
The MindXO AI Governance Maturity Assessment™ evaluates your readiness across the eight foundations required to scale AI efficiently and safely.
Your position is mapped on a 3-tier scale: Minimal Viable Governance; Managed Governance and Risk Aware Governance
These pillars form the backbone of your AI operating model and give leadership a unified view of strengths, gaps, and priorities.
Assesses the organization’s ability to identify, classify, and maintain visibility over AI systems, use cases, and data flows across the enterprise.
Assesses how AI risks are identified, owned, escalated, and managed, including clarity of accountability across business, technology, and risk functions.
Assesses the presence of lifecycle controls for AI systems, including development standards, deployment gates, monitoring, and change management.
Assesses how data used by AI systems is governed, including data quality, lineage, access controls, and fitness for purpose.
Assesses the organization’s ability to define ethical boundaries, comply with applicable regulations, and operationalize responsible AI principles.
Assesses how AI-related vendors and external models are selected, governed, and monitored, including dependency and risk management.
Assesses C-level ownership, strategic alignment, value tracking, and the extent to which AI initiatives are tied to measurable business outcomes.
The radar chart benchmarks your current state against enterprise-grade targets across data quality, governance, interoperability, and system maturity.
Together, these pillars show exactly what must evolve for your organization to scale AI safely, efficiently, and responsibly.