AI Maturity Research
Data-driven insights on what separates AI leaders from experimenters
The 6 Pillars Framework
Our AI maturity framework is based on extensive research across hundreds of organizations implementing AI at scale. We've identified 6 critical pillars that separate successful AI adopters from those struggling to move beyond pilots.
Key Finding: Organizations strong across all 6 pillars are 5x more likely to achieve measurable business impact from AI initiatives.
The Three Maturity Levels
Experimenter (0-10 points)
Organizations running ad-hoc AI initiatives without formal strategy. Common characteristics: isolated pilots, lack of C-suite sponsorship, undefined success metrics.
Achiever (11-20 points)
Organizations with structured AI programs beginning to scale. Common characteristics: executive sponsorship, dedicated AI teams, formal governance, some production deployments.
Pacesetter (21-24 points)
Industry leaders with AI embedded in strategy. Common characteristics: cross-functional excellence, 60%+ pilot success rate, measurable business impact, continuous innovation.
Research-Backed Statistics
- • 83% of AI Achievers have formal C-suite sponsorship vs 56% of Experimenters
- • 3x faster model deployment with centralized data platforms
- • 5x more AI specialists at Pacesetters compared to Experimenters
- • 68% of Pacesetters have mature MLOps vs 18% of Experimenters
- • 60% pilot success rate for Pacesetters vs 32% industry average
- • 4x faster deployment cycles with agile AI development practices
Why Most AI Projects Fail
Our research shows that 68% of AI pilots never make it to production. The top reasons:
- Lack of executive sponsorship and strategic alignment
- Poor data quality and governance
- Insufficient technical infrastructure (MLOps)
- Skills gap and lack of AI literacy
- No clear path from pilot to production
Organizations that address all 6 pillars systematically achieve 5x better outcomes.