The Execution Gap in AI Maturity
The systematic gap between organizations’ aspirations for AI and their actual implementation capabilities. Most organizations overestimate their AI readiness and underestimate what’s required to operationalize AI at scale.
Manifestations
Strategy-Reality Gap: Executive roadmaps assume capabilities that don’t exist Pilot-Production Gap: POCs succeed but fail to scale Tool-Process Gap: New AI tools, old workflows Data-Ambition Gap: AI plans exceed data infrastructure
Root Causes
- Underestimating data work (see: Data Cascades)
- Treating AI as technology project vs. organizational change
- Lack of MLOps/AI infrastructure maturity
- Skills gaps: not just ML engineers, but ML-literate domain experts
The Maturity Paradox
Organizations that think they’re ready often aren’t. Organizations that recognize how hard this is often make better progress.
Indicators of Real Maturity
- Clear ownership of data quality
- Production ML monitoring in place
- Documented model governance
- AI literacy beyond the data science team
Related: 00-source—stanford-ai-index-2025, 04-molecule—data-cascades-concept, 07-atom—execution-gap-ai-maturity