Autonomy = systems moving across contexts without human handoff.
8 years studying how digital technologies hit organizational reality: automation, digitalization, process redesign. Porsche, Lufthansa Technik, Accenture, and others.
10 months building with AI revealed what 8 years obscured: the bottleneck was never the technology. It's organizational ontology. CMC makes that constraint measurable.
Same gaps. Different industries. Same blind spots.
Most AI deployment failures are not technology failures. They are infrastructure failures. Organizations buy AI products without understanding what their internal systems need to look like for those products to work.
The CMC Framework exists to make that infrastructure requirement visible before procurement, not after. By scoring infrastructure feasibility across six dimensions — Formality, Capture, Structure, Accessibility, Maintenance, and Integration — the framework predicts where deployments will stall, which gaps take the longest to close, and which capabilities are actually within reach.
The framework is built on research synthesis from MIT, RAND, S&P Global, IDC, BCG, McKinsey, and Gartner — covering over 15,000 organizations. It maps 730 AI capabilities across 7 industries against the infrastructure they actually require.
The question is not whether your organization can adopt AI. The question is what your organization needs to look like before AI will work.
The CMC Framework was created by Jonathan Stone to address a gap in AI deployment planning: organizations buying AI products without understanding what their internal infrastructure needs to look like for those products to work.
The framework synthesizes research from MIT, RAND, S&P Global, IDC, BCG, McKinsey, and Gartner — covering over 15,000 organizations. It maps 730 AI capabilities across 7 industries against the infrastructure they actually require.