Tracks¶
The eight workstreams of enterprise AI transformation. Each track contains a core framework, practitioner guides, and assessment tools. For how the tracks fit together — what each owns, how they depend on one another, and how to sequence them — see Framework Architecture. For the shared five-level ladder and the eight-track maturity matrix, see the Maturity Model.
In this section¶
| Page | Last updated |
|---|---|
| AI Strategy & Leadership What good AI strategy actually contains, how to connect AI investment to business outcomes, and the governance structure that sits above all eight tracks. |
Updated 2026-06-12 |
| AI Governance & Risk Model risk, vendor risk, regulatory exposure, acceptable use policy, and the governance layer above AI deployment. |
Updated 2026-06-16 |
| Data Readiness Data readiness is a property of the path a use case touches, not an estate-wide gate — the diagnostic, the framework, and the assessment tools. |
Updated 2026-07-03 |
| Technology Architecture & Platform The AI platform layer — tooling standardization, API governance, model selection, build vs. buy decisions, and avoiding point-solution sprawl. |
Updated 2026-06-18 |
| Workflow Optimization & Automation How to identify, prioritize, and redesign AI-enabled workflows — from assisted tasks to full agentic automation — and capture value that most AI programs leave on the table. |
Updated 2026-06-17 |
| AI Adoption & Culture What adoption actually requires beyond tool rollout — mindset shift, change resistance, trust-building, and the culture conditions that make AI stick. |
Updated 2026-06-12 |
| Talent & Capability Building The capability stack an AI-mature organization needs — role redesign, AI literacy, internal champions, and build vs. hire decisions. |
Updated 2026-06-12 |
| Measurement & Value Realization Why most AI programs can't prove they worked — and how to instrument, attribute, and use measurement to reprioritize investment. |
Updated 2026-06-12 |