Tracks¶
| Page | Last updated |
|---|---|
| Tracks The eight workstreams of enterprise AI transformation, each containing a core framework, practitioner guides, and assessment tools. |
Updated 2026-06-12 |
| 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 2026-06-12 |
| Data Readiness The six foundational components of AI data readiness — Data Quality, Data Governance, Access & Integration, Lineage & Metadata, Infrastructure Readiness, and Security & Compliance. |
Updated 2026-06-12 |
| Technology Architecture & Platform The AI platform layer — tooling standardization, API governance, model selection, build vs. buy decisions, and avoiding point-solution sprawl. |
Updated 2026-06-12 |
| Workflow Optimization & Automation How to identify, prioritize, and redesign AI-enabled workflows. From assisted tasks to full agentic automation. |
Updated 2026-06-12 |
| 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 |