AI as Substrate: Architecting the AI-Native Enterprise
Why winning firms treat AI as the substrate they are built on, not a tool they adopt — and the architecture (AI‑SAFE) that makes it real.
Audience takeaways
- The four commitments and 36‑cell matrix that name every AI concern and accountable role.
- Where AI initiatives fail structurally — and how to defend each cell.
- The Trust and Value rings: containing risk while proving return.
The CEO’s Real Job in the AI Transition
95% of enterprise AI fails for organizational, not technological, reasons. The three non‑delegable mandates a CEO must personally hold.
Audience takeaways
- Architect, Operator, Steward — and why none can be delegated.
- The signature 2026 failure mode: running Architect and Operator at full speed while deferring the Steward.
- A ten‑move playbook that separates winners from the failure list.
The Inference Economy
Why cost‑per‑token is becoming the defining metric of enterprise AI — and how to run compute as a managed asset, not an open tab.
Audience takeaways
- Route every request to the cheapest capable model.
- Attribute value per workflow; treat inference as a P&L line.
- AI FinOps as a board‑level discipline.
Provable or Unsellable: AI Governance in Regulated Industries
In regulated industries, AI that cannot prove what it did does not ship — so governance becomes the accelerator, not the brake.
Audience takeaways
- A six‑step governance lifecycle mapped to the EU AI Act, ISO 42001, NIST, FDA and GxP.
- Run the process once; discharge every framework together.
- Why GRC now sets the pace of AI velocity.