Playbooks

How I actually run this work. Written for people stepping into AI leadership roles and for the people I mentor. Anonymised, field-tested, no theory for theory's sake.

AI Enablement Lead

The first 60 days and beyond: listen and map, ship a governed first win, then turn it into repeatable capability. Includes the evaluation frame, the governance skeleton and the metrics that matter.

AI Delivery Manager

Running AI work to a date: the weekly-delivery quarterly-judgement rhythm, one-way-door decision gates, hard deadlines with uncertain scope, recovery patterns and flow metrics.

AI Architect

Designing systems people must trust: evidence-first architecture, abstain-by-default, human gates as components, evals as the contract, and the metrics that keep answers checkable.

CTO / Director of Technology

Answering the board AI question: the build-vs-buy test, portfolio over projects, organisation topology, governance as a rhythm, and escaping the integration tax.

One job, many titles

The market posts this work under different names: AI Enablement Lead, AI Adoption Leader, AI Transformation Lead, Head of AI Adoption. The method is shared; the emphasis differs.

The embedded, client-side version of this work is increasingly hired as Forward Deployed Engineering: build the system inside the client's environment, alongside their team. The delivery and architect playbooks cover that ground; the working model is the one this site describes throughout: build with you, transfer capability, leave.

Whichever title the role carries, the success test is the same: the organisation can run it without you.

Stepping into one of these roles, or hiring for one? Email hello@krishnachodipilli.com.