AI Enablement in Organizations
How organizations actually adopt AI — beyond tools and hype
Audience: Leaders, managers, and change-makers driving AI adoption — plus technical specialists (engineering, data/ML, analysts) stepping into enablement and leadership-facing work
After this track you will
- Define a clear target state for AI adoption — what 'done' actually looks like for your organization
- Understand why AI initiatives fail — and diagnose exactly which failure mode your organization is in
- Build the leadership, governance, and operating model that makes AI adoption inevitable rather than accidental
- Design AI learning programmes that create lasting behaviour change, not one-day workshops
- Make confident technology decisions: build vs. buy, open vs. closed, enterprise systems vs. general-purpose AI
- Run an AI use case pipeline from idea to production — with the measurement to prove it's working
Framework
AI Ways of Working
Six dimensions of organizational AI readiness — developed by Alexey Makarov alongside this series. Each module covers one dimension in depth.
6 modules · 21 episodes
The Target State
Most organizations start with tools. They should start with a definition of success. This module answers the first question every AI enablement leader must answer: what does done look like?
Leadership & Governance
The #1 predictor of AI success is who is accountable for it. This module covers what leaders actually need to do, who should own AI, how to build a real AI strategy, and what an operating model looks like.
People & Culture
You can buy the best AI tools in the world. You cannot buy the organisation's will to use them. This module covers non-adoption patterns, literacy baselines, and building new ways of working.
Technology Decisions
The wrong technology decision is expensive. The wrong governance decision is catastrophic. This module maps the AI landscape, covers build vs. buy, open vs. closed, data governance, and enterprise system integration.
Implementation
Strategy without implementation is daydreaming. This module shows how to discover real use cases, build a use case pipeline, govern the portfolio without bureaucracy, and measure what actually matters.
Sustaining Momentum
The biggest AI risk is not moving too fast. It's stopping after the first year. This module covers sustaining momentum, staying current in a fast-moving field, and what it actually looks like when the whole programme is working.
6 Additional Episodes
Advanced topics for practitioners, internal champions, and AI enablement leads.
Data Sovereignty — When Your Data Cannot Leave the Building
The CAIO Role — What It Actually Is and How to Build Toward It
Agentic AI Governance — The New Problem Nobody Has Solved Yet
Navigating Works Councils and Co-Determination in AI Rollouts
AI Enablement as a Career — Making the Transition from Engineering to Enablement
Your First 90 Days as an AI Enablement Leader
These episodes are in production and will be added to the series as they are filmed.
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