intermediate~8–10 hours

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.

Direction & Target StateDefine where you are going before deciding how to get there.

Leadership & GovernanceBuild the accountability structure that makes AI adoption inevitable.

People, Culture & ReadinessAddress the human side of AI adoption — where most initiatives actually fail.

Technology Decisions & ToolsMake confident AI technology choices — not just tool selections.

Implementation & DeliveryMove from strategy to production — with a pipeline that works.

Learning & SustainabilityBuild an AI programme that survives year two — and keeps improving.

The AI Ways of Working Framework gives organizations a structured lens to assess and advance AI maturity across all six dimensions. Learn more →

6 modules · 25 episodes

Module 1·Direction & Target State

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?

1What Does an AI-Enabled Organization Actually Look Like?

2Pilot Purgatory — The Pattern Killing Your AI Initiatives

3The Capability Gap Nobody Talks About

4The Three Phases of AI Adoption — Where Is Your Organization?

Module 2·Leadership & Governance

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.

5The Leadership Imperative — Why This Cannot Be Delegated

6Who Should Own AI in Your Organization?

7Building Your AI Strategy — Not a Project, a Capability

8The AI Operating Model — Designing the System That Delivers

9Responsible AI — It's Not Compliance, It's Trust

Module 3·People, Culture & Readiness

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.

10Why People Don't Use AI (And What Actually Works)

11The AI Literacy Baseline — What Everyone Needs to Know

12AI Ways of Working — New Habits for a New Era

Module 4·Technology Decisions & Tools

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.

13The AI Technology Landscape — What Actually Exists and What You Need

14Build vs. Buy — The Decision That Shapes Everything

15Open vs. Closed Models — What Every Leader Should Know

16Your Data and Your Systems — What AI Actually Changes

17EU AI Act, GDPR, and Works Councils — The Legal Terrain Every AI Leader Must Navigate

Module 5·Implementation & Delivery

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.

18The Use Case Problem — Why People Can't Tell You What They Need

19Building Your AI Use Case Pipeline

20Avoiding Competing Initiatives — The Governance Trap

21Measuring What Actually Matters

22From Prompts to Agents — Understanding the Enterprise AI Progression

Module 6·Learning & Sustainability

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.

23The Year-Two Problem — Sustaining What You Built

24Staying Current — How Not to Fall Behind in Fast-Moving AI

25The AI-Native Organization — What It Actually Feels Like

Premium Track·Members · Framework

6 Additional Episodes

Advanced topics for practitioners, internal champions, and AI enablement leads.

1Not yet filmed

Data Sovereignty — When Your Data Cannot Leave the Building

2Not yet filmed

The CAIO Role — What It Actually Is and How to Build Toward It

3Not yet filmed

Agentic AI Governance — The New Problem Nobody Has Solved Yet

4Not yet filmed

Navigating Works Councils and Co-Determination in AI Rollouts

5Not yet filmed

AI Enablement as a Career — Making the Transition from Engineering to Enablement

6Not yet filmed

Your First 90 Days as an AI Enablement Leader

These episodes are in production. Access will be available to YouTube channel members and AI Enablement Playbook buyers.

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