Enterprise AI Adoption

Enterprise AI adoption that survives month three

Enterprise AI adoption fails for organizational reasons, not technical ones. Leadership delegates instead of adopting, training stays generic instead of role-specific, and the rollout gets treated as a launch event instead of a program. Fixing those three things is most of the job, and it's the job I do.

Why rollouts stall

Licenses get bought, the launch email goes out, and within a quarter usage flattens. AI capability now improves every few months, and most enterprise change cycles run 18–24 months. I call the gap between the two adoption debt: what the tool can already do minus what your people have actually absorbed.

The three-phase program

  1. Leaders use it first, in public.
  2. Role-based playbooks for each business unit, built on real workflows.
  3. Run it as a program with usage metrics, champions, and monthly refreshes when the models change.

Proof

Live Microsoft 365 Copilot and agentic-workflow program across 12+ business units at a publicly listed insurer. Earlier: GenAI enablement as AI Project Manager at Aroundtown SA (MDAX).

Tell me where your rollout is stuck

Frequently asked questions

Why does enterprise AI adoption fail?

Mostly for organizational reasons: leaders delegate instead of using the tools themselves, training is generic, and the rollout is run as an event rather than a program.

What is adoption debt?

The gap between what a deployed AI tool can already do and what the organization has actually absorbed into how it works. Daniel Wishnia coined the term.

How do you measure Copilot adoption?

By active, repeated use in real workflows per role and business unit, not by licenses assigned.

How long does an adoption program take?

It's an ongoing program, not a one-off project. The first measurable shifts usually come from the leadership and playbook phases. Timing depends on the organization, so I don't promise a fixed number.

Further reading

Related: AI visibility audit · Keynotes on AI adoption