AI programs don't fail for lack of models. They fail for lack of owners.
A pilot works because three motivated people carry it. A capability works because the organization does: decisions have owners, models have stewards, incidents have escalation paths, and none of it depends on heroics.
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We design that machinery with you and then help you run it in. Ownership and decision rights, governance that enables rather than strangles, and the skills plan that makes your people the ones who carry it forward.
FOR LEADERS WHO
want AI to survive the departure of its champions.
FOR TEAMS WITH
pilots that work and no way to make them routine.
FOR ORGS WHERE
every AI decision escalates to the same two people.
01 What you get
01
The operating model.
Who owns what: models, data, decisions, budgets, and the interfaces between business, engineering, and risk.
02
The governance frame.
Approval paths, oversight duties, and escalation routes sized to your risk, so governance speeds delivery instead of throttling it.
03
The skills plan.
The roles the model requires, mapped against what you have: what to hire, what to train, and what to borrow while you do.
04
The adoption playbook.
How new use cases enter, get funded, ship, and get retired, written as routine rather than exception.
02 How it runs
Four phases, designed with the people who'll run it.
An operating model imposed from a slide deck dies in a quarter. We build it in the room.
PHASE 01
Map.
How AI decisions are made today: who approves, who blocks, where things stall, and what the informal rules really are.
PHASE 02
Design.
Ownership, governance, and escalation drafted with the people who will hold them, argued until they fit how you actually work.
PHASE 03
Equip.
Roles staffed, training run, playbooks written with your teams, not handed to them.
PHASE 04
Embed.
We run the first cycles together, real use cases through the new machinery, and adjust what creaks.
03 Questions we answer
Who owns AI once the consultants leave?
How do we govern models without strangling them?
What do we hire, and what do we train?
How do teams escalate when the AI is wrong?
How does an idea become a funded use case?
What does running AI as a capability actually cost?
04 Continue the arc