Before we start — the one thing to hold onto
Most organisations think they have an AI adoption problem. They don't. They have an ownership problem. I've sat with leadership teams who had twelve AI pilots running across eight departments — and nobody could name who was responsible for any of them. Each team was experimenting. Nobody was deciding.
An AI operating model isn't a technology architecture. It's an answer to one question: who in this organisation owns AI decisions, at what level, and by what criteria?
That one idea changes everything. It turns AI from a collection of experiments into a capability the organisation can actually govern, scale, and trust.
Keep it in mind.
Purpose
The goal of this workshop is concrete: design the ownership structure and governance model that lets your organisation move from isolated AI pilots to a coordinated, scalable AI capability — without creating a bottleneck at the centre.
I've seen organisations freeze at the pilot stage for eighteen months — not because the technology wasn't ready, but because nobody had decided whether AI ownership lived in a central CoE, in each business unit, or somewhere in between. That indecision is expensive. Every week without a model is a week of duplicated effort, inconsistent standards, and AI initiatives that deliver locally and create risk globally.
You'll leave with an ownership model, a governance framework with clear decision rights, and a 90-day plan to activate it without stalling the pilots already in flight.
Who Should Attend
This workshop is designed for the people who have to answer for AI outcomes — not the people building the models.
- CTO, CIO, Chief AI Officer
- VP Engineering, VP Product
- Enterprise Architects and AI Strategy Leads
- Business Unit Leaders responsible for AI adoption
Typical team size: 6-10 participants
Format: In-person or virtual (hybrid available)
Try it yourself — The ownership audit
Pick any AI initiative currently running in your organisation. Can you name: who owns the business outcome? Who owns the model quality? Who owns the governance and risk? Who decides when it's ready for production?
If you get four different answers — or no answers — that's exactly why this workshop exists.
What You'll Achieve
By the end of this workshop, you will have:
- A clear AI ownership model — centralised, federated, or hybrid — matched to your organisation's structure
- Decision rights framework for AI: what gets decided where, by whom, at what risk threshold
- AI governance principles that don't slow down experimentation but catch risk before production
- Capability map — what your organisation can do with AI today versus what it needs to build
- A shared language between business and technology for evaluating AI readiness
- A 90-day activation plan to move from model to practice
These aren't governance documents to store in a policy library. They're the operating agreements your AI teams will use in next month's go/no-go decision.
Typical Outcomes
Immediate outcomes (within 1 week):
- Agreed AI ownership model documented and shared with leadership
- Decision rights matrix covering pilot, staging, and production thresholds
- Identified top 3 AI initiatives requiring immediate governance clarification
Short-term outcomes (within 1 month):
- AI governance framework adopted by at least two business units
- Ownership structure embedded in how new AI initiatives are scoped and approved
- Reduced duplication across parallel AI experiments
Long-term outcomes (3-6 months):
- AI initiatives moving from pilot to production with consistent criteria
- Risk and compliance teams aligned with AI governance model
- Organisational AI capability growing in a coordinated direction rather than fragmenting
Workshop Structure
Morning (3 hours): Current State — How AI is Actually Being Run
- Ownership audit: map every active AI initiative against current ownership
- Identify where decisions are being made, avoided, or duplicated
- Surface the governance gaps that are slowing production readiness
Afternoon (3 hours): Operating Model Design
- Choose and design the right ownership structure for your organisation
- Build the decision rights framework — what gets decided centrally, what gets delegated
- Define governance checkpoints that protect without creating bottlenecks
- Draft the 90-day activation plan
Total duration: 1 day
Adjustable: Yes — can extend to a second half-day for cross-functional alignment across business units
Try it yourself — The duplication check
List every team in your organisation currently working on anything involving AI or LLMs. Now check: are any of them solving the same problem independently? Are any of them using different vendors for equivalent capabilities? Are any of them making risk decisions that should be made at a higher level?
Most organisations I work with find they have three versions of the same AI experiment and no process for deciding which one becomes the standard.
Prerequisites & Preparation
Before the workshop:
- Inventory of active AI initiatives across the organisation (even informal ones)
- Participants identify their biggest AI governance pain point in one sentence
- Existing AI or data governance policies, if any, reviewed in advance
Recommended team composition:
- 1-2 C-level technology or AI leaders
- 2-3 Business Unit leaders or heads of product
- 1-2 Enterprise Architects or AI Strategy leads
- 1 Risk, Legal, or Compliance representative (essential — not optional)
How to know if this landed
You'll know this has landed when someone stops asking "can we use AI for this?" and starts asking "who needs to approve this, and by what criteria?" When a new AI initiative gets scoped with ownership defined before the first line of code. When the compliance team is part of the conversation at the beginning instead of the end. When the organisation can say, without hesitation, what it takes to move an AI system from pilot to production.
What changes when the mental model clicks
I've run this session with organisations that had invested significantly in AI tools and seen almost nothing reach production. The technology was capable. The teams were motivated. The problem was that every initiative hit the same invisible barrier — nobody had the authority to say yes to production, and nobody wanted to say no to experimentation. So everything stayed in pilot, indefinitely.
What changes after this workshop:
Leaders stop treating AI governance as a compliance exercise and start treating it as an acceleration tool. The ownership audit tends to be the moment things click — seeing every AI initiative mapped against who actually owns it, and how many cells in that map are empty, is usually enough. People realise the bottleneck isn't the technology. It was never the technology.
The decision rights framework tends to immediately change how new initiatives are scoped. Teams stop starting with "what can we build?" and start with "who owns this outcome, and what does good look like?" The experiments get faster. The path to production gets shorter. They stop blaming "organisational resistance" when the real problem was that nobody had designed the operating model for AI to move through.
Book a Workshop
Ready to give your leadership team the AI ownership model they need to move from pilot to production?
or
1-day intensive workshop includes AI ownership audit, operating model design, decision rights framework, governance checkpoint design, capability mapping, and a 90-day activation plan.