Pilots with no owner
A pilot proves the technology and then stops, because no one at leadership level owns the change to the process, the role or the measure.
AI operating model
The AI Operating Model: Change How the Business Runs, Not Just the Tools It Uses
AI creates value only when the way the business works changes around it: who owns each result, who decides, how work flows, what data it runs on and how risk is controlled. That is the AI operating model. Without one, AI stays a set of pilots. With one, it shows in the numbers.
What is an AI operating model?
The way value, ownership, work and control fit together once AI is in the business
An AI operating model, sometimes called an AI-enabled or AI target operating model, sets out how an organisation turns AI into results: where AI will create value, who owns each outcome, who can approve and stop AI decisions, how workflows and roles change, what data and technology it depends on, and how the board sees risk and return. It is a management design, not a technology architecture.
Most AI programmes stall because the tool changes and the operating model does not. The pilot works, nobody owns the change to the workflow, and the value never reaches the P&L. AIPEX designs the AI operating model with leadership teams and starts from evidence: the AI Readiness Assessment shows which foundations are in place and which will hold the model back.
Why the board should care
Why the operating model decides whether AI pays back
Decisions nobody authorised
AI, and now AI agents, act inside workflows. If decision rights and limits are not designed, accountability sits with whoever switched the tool on.
Spend without a return
Without a value case, a baseline and a reporting line to the board, AI spend spreads thin and no one can show what it returned.
Who this is for
- CEOs and COOs who need AI to change how the business runs, not just add tools
- Leadership teams with pilots running and no route to production
- Enterprises and scale-ups moving from local AI projects to an enterprise AI operating model
- Boards that need clear accountability for AI, including AI agents that act on their own
- Investors and portfolio leaders standardising AI across several businesses
What it solves
- No agreed owner for AI outcomes at leadership level
- Decision rights for AI, and for AI agents, left undefined
- Workflows and roles unchanged around the new technology
- Duplicate AI projects across functions with no shared standards
- No line of sight from AI spend to measured business results
Sound familiar?
The problems an AI operating model fixes
Our AI pilots are not scaling
The pilot worked, then stalled. Nobody owned the change to the workflow.
Why AI pilots fail →Our AI tools are not delivering ROI
Time is saved but never shows in the numbers, because there is no baseline or benefit owner.
How to measure AI ROI →Staff use AI we have not approved
Uncontrolled AI use puts data and decisions outside any control.
Shadow AI risk →Nobody owns AI
AI is everyone's experiment and no one's responsibility.
Who owns AI in a business →We have AI governance gaps
A policy exists, but no register, no risk tiers and no report to the board.
Close the governance gaps →We do not know where to start
Too many ideas, no agreed order and no evidence of readiness.
How to implement AI →The AIPEX AI operating model framework
Six components every AI operating model needs
The framework is the same whatever the sector. What changes is the design choice inside each component, and that depends on your strategy, your risk and your readiness today.
Value and priorities
Where AI will create measurable value, the short list of funded priorities, and a clear record of where you will not invest.
Ownership and decision rights
A named leadership owner for each outcome, and clear rules on who can approve, change and stop AI, including limits on what AI agents may do alone.
Workflows and roles
How the work itself changes: which steps AI takes on, where people stay in the loop and how roles and incentives move with it.
Data and technology
The data, platforms and suppliers each priority depends on, and whether they are ready to carry it.
People and capability
The skills, leadership and change support needed for adoption, and where you build, buy or borrow capability.
Governance and measurement
Controls in proportion to risk, an AI register, a baseline for every priority and a quarterly report the board can rely on.
AI operating model design
How AIPEX designs your AI operating model
We design the operating model with your leadership team, one part at a time, starting from evidence of readiness. Each part has its own guide.
Readiness
Evidence of your current state across 30 areas, so the design starts from what your foundations can support.
AI Readiness Assessment →Use case prioritisation
A short list of funded priorities scored on value, feasibility, readiness and risk.
How to prioritise AI use cases →Target operating model
The future-state design: value, ownership, workflows, data, people and controls.
The AI target operating model →Roles and responsibilities
Who owns, decides and delivers AI, from the board to each AI system.
AI roles and responsibilities →Governance operating model
Committee, register, risk tiers, controls and board reporting in proportion to the risk.
The AI governance operating model →Roadmap
The first 90 days and a 12-month plan to move from current to target state.
The AI implementation roadmap →Design choices
Centralised, hub and spoke, or federated
- Centralised: an AI centre of excellence sets standards and delivers. Fast control, but can become a bottleneck
- Hub and spoke: a small AI centre of excellence sets standards and governance, and business units deliver. The most common enterprise AI operating model
- Federated: business units own AI within shared guardrails. Fast, but needs mature governance and data
- The right choice follows your readiness. The AI Readiness Assessment shows which model your foundations can support today
Agentic AI
AI agents make the operating model a board question
An agentic AI operating model has to answer what an agent may do without a person, who is accountable for its actions, how it is monitored and how it is stopped. Those are design decisions about authority and control, and they belong with leadership.
Autonomy limits
What each agent may decide, spend or send on its own, and where a person must approve.
Accountability
A named owner for every agent, and the action they take when it goes wrong.
Oversight
Monitoring, logs and a reporting line to the board, in proportion to the risk.
What you get
What leadership teams leave with
- An AI operating model design matched to your strategy and your readiness
- Clear AI roles and responsibilities: named owners and decision rights for every priority, including AI agents
- The workflow and role changes each priority needs to reach the P&L
- An AI governance operating model: committee, controls and reporting in proportion to the risk
- A value case, baseline and measures for each priority
- An AI adoption roadmap: the first 90 days and a 12-month plan
How it works
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Assess
The AI Readiness Assessment scores 30 areas across six domains, so the design starts from evidence of what your foundations can support.
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Design
An Executive AI Strategy Day with your leadership team: priorities, owners, decision rights and the operating model choice.
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Implement
Changes to workflows, roles, data and governance led through to results, often by a Fractional Chief AI Officer.
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Measure
Quarterly board reporting against the baseline, so the operating model is judged on results, not activity.
Want to see where you stand first? The free AIPEX Score check gives a headline view in about 3 minutes. The full AI Readiness Assessment scores the foundations your operating model depends on.
Free board download
AI ROI Board Readiness Scorecard
A 15-minute board test of whether your AI returns would survive the CFO: five areas, 20 evidence-based checks and a clear result.
- Five areas with 20 checks: value case, baseline, measures, ownership and board reporting
- A simple score: Board ready, Value unproven or Activity, not value
- How to turn time saved into a number the CFO will accept
FAQ
Frequently asked questions
What is an AI operating model?
An AI operating model is how an organisation turns AI into results. It sets where AI will create value, who owns each outcome, who can approve and stop AI decisions, how workflows and roles change, what data and technology it relies on, and how the board sees risk and return.
What does an AI operating model framework include?
The AIPEX framework has six components: value and priorities, ownership and decision rights, workflows and roles, data and technology, people and capability, and governance and measurement. Each needs a clear design choice and a named owner.
What is an AI target operating model?
An AI target operating model describes how the business should run once AI is embedded: the value it delivers, who owns it, how work and roles change, and how it is governed. It is set against the current state, so the gap becomes the AI adoption roadmap. See the AI target operating model.
What is an enterprise AI operating model?
It is the operating model applied across a whole organisation rather than one function. Most enterprises use a hub and spoke design: a small central team sets standards, governance and shared platforms, and business units own delivery and results.
What is an AI centre of excellence?
An AI centre of excellence is a small central team that sets AI standards, governance and shared platforms, and supports business units to deliver. It is the hub in a hub and spoke operating model. It works best with a clear mandate from the board and a limited, funded list of priorities.
What is an AI governance operating model?
It is the governance part of the AI operating model: who sits on the AI committee, what the board sees, how AI is registered, risk-tiered and approved, and who can stop it. It should be in proportion to the risk. See the AI governance operating model.
What are the key AI roles and responsibilities?
At minimum: a board sponsor, an executive owner for AI overall, a named business owner for each AI use case and agent, a risk and compliance lead, and a data owner for each critical data set. Clear AI roles and responsibilities matter more than new job titles. See AI roles and responsibilities.
Who owns AI in a business?
The board is accountable, and one named executive owns AI day to day, often a Chief AI Officer. Business leaders own the results in their areas. Where nobody on the board has the time or experience, a Fractional Chief AI Officer fills the role.
How do you prioritise AI use cases?
Score each candidate on value, feasibility, readiness and risk, against an outcome the board already tracks, then fund three to five with an owner and a baseline. See AI use case prioritisation.
How does AI business process automation fit into the operating model?
Automation is one of the ways AI creates value, but automating an unchanged process rarely pays back. The operating model decides which processes to redesign first, who owns each one, where people stay in the loop and how the result is measured, so automation reaches the P&L instead of moving work around.
What is the AI productivity paradox?
It is the gap between the time AI saves individuals and the results the business can see. People work faster, but workflows, roles and targets stay the same, so the time saved is absorbed and never shows in the numbers. Closing the gap is an operating model job: redesign the work, set a baseline and decide where the capacity goes.
What is an AI adoption framework?
A structured way to move from AI pilots to AI in everyday work. The AIPEX approach combines the operating model, which sets owners, workflows and controls, with an adoption roadmap: assess readiness, agree priorities, change one workflow at a time and measure against a baseline. The same framework applies to enterprise, generative and agentic AI, with tighter controls where AI acts on its own.
How does agentic AI change the operating model?
AI agents take actions, not just produce answers. The operating model must set what each agent may do without a person, who is accountable for it, how it is monitored and how it is stopped. See our briefing on agentic AI risks and controls.
How do you design and implement an AI operating model?
Start with evidence of readiness, agree the priorities and owners with the leadership team, choose the design that your foundations can support, then change workflows, roles and governance one priority at a time, measured against a baseline. Implementation is where most value is won or lost.
Who evaluates AI operating model effectiveness?
The board, through a named executive owner. Effectiveness is judged on measured results against a baseline, how many priorities reach production, and whether risk stays within the limits the board has set. The AI Readiness Assessment gives the starting baseline and can be repeated to show progress.
How does the AI Readiness Assessment fit in?
It is the first step. The AI Readiness Assessment scores 30 areas across strategy, cyber, data, AI, infrastructure and change, and shows which parts of the operating model your foundations can support now and what to fix first. £999 + VAT, including a debrief with a senior partner.
What do firms specialising in AI operating model design do?
They help leadership teams decide how the business will run with AI: priorities, ownership and decision rights, workflows and roles, data, people and governance. Good ones start from evidence of readiness, design with the leadership team rather than for it, and stay independent of technology vendors.
How is AIPEX different from other AI operating model consulting firms?
AIPEX specialises in AI operating model design. It works at leadership level, is independent of technology vendors and starts from the business outcome. Every design choice comes with an owner, a measure and a date, and a Fractional Chief AI Officer can lead the change through to results.
Next step
Talk to us about your AI operating model
A confidential 30-minute scoping call with a senior partner about how AI should change the way your business runs, and where to start. We reply personally, usually within one working day.