Technology, data and AI. Chief AI Officer
AI for the Chief AI Office
AI spend is easy to see. AI value isn’t.
You inherited dozens of pilots, five model providers and a board that wants a number. Some use cases work, most never leave the lab, and nobody can say what the whole portfolio is worth. We map the Chief AI Office the way you actually run it, then put agents and shared foundations where they turn AI into value the CFO signs off.
The short answer
One portfolio of use cases with owners and value targets, one governed platform every team builds on, data that agents can trust, and value tracked with finance from day one.
Agents help run all four. They keep the registry live, route tasks to the right model, watch quality and cost in production and report value against plan. You get to spend your time on the bets that matter, not on chasing status.
The mandate
Turn data and AI into enterprise value. Safely, on one platform, instead of fifty scattered pilots.
What the board measures you on
EBIT impact from AI
Against the plan
Use cases in production
Not in the lab
Idea to production time
How fast value arrives
Cost per AI task
Models and infrastructure
Governance coverage
Every system registered and rated
Adoption
People actually using it
The canvas of the Chief AI Office
BCG finds that about 70% of the value in AI comes from people and process, 20% from technology and data and only 10% from the algorithms. The canvas reflects that.
Canvas 1
What you run
The engine that turns AI into value.
AI portfolio
The use case pipeline from idea to scale.
The AI platform
Where agents are built, run and controlled, with the CIO.
Model and agent operations
Deploying, monitoring and improving models and agents.
AI centre of excellence
Skills, standards and reusable components.
Adoption and change
Helping teams work with agents, not around them.
Canvas 2
What you answer for
The things the CEO, the board and regulators hold you to.
AI policy and the EU AI Act
Risk tiers, human oversight and documentation.
Value from AI
Reported in numbers finance accepts.
Model and vendor choices
Which models and providers the company depends on.
Data readiness
Quality, lineage and access, with the Data Office.
Where value leaks
MIT’s NANDA study found that 95% of generative AI pilots showed no measurable impact on profit and loss. These six leaks are usually why.
01
Pilots that never scale
Use cases stall between proof of concept and production.
Validation First pilots. Owner, value target, kill criteria and controls set up front, built on the production platform from day one.
Pilot to production rate
Idea to production time
02
Unmanaged model cost
Every task goes to the most expensive model.
A decision layer. Routes each task to the right size of model and tracks cost per task for every agent.
Cost per AI task
Model spend
03
Duplicate use cases
Business units build the same thing twice, with different vendors.
Registry agents. Keep one registry of agents and reusable components and flag overlap before money is spent.
Reuse rate
Duplicate spend
04
Data not ready
Key knowledge sits in documents and silos agents can’t reach.
Data agents. Check quality, fix gaps and extract structured facts from unstructured documents.
Data quality score
Data usable by agents
05
Value not measured
AI spend is visible. AI value is not.
Value tracking agents. Link each agent’s use to the KPI it was meant to move and report it monthly with finance.
EBIT impact against plan
Use cases with a baseline
06
Adoption that stalls
The agent works. People keep doing it the old way.
Adoption agents. Track real use by team and task and point training and support where it’s needed.
Active users
Tasks handled by agents
Process lifecycles
We start from the platforms and tools you already run. The orange steps are where an agent takes on the work, so your team spends its time on the bets and the people.
Idea to value
Every use case
Intake
Assess
Value, cost, risk, reuse
Agent
Validate
Build
Deploy
Measure
Against the baseline
Agent
Scale or stop
Typical systems. Portfolio tools, Jira, Cortex
Model and agent operations
Every agent in production
Develop
Evaluate
Quality and safety tests
Agent
Deploy
Route
Right model per task
Agent
Monitor
Quality, cost, drift
Agent
Retire
Typical systems. Azure AI Foundry, AWS Bedrock, Vertex AI, Cortex
AI governance
Every AI system
Register
Owner, purpose, data
Agent
Classify
EU AI Act risk tier
Agent
Document
Drafted from the build
Agent
Human oversight
Report
To the board and regulators
Agent
Typical systems. AI registry, GRC tools, Cortex
Adoption
Every rollout
Design with users
Train
Roll out
Measure use
By team and task
Agent
Reinforce
Targeted support
Agent
Typical systems. Agent logs, learning platforms, collaboration tools
Agent intervention point
The Chief AI control tower
Not a slide of logos and pilots. A live view of every use case, what it costs, what it returns and what needs a decision.
Illustrative
Chief AI control tower
Sample data to show the view. Not client results.
Value from AI
€6.8m
of €12m plan
In production
9
▲ 3 this quarter
Cost per task
€0.03
▼ 38%
Reuse rate
41%
▲ 12 pts
Active users
63%
▲ 9 pts
Not yet rated
7
Risk tier missing
This week’s AI brief
Stalled
Four pilots past their decision date with no owner in the business
Combined spend €1.1m. Scale or stop recommended
Cost
Document agent sends 80% of tasks to the largest model
Routing change saves an estimated €14k a month
Overlap
Two units building contract review agents with different vendors
One shared component proposed
Value
Finance close agent confirmed at €1.4m a year by the CFO Office
Moved from pilot to scaled
Value realised by use case
Finance close
€1.4m
Service
€1.2m
Pricing
€1.0m
Invoice audit
€0.6m
Four use cases deliver 62% of AI value. All four run on the shared platform.
Illustrative example. Figures are invented to show how the view works. In discovery we build it on your own data and KPIs.
Your industry changes the agenda
The canvas stays the same. Where AI pays back first, and where the rules bite, does not. Here’s the question we hear most from AI leaders in each industry, and where we’d start.
CPG
Which AI bets move margin, not just marketing output?
Portfolio review against margin KPIs
FMCG
How do we scale one use case across thirty markets?
Reusable components on one platform
Retail
How do we run pricing and service agents without brand risk?
Guardrails, monitoring and human oversight
D2C
How do we keep model costs in line as volume grows?
Model cost routing
Automotive
How do we govern AI in products and in operations at once?
Registry and Annex I readiness
Healthcare
How do we use AI where the rules on patient data are strictest?
Risk tiering, documentation and oversight
Energy
How do we bring AI into critical operations safely?
Evaluation and monitoring for operational agents
Manufacturing
How do we move from plant pilots to every plant?
Validation First pilots built for scale
Logistics
Which document and planning agents pay back first?
Portfolio review and data readiness
BFSI
How do we meet model risk rules and the EU AI Act together?
Registry, model risk controls and audit trails
B2B technology
How do we use AI inside the business, not just in the product?
Internal agent portfolio and adoption
How the Chief AI Office connects
The Chief AI Office doesn’t own the data, the platform or the people who use AI. It answers for all of them. The agents that serve it share the same registry and controls as every other office, so the portfolio view is the same everywhere.
The AI delivery relay
Technology, data and AI
See where AI gets stuck between your offices.
The ambition
CEO Office
Where the AI budget and the board’s questions start.
The platform
CIO Office
Where agents are built, run and controlled.
Service
AI governance and the EU AI Act
Registry, risk tiers and oversight.
How we’d start
Pilot 1
AI portfolio review
↳
Every use case and agent listed with owner, stage, cost and risk tier
↳
Each one scored on value, readiness and reuse
↳
A scale, fix or stop recommendation, with the evidence
Your decision
What to scale this year, and what to stop funding.
Pilot 2
Model cost routing
↳
Your three most used agents routed through a decision layer
↳
Each task sent to the right size of model, quality checked
↳
Cost per task and quality compared with today
Your decision
Whether every agent runs through the decision layer.
Both run under our Validation First Framework. They’re tested on your own data before you commit a larger budget, and if the numbers don’t hold, we tell you.
Your intervention map
Bring every AI use case you know about and the number the board expects. We bring the Chief AI Office canvas. In one session we map your portfolio, platform and governance and mark where value would come fastest.
01
Your Chief AI Office canvas
What you run and answer for, checked against how your teams, platforms and partners really work.
02
Your value chain
Your AI portfolio placed on your industry value chain, so you see where AI matters most.
03
Your intervention points
Each leak with an agent design, its controls and the KPI it moves. Delivered as a mind map, an architecture view or an interactive dashboard.
AI for the Chief AI Office, answered
What does a Chief AI Office need to succeed?
Four things. One portfolio of AI use cases with clear owners and value targets, one governed platform every team builds on, data that agents can trust, and value tracked with finance from day one. Agents help run all four, from the registry and model routing to monitoring and value reporting.
How do you move AI pilots into production?
By deciding what success looks like before the pilot starts and building on the production platform from the first sprint. Our Validation First Framework sets a business owner, value target, kill criteria and controls for every pilot, so the move to production is a decision, not a rebuild.
How should a company measure the ROI of AI?
Against numbers finance already tracks. Each use case gets a baseline, a target and a named KPI such as cost per case, days to close or conversion. Value tracking agents link agent usage to that KPI every month, so the AI portfolio is reviewed like any other investment.
How can we reduce the cost of AI models?
Route each task to the right size of model. A decision layer sends simple work such as classification and extraction to small, cheap models and keeps large models for complex reasoning. Cost per task is tracked for every agent, so savings are visible and stay in place.
What does the EU AI Act require from a Chief AI Office?
An inventory of every AI system, its risk tier, documentation, human oversight and monitoring. Under the Digital Omnibus, high risk obligations for Annex III systems apply from 2 December 2027 and for Annex I from 2 August 2028. A live registry and logged agent actions make the evidence easy to produce.
Where should a Chief AI Officer start?
With a portfolio review. List every AI use case and agent, score each on value, cost, risk and readiness, and decide what to scale, fix or stop. Then route the most used agents through a decision layer to cut model cost. Both pay back within a quarter.
