For the Chief AI Officer
Sovereign intelligence for the Chief AI Officer
Move from AI experiments to governed production AI.
The Chief AI Officer is usually asked to deliver value and manage risk at the same time, often without direct control of the systems involved. The pattern that works is a shared operating model: one inventory, one set of autonomy rules and one way to measure outcomes. This page sets out the worries and the questions that test whether a platform supports that model.
What a Chief AI Officer worries about
A portfolio of experiments with no path to production
Pilots succeed in demos and stall at security review because nobody defined what production requires. The gap is rarely the model; it is identity, approvals and evidence.
No inventory of the AI estate
You cannot govern what you cannot list. Models, agents, prompts, data flows and owners need to be inventoried in one place. See the AI estate.
Deciding how much autonomy is safe
Binary choices between manual and autonomous do not fit. You need levels, criteria for moving between them and a record that justifies each move.
Evaluation that stops at launch
Models, prompts and data change after go-live. Without continuing evaluation, quality drifts silently and the first signal is a complaint.
Proving value to the executive team
Activity metrics such as prompts sent do not persuade a CFO. Outcomes need a baseline, an owner and a method for attributing change.
What a Sovereign Intelligence Platform gives you
A shared operating model
Six layers and a trust fabric give product, security, legal and operations one vocabulary. See the platform overview.
Controlled autonomy
Five levels, from Assist to Adaptive, with approval, monitoring, risk bounds and audit depth changing at each step. See controlled autonomy and the rollout plan.
Trust Profiles as the system of record
Each AI system carries identity, owner, risk level, approved models, data classification and approval rules, which doubles as the inventory and the approval artefact.
A route from pilot to operations
The ten-step guide lays out the sequence, from defining requirements to operating and learning, with checklists at each stage.
Outcomes at the top of the stack
The solutions layer is about measurable business results, which feed evidence back into data and context to improve the next release.
Capabilities are described as what the platform is designed to let you do. For what is true today and what is not claimed, see the trust centre.
Questions to ask any vendor
Use this as a checklist in any evaluation, ours included. Each question comes with what a good answer looks like.
01How does the platform produce an inventory of every model, agent and workflow, with an owner for each?
A good answer: An automatically maintained registry with owner, risk level and status, exportable, and a flag for systems with no owner.
02Which autonomy levels do you support, and what changes at each level?
A good answer: Defined levels with different approval, monitoring and limits, and an audit record of who moved an agent between levels and why.
03Can an agent change its own permissions or autonomy level?
A good answer: No. Level and scope changes are made by the accountable owner and recorded.
04How do we evaluate a system before launch and after every change?
A good answer: A regression suite built from our own cases, run on model, prompt, tool and data changes, with a gate before release.
05How do we measure business outcomes, not just usage?
A good answer: Support for outcome metrics defined with the process owner, baselines and comparison against a control group or period.
06What approval workflows exist for high-risk actions, and can approvers be named by role?
A good answer: Threshold-based approvals routed to named people or roles, with timeouts, escalation and a full record of each decision.
07How do we classify AI systems by risk, and does that classification drive controls?
A good answer: A risk field per system that changes required approvals, monitoring and logging. For EU-regulated uses, a mapping to the AI Act categories.
08How do you support multiple models and let us change them without rewriting applications?
A good answer: A gateway with approved-model lists per system, routing rules and tested fallbacks.
09What reporting can I give the board on risk, incidents and value?
A good answer: Dashboards and exports on inventory, incidents, policy decisions, approval rates and outcome metrics, with definitions we control.
10How do you handle systems that were built outside the platform, such as a team's own scripts or a third-party copilot?
A good answer: A way to register external systems in the same inventory with an owner, risk level and data-flow description, and to apply policy at the gateway or connector where the platform can reach, while stating plainly which controls cannot be applied to a tool the vendor does not run.
11How do you help us train and support the people who will work alongside these systems?
A good answer: Role-based guidance, in-product explanations of what the AI can and cannot do, and records of onboarding that support AI literacy duties, rather than a one-off webinar.
12What does a typical path from first use case to enterprise-wide deployment look like?
A good answer: A staged route where each step adds one capability, with exit criteria. Be wary of plans that require committing to the whole platform on day one.
Recommended reading
- Guide: set autonomy levels
- From AI pilot to governed AI operations
- Capability plus control
- Platform: Trust Profile
- Readiness self-assessment
Not sure where to start? Take the readiness assessment or read the build guide.
Frequently asked questions
Who should own AI governance?
A named accountable executive, with a working group across security, legal, data and operations. Ownership of each individual system sits with the business process owner.
Should every agent reach full autonomy?
No. Many should stay at an approval or supervised level permanently. The right level depends on risk, reversibility and the evidence you hold.
What is the first thing to do on Monday?
Inventory what exists. List models, agents, data flows and owners, then rank by risk. Everything else builds on that list.
Build with control: for the Chief AI Officer
Start with one entry point. Add intelligence, agents, workflows and infrastructure as you prove value. Or read the step-by-step build guide and take the readiness assessment.