AI Governance & Risk

Use AI with confidence. Keep the business in control.

Good AI governance gives business leaders a clear way to decide what is acceptable, who is accountable and what evidence is required — without turning sensible progress into a compliance exercise.

Book a Conversation See what governance needs to cover

For established and mid-sized UK businesses setting responsible foundations before AI activity begins, or bringing clearer control to tools, pilots and suppliers already in use.

Leadership control view

Decision framework4 essential checks

Before an AI decision moves forward

Leadership can see whether the essentials are understood and who owns the next decision.

01Purpose & ownershipBusiness purpose and accountable owner are clearClarify

02Data & rightsData, privacy and IP use are understoodAssess

03Risk & dependenciesMaterial risks and dependencies are visibleExpose

04Oversight & monitoringControls are proportionate to the decisionDefine

Available leadership decisions

ApproveSet conditionsEscalateStop

Our governance principle

Enable judgement. Do not replace it with paperwork.

AI governance is the structure that helps leaders make and maintain responsible decisions. It connects principles to ownership, risk assessment, approval, delivery and review.

Governance does not eliminate risk. It gives the business a consistent way to understand risk, decide what is acceptable and remain accountable as circumstances change.

When governance is needed

Before the first initiative — and after activity is already underway.

You are setting the rules before activity grows

The business is deciding how employees and teams may use AI and wants sensible boundaries from the outset.

Tools are already entering the business

AI is being adopted through individual accounts, software features or suppliers without a consistent view of ownership and risk.

A pilot is becoming operational

An initiative has shown promise and now needs clearer controls, accountability and monitoring before wider use.

Leadership needs a coherent view

Different teams own parts of the picture, but the board cannot yet see the organisation's AI exposure, decisions and priorities as a whole.

The governance coverage

See the whole decision, not one category of risk.

Effective governance brings the relevant perspectives together. The depth of review should reflect the context, consequence and degree of autonomy involved.

Purpose & accountability

Make the business purpose, sponsor, accountable owner and decision route clear before activity moves forward.

Data, privacy & confidentiality

Understand what information is used, where it goes, who can access it and what obligations follow.

Security & resilience

Consider access, supplier security, failure modes, continuity and the consequences of misuse or disruption.

Law, regulation & contracts

Identify relevant legal, regulatory and contractual requirements, with specialist advice where it is needed.

People, fairness & oversight

Define appropriate human judgement, transparency and safeguards for employees, customers and others affected.

Operations, performance & value

Set expectations for testing, monitoring, incident response and whether the initiative continues to justify its place.

Proportionate by design

Not every use of AI needs the same level of control.

A low-consequence assistive task should not face the same process as AI influencing employment, safety, finance or a material customer outcome. The control should fit the decision.

Classify the context and consequence — not simply the technology.

Routine assistive use

Clear rules and human checks

Approved tools, practical boundaries and responsibility remaining with the person using the output.

Managed operational use

Named ownership and structured review

A defined purpose, data and risk review, testing, controls and performance monitoring.

Material or high-impact use

Senior decision and deeper assurance

Specialist input, formal approval, stronger evidence, human oversight and continuous review.

How we establish governance

From principles to everyday decisions.

Governance becomes useful when it is embedded in how the business selects, approves, delivers and reviews AI — not kept in a policy that sits apart from the work.

Discover

Create visibility of current and proposed AI use, including embedded tools and third-party suppliers.

Assess

Understand the purpose, affected people, data, consequences and material dependencies.

Decide

Approve, add conditions, escalate or stop using clear decision rights and proportionate criteria.

Mobilise

Put ownership, controls, evidence, reporting and response arrangements around the decision.

Review

Monitor what changes, learn from experience and strengthen, scale or stop based on evidence.

See the AI Transformation Operating System

What the work should produce

A governance foundation the leadership team can use.

The precise outputs depend on the organisation and the risks involved. The aim is a practical operating framework, not compliance theatre.

An AI governance charter and practical policy framework

Clear ownership, decision rights and escalation routes

A register of material AI tools, suppliers and initiatives

Proportionate risk classification and review criteria

Expectations for data, privacy, IP and security

Human oversight, testing and monitoring requirements

Supplier assurance and evidence expectations

Leadership reporting and a regular review cadence

Illustrative governance situations

What proportionate control can look like in practice.

These are illustrative scenarios, not legal guidance or claims of historic client work. Each organisation must assess its own context and obligations.

Illustrative01

Public AI tools in everyday work

Situation: Employees are using generative AI to draft, summarise and research, often through personal or unapproved accounts.
Leadership question: What uses are acceptable, what information must stay out and which approved tools should people use?
Governance response: Set clear usage rules, approved routes, practical training and proportionate monitoring without trying to prohibit every low-risk use.

Illustrative02

AI-assisted customer communication

Situation: A team wants AI to help produce or respond to customer communications more quickly and consistently.
Leadership question: Where must human judgement remain, how will quality be tested and what happens when the system is wrong?
Governance response: Define accountable ownership, review thresholds, data boundaries, testing and a route for exceptions and incidents.

Illustrative03

Supplier-led decision support

Situation: A technology supplier proposes AI that will influence an important operational or people-related decision.
Leadership question: Can the business explain, challenge and remain accountable for the outcome rather than delegating responsibility to the supplier?
Governance response: Apply deeper due diligence, specialist review, evidence requirements, human oversight and ongoing performance monitoring.

Connected leadership questions

Apply governance to the choices that matter.

Find AI Opportunities

Where could AI create meaningful business value?

Develop credible opportunities with risk, ownership and readiness visible from the start.

Explore AI opportunities

Prioritise AI Investment

Which opportunities should we actually pursue?

Compare value, feasibility, risk and readiness before committing resources.

Explore AI prioritisation

A practical first conversation

Where does AI governance need to start in your business?

We will look at how AI is being considered or used, where ownership and boundaries are unclear and what level of control is proportionate to the decisions ahead.

Book a Conversation

Make current activity visible

Clarify ownership and boundaries

Define a proportionate next step