AI Governance
AI Governance Programme Design
FrontierScale AI designs AI governance programmes that let organisations adopt AI at scale, with clear decision rights, practical controls and evidence that boards and investors can trust.
Why this matters
AI adoption is now an operating-model problem before it is a technology problem. Executives are accountable for AI use cases they cannot always see, supported by controls they have not yet designed. A well-designed governance programme turns AI from a distributed experiment into a governed capability — with clear decision rights, risk-based controls and executive reporting that stand up to scrutiny.
What we address
Problems this engagement solves
Unclear accountability
Ownership of AI use cases, models and vendor tools is dispersed across business, technology and risk teams.
Policy without controls
AI policies exist on paper but lack risk-tiered controls, evidence and enforcement.
Fragmented risk view
AI risk is not integrated into model risk, operational risk, third-party risk or technology risk frameworks.
Weak board reporting
Boards receive activity updates but cannot see AI risk exposure, high-risk systems or control effectiveness.
Unstructured intake
New AI use cases are approved ad hoc, with inconsistent risk assessment and documentation.
Slow, ambiguous decisions
Approvals stall because committees, decision rights and escalation paths are not defined.
Our approach
How FrontierScale AI works
Step 01
Diagnose governance maturity
Assess current AI governance, policies, controls, committees and reporting against leading practice and obligations.
Step 02
Design the operating model
Define committees, decision rights, roles and interfaces with risk, technology and AI delivery functions.
Step 03
Build the policy and control framework
Create AI policy, risk taxonomy, control framework, use case intake, risk tiering and approval workflows.
Step 04
Define assurance and reporting
Design independent assurance, evidence capture and board-level reporting for AI risk and control effectiveness.
Typical deliverables
Board-ready outputs
Every engagement produces evidence-backed artefacts your executives, auditors and regulators can review with confidence.
- AI governance blueprint
- AI policy framework
- AI risk taxonomy
- AI control framework
- Operating model design
- RACI matrix
- Governance committee charter
- Board reporting template
- Implementation roadmap
Who it is for
Best-fit clients
- Chief AI Officers and Chief Data Officers
- Chief Risk Officers and Heads of Model Risk
- CTOs and CIOs building AI operating models
- Boards and executive committees
- Heads of Compliance in regulated firms
Common triggers
When to engage
- New Chief AI Officer or CDO appointment
- EU AI Act, sector rule or supervisory attention
- Rapid GenAI adoption across business units
- Investor, auditor or regulator readiness review
- Merger, carve-out or new operating model
FAQs
Common questions
How is this different from an AI strategy?+
AI strategy defines where and why to invest in AI. Governance programme design defines how AI is adopted safely, with the operating model, controls and reporting needed to make that strategy deliverable.
Do we need this if we already have model risk management?+
Model risk management is one input. AI governance extends it to GenAI, agentic AI, third-party models and non-quantitative use cases, and links it to responsible AI and executive oversight.
How long does a programme build take?+
A typical end-to-end design is 8–16 weeks. Many clients begin with a 2–4 week readiness assessment to prioritise the work.
Related services
Explore related engagements
AI Governance Audit
Independent review of an existing programme against executive expectations.
EU AI Act Readiness
Applicability, high-risk classification, gap analysis and implementation.
AI Transformation Strategy
AI portfolio, operating model and roadmap alignment for the enterprise.
Make AI adoption defensible, governed and investment-ready
Speak with FrontierScale AI about AI governance, EU AI Act readiness, AI safety assessment or AI due diligence.