Governance design
Decision rights, escalation, and sign-off that a committee can actually enforce.
Capability
Responsible AI & governance
Clarity at the top is what makes speed possible further down.
Our point of view
Governance is usually positioned as the cost of using AI. We position it as the enabler: once the boundaries are settled, teams stop re-litigating the same question for every use case.
The practical core is a decision-rights model with teeth. Which decisions may never be delegated; what evidence is required before a model influences a customer outcome; who signs when harm occurs; how model risk is reported alongside financial and operational risk; how the board stays literate.
Regulation is a design input, not an afterthought. We map obligations to controls early so that compliance is built into the workflow rather than audited onto it afterwards.
What we do inside this capability
Risk frameworks
Model inventories, tiering, validation standards, and reporting to board and regulator.
Regulatory mapping
EU AI Act, sector rules, and jurisdictional obligations translated into workflow controls.
Assurance and audit
Evidence packs and reproducibility standards that survive internal and external audit.
Research behind this work
Article
AI in the boardroom: governance that accelerates rather than blocks
Boards that understand AI well enough to set boundaries move faster than boards that outsource the question to a committee.
21 August 2026 · 7 min read
Article
Financial services after AI: the new economics of trust
When underwriting, advice, and monitoring are partly automated, the differentiator stops being the model and becomes the evidence trail around it.
12 June 2026 · 11 min read
Next step
Where would responsible ai & governance move the needle for you?
Bring a live decision. We will bring the pattern from comparable organisations — including the failures.