AI governance
For leaders moving from experimentation to accountable AI use.
Typical deliverables
AI inventory, ownership model, acceptable-use policy, and oversight roadmap.
Practical support to assess AI risks, strengthen privacy and ethical oversight, and prepare for regulatory requirements. Turn uncertainty into policies, assessments, and a clear implementation roadmap.
Start with the decision you need to make. Build an engagement around your systems, people, and risk context.
For leaders moving from experimentation to accountable AI use.
Typical deliverables
AI inventory, ownership model, acceptable-use policy, and oversight roadmap.
For teams making decisions that affect people.
Typical deliverables
Stakeholder impact review, fairness considerations, and human oversight recommendations.
For organizations evaluating an AI system or vendor.
Typical deliverables
Evidence review, prioritized risk register, control gaps, and a mitigation plan.
For teams preparing to demonstrate responsible data and AI practices.
Typical deliverables
Data-flow review, requirements mapping, readiness gaps, and documentation priorities.
For decision-makers and teams adopting AI in day-to-day work.
Typical deliverables
Role-specific workshops, practical scenarios, and guidance for safer use.
AI Safety Force focuses on the decisions, responsibilities, and controls that make responsible adoption workable.
Define the AI use case, affected people, decision owners, and questions the engagement must answer.
Examine documentation, data practices, and controls. Record gaps, assumptions, and limitations.
Prioritize recommendations with owners and an implementation roadmap. Agree how progress will be reviewed.
An advisory assessment identifies risks and improvement opportunities within an agreed scope. It does not guarantee legal compliance, certify a system as safe, or replace advice from qualified legal counsel. Any framework mapping should identify the version, applicability, evidence, and limitations.
View an illustrative assessment outline →Our existing frameworks provide a starting point for a scoped advisory engagement. Deliverables and applicable requirements are agreed before work begins.
Notes on AI governance, compliance, and safe implementation.
Cost savings are the easy part of the calculation. The regulatory and ethical exposure is where additional assessment is needed.
A walkthrough of the four categories behind an advisory assessment, and why we score them separately instead of as one number.
Discuss what you are building or adopting, where the uncertainty sits, and what a useful engagement would deliver.
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Describe the decision you need to make and the outcome you want. We can then discuss scope, deliverables, and whether an engagement fits.
Agree the work, responsibilities, timing, fees, and confidentiality arrangements before sharing detailed evidence.