Services · AI Governance Advisory
AI Governance Advisory
Policy, process, and oversight structure design for organizations standing up an AI governance function. We help define who approves what, what evidence gets retained, and how AI risk decisions get escalated and documented.
What AI governance advisory covers
AI use policy design
Defining which uses of AI are permitted, which require additional review, and which are prohibited — with enough specificity to be enforceable rather than aspirational.
Accountability structure
Establishing who is responsible for each AI system across development, deployment, and post-deployment monitoring — including escalation paths when something goes wrong.
Approval and intake processes
Designing the workflow by which new AI projects enter the governance process, what review is required before deployment, and what documentation must be retained.
Risk documentation standards
Templates and standards for the documentation that governance requires — model cards, risk assessments, monitoring reports, and incident records.
AI incident response
Process design for detecting, triaging, escalating, and resolving AI incidents — including the reporting obligations that regulatory frameworks may impose.
Board and executive reporting
Defining what AI risk information flows to board and executive level, at what frequency, and in what form — translating technical risk into governance-relevant language.
How we approach it
Governance design starts with inventory: what AI systems exist, who owns them, what decisions they make, and what accountability currently exists. From there we identify the gaps between current practice and the governance structure that regulatory context and organizational maturity require.
We produce implementable artifacts, not frameworks. The output is a policy document your legal team can adopt, a process map your operations team can follow, and templates your developers can fill in — not a generic governance guide that requires another consulting engagement to operationalize.
What you receive
An AI use policy, an accountability and escalation structure, intake and approval process documentation, risk documentation templates, and an incident response workflow — calibrated to your regulatory context and organizational size rather than generic.
Who this is for
Organizations deploying AI in regulated environments who need governance structure before an audit asks for it, legal and compliance teams who need a framework to operate from, and executives who need to know what their AI risk accountability structure looks like.
Frequently asked questions
What does AI governance actually involve?
AI governance is the set of policies, processes, accountabilities, and oversight structures that determine how an organization develops, deploys, and monitors AI systems. Effective governance defines which uses of AI require approval and at what level, what documentation is retained for each deployment, how AI incidents are detected and escalated, and who is responsible for ongoing monitoring. Governance without implementation is paperwork; implementation without governance is uncontrolled risk.
When should an organization stand up a formal AI governance function?
Organizations typically need formal governance when they have multiple AI systems in production, when those systems operate in regulated contexts, when procurement or audit processes are beginning to ask for governance evidence, or when internal stakeholders lack a shared understanding of who is accountable for AI decisions. The cost of establishing governance after an incident is consistently higher than establishing it before.
How is AI governance different from general IT governance?
IT governance addresses deterministic systems where behavior follows documented specifications and failures are typically traceable to specific causes. AI governance addresses probabilistic systems where behavior is statistical, failures can be subtle and gradual, and accountability is more difficult to assign. AI governance must also address fairness, bias, and interpretability — dimensions with no analog in conventional IT governance frameworks.