AI Governance Consulting: Decision Rights, Evidence and Handover

AI readiness asks whether to pilot a workflow. Governance defines who may approve, inspect and stop its work, and who receives it after handover.

Start with the action that needs governing, such as preparing a report, changing a record or communicating externally. The useful output is an agreed decision-rights record that connects each action to its authorizer, evidence and receiving owner.

An agreed workflow boundary, decision-rights record, evidence and acceptance requirements, escalation path and receiving-owner record. The mandate determines which of these needs work.

The handover should identify the accepted output, evidence location and access conditions, receiving owners, residual limitations, escalation route, and conditions that would reopen the decision.

The process and technical owners, a domain or risk reviewer, and the data and security owners whose authority the workflow depends on.

An evaluation, often called an “eval,” tests expected behavior using agreed cases. For one case type and reviewer team, set representative routine cases, difficult cases and expected outcomes with the responsible risk owner: unsupported statements, missing sources, conflicting versions, inaccessible or revoked documents, and proposed actions that must not proceed without authority.

Keep acceptance decisions separate. Technical: can the agreed evidence and behavior be inspected, including source updates and access changes? Domain/risk: does the output support the intended use, with unresolved gaps visible? Operational: can the receiving team perform review, stop the workflow and handle exceptions within its capacity?

Compare time per accepted case, rework and critical errors with the existing process. There is no universal accuracy threshold; the risk owner sets the substantive conditions for the actual use. A citation or model score alone is not acceptance. Document what was checked, by whom, and what remains unreviewed: an unknown is not a passed check. For tooling, compare agent governance platform coverage; for data and authority paths, use the AI security risk assessment.

Name who monitors the AI workflow, which signals they watch, how often the receiving owner reviews them, and what sends the workflow back to an acceptance check. Starting points include reviewer overrides, unsupported statements found in review, changes to source permissions and any new action the workflow can take. Adapt the signals and review cadence to the actual use; this is not a tested monitoring checklist.

A later source-permission change, new action capability or rising review burden may justify another bounded check; it does not automatically justify a larger rollout.

Separate approval of a draft from authority to perform a specific action. Bind authorization to the destination and output version, and retain the observed execution result and escalation route.

Drafting permission is not permission to make a financial decision or act externally. Reviewer competence and recovery from a failed action must each be evidenced, for example a tested rollback or a recorded unknown state.

The NIST AI Risk Management Framework is a voluntary reference for organizing AI risk work; confirm the current version when choosing a framework.

A framework organizes the questions. Qualified legal and specialist reviewers determine the requirements for your use and the evidence that satisfies them.

The curator checks which CTO in the network has relevant experience and whether that person will take the scope. Participation is agreed per inquiry.

Marton Csernai's profile reports 100% citation provenance for regulated document analysis; citation provenance concerns traceability, and answer accuracy needs its own evaluation. Neil Catton's profile includes AI and data strategy for public safety and justice organizations. These are attributed profile statements.

First check whether your technical lead or current provider can resolve the question with existing document tools, search, rules or manual review. Senior advisory support is for a material gap in authority, architecture or cross-team ownership.

Scope depends on the number of workflows, action capabilities, data sensitivity, existing evidence, reviewer capacity, specialist acceptance and receiving ownership. Where the decision is understood, scope and estimate can follow directly; a limited diagnostic addresses only material uncertainty. Timing, price, implementation resources and specialist participation are confirmed after those dependencies are understood.

It is not a company-wide policy rewrite. Financial document review is one example rather than the limit of this service, and in the example, financial conclusions and client communications remain outside scope.

This is technical and operational CTO advisory. Legal opinions, certifications, attestations and clinical or financial conclusions belong to the qualified parties responsible for them. The example is illustrative; proposed roles take effect when authorized people accept them. Grand Rounds is an initiative of Dinarys, and a conversation may lead to cooperation with Dinarys.