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Decision rights and evidence
AI governance consulting
AI governance consulting establishes who can approve, change, monitor and stop an AI-enabled workflow, and what evidence those decisions require. predeploy helps connect policies to the systems your organisation actually uses. This is practical governance design and implementation support. It differs from teaching governance concepts or providing a policy template, and it does not certify legal compliance or replace specialist advice.
Leave with named decision owners, a proportionate control plan and an evidence record for a defined AI workflow.
NIST describes its AI risk framework as voluntary guidance for design, use and evaluation. NIST: AI Risk Management Framework ↗
NCSC guidance covers design, development, deployment and operation. NCSC: secure AI system development ↗
New to the topic? Read the background on artificial intelligence.
Decision rights and evidence
where should governance work begin?
Identify the system, its intended use and the person accountable for its operation.
A policy cannot be applied well if the organisation does not know which workflow it covers or who owns it. Record the inputs, users, supplier dependencies and consequences of error. The voluntary NIST AI Risk Management Framework provides background for risk management. AI governance training teaches relevant concepts; this consulting engagement turns the agreed responsibilities into practical decisions and records for a particular scope.
Boards overseeing AI change can use independent AI transformation advisory.
Identify the workflow.
Name the accountable owner.
Record its intended use.
Decision rights and evidence
which decisions need an explicit owner?
Assign responsibility for approval, access, change, incidents and withdrawal.
An illustrative document assistant may have a business owner, a technical maintainer and a separate information owner. Specify who approves its use, who changes sources and who pauses it when results are unreliable. AI operating model consulting addresses how those responsibilities are organised across a portfolio. Governance defines the decisions and evidence; the operating model defines the recurring teams, capacity and handoffs that deliver them.
Decision rights and evidence
what controls should be documented?
Choose controls that address the workflow’s actual risks and can be demonstrated.
For a source-based assistant, relevant checks may include permitted retrieval, unsupported answers, changes in source versions and human review before consequential use. The NCSC secure development guidelines provide lifecycle security guidance. AI evaluation consulting develops representative examples for testing behaviour. A lengthy checklist is not evidence that controls work; keep the test result and the responsible reviewer alongside the control description. The AI governance framework guide sets out inventory, decisions, evidence and review. AI risk assessment provides a task-specific risk record, and LLM evaluation explains how test evidence can support an approval instead of becoming an isolated quality score.
| Check | What to establish |
|---|---|
| 1 | Connect controls to specific risks. |
| 2 | Keep test evidence. |
| 3 | Name the reviewer. |
Decision rights and evidence
how does governance connect to policy and procurement?
Carry the same requirements into permitted-use guidance and supplier assessment.
Use the AI policy template to structure allowed-use rules, then identify what each supplier must explain about data access, operation and exit. AI consulting RFP support makes those evidence requirements visible to bidders. Questions about legal duties should be resolved with the relevant specialist; a general framework cannot establish the applicable rule for every deployment or promise that a procurement decision satisfies it.
Decision rights and evidence
how is governance maintained after launch?
Review changes, failures and exceptions using the same decision record.
Agree which events require reassessment: a new data source, a new action, a different user group or a material change in supplier behaviour. Keep a change record and a route for reporting an incident. AI operations consulting covers recurring service review. The governance scope should specify which records predeploy prepares, which decisions remain with the organisation and who maintains the controls once the engagement ends.
Define reassessment triggers.
Keep exceptions visible.
Assign ongoing maintenance.
Make the decision concrete
What does the current task cost in time?
Choose a task connected to ai governance consulting and enter its current workload. This gives you a baseline to discuss before buying training or commissioning a build.
A baseline is useful even if you decide to keep the process manual. Measure the work that matters, then compare quality and review effort as well as time.
Assess the workflow before changing it, or plan business AI training if the gap is how people use existing tools.
Your current workload
Estimate how much time the existing task takes before discussing ai governance consulting.
16 hours per week
People × minutes × weekly tasks ÷ 60. A workload baseline from your inputs, not a prediction of AI time savings. Include review and support time when comparing a proposed workflow.
A useful AI investment leaves your team able to do the work and judge the result.
Continue with the right question
Where should you go next?
Use the overview for context, then follow the pages that match your delivery decision.
See our ai consultancy service or read the ai implementation consulting overview.
FAQ
Questions, answered
Evidence and further reading
Which sources support the guidance?
Source links support the specific background described here. The practical checklists and illustrative examples are predeploy’s guidance.
NIST describes its AI risk framework as voluntary guidance for design, use and evaluation. NIST: AI Risk Management Framework ↗
NCSC guidance covers design, development, deployment and operation. NCSC: secure AI system development ↗
Background reading on the broader field of artificial intelligence. Wikipedia: artificial intelligence ↗
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