AI Governance
Practical governance for AI outputs, agents and human-AI systems.
As AI systems become more capable, organisations need governance models that address more than capability. AI outputs can shape judgement through fluency, confidence and apparent authority, while persistent, tool-using agents introduce additional questions of lifecycle, resources, permissions and accountability. Any system with roles, memory, costs, client-facing tasks or operational access requires clear boundaries, human oversight and responsible review.
Capability is not authority. Fluency is not verification. Confidence is not evidence.
A Lightweight Check for AI Output
As AI systems become more capable, one of the risks is not simply that they may be wrong, but that they may be wrong with fluency, structure and apparent authority. Human beings are used to treating coherent delivery as a competence signal because, in human contexts, fluency usually carries some cost: experience, learning, practice, accountability or reputational risk. Generative AI disrupts that cost structure. It can produce coherence without having earned the authority that coherence normally implies.
Before treating an AI output as reliable, ask whether its:
A: Assumptions are stated
What is the system assuming, and are those assumptions explicit, reasonable and relevant
R: Risks are specific
What could go wrong if this output is trusted, acted on or circulated? Risks should be specific to the output, context and intended use, not generic disclaimer language.
S: Scope is bounded
What is this output valid for, and where does its usefulness stop? Has it stayed within the appropriate domain, evidence base and authority level?
E: Evidence is checked
What supports the claim? Are sources, calculations, reasoning steps or verification pathways available, and is the confidence level proportionate to the evidence
Capability is not authority. Fluency is not verification. Confidence is not evidence.
This check can be used by individuals reviewing AI output, and by designers building AI systems that should make assumptions, risks, scope and evidence visible before their outputs acquire authority through polish alone.
Putting AI governance into practice
Good governance is not only a policy or compliance document. It becomes meaningful through the everyday choices people make when they instruct AI, review its outputs and decide what may safely be used.
I offer practical 1:1 AI workflow coaching for individuals and organisations who want to develop confident, useful and well-governed AI practice around real work.
Find out about Practical AI Workflow Coaching