Cloud governance asked where your data lived. AI governance also has to ask who checks the model’s answers and who owns the decision when they’re wrong.
Our last four posts drew lessons from the cloud years. Here is where the comparison breaks down.
A hosted server returns exactly what you put in. Upload a contract and you get the same contract back, byte for byte. A language model works differently. Ask it to summarize that contract and it returns a plausible answer that is usually right and occasionally, confidently wrong. It can miss a clause, invent a date or misread a figure, and the summary reads just as smoothly either way.
That difference moves the main control point. In the cloud era, governance meant knowing where data sat and who could reach it. Those questions still matter, as our last post on hosting and data location showed. With AI, governance also means checking what the model produces before it reaches a client, a regulator or a balance sheet.
For an SME, the practical step is to put these questions into procurement, next to price and security. Before you sign anFor an SME, the fix starts with people. Every AI workflow needs two names:
- A reviewer, who checks the output before it leaves the building. For a meeting summary, a quick read is enough. For a contract extraction or a client reply in a regulated field, the reviewer should be someone qualified to sign it.
- A decision owner, who answers for the outcome when the output is wrong. The model can’t be held accountable; a person has to be.
In a small firm, one person can hold both roles, as long as both are written down before the workflow goes live.
The Second Migration sets out a one-page AI governance model for SMEs, along with a workload-by-risk matrix that shows how much review each task needs.
Read the full paper:
The Second Migration – What the Cloud Years Teach SME Leaders about AI
