Should AI governance include decision ownership?
Something I've been thinking about recently:
Let's say an AI agent makes a recommendation and that recommendation ends up influencing a real business decision. A few months later, someone wants to understand why that decision was made. In most cases, it's not that hard to find the model, the prompt, or the output. Teams are getting much better at tracking those things.
What feels harder is understanding what happened in between. Who reviewed the recommendation? Who approved it? What information were they looking at when they decided to move forward with it?
Maybe there was a conversation that wasn't documented. Maybe there was context that seemed obvious at the time but wasn't recorded anywhere. Maybe there were reasons for trusting the recommendation that never made it into a system.
I keep coming back to this because the output is only one part of the story. The decision happens when a person looks at that output and decides what to do next. If that context disappears, it becomes much harder to understand how a decision was made, even when the AI history is still available.
Curious how other teams are thinking about this.
Replies
I guess it depends on the decision, but if we're talking about decisions with regards to how a specific feature should work or how a specific bug should be fixed, then the developer who was working on that task is owning the responsibility for accepting that code (assuming that it's not a vibecoded product). If there is a PR with some code changes, there usually is a task (GH issue/Jira ticket/something else) and if the decision is not straightforward, the developer would leave a comment in the task explaining their decision. If there's anyone who thinks that it should be different, they'd just respond in the task - and this conversation or feedback can be used as a future reference for this decision.
@sk_uxpin I agree this works well when the decision is still close to a ticket, PR, or task.
The gap starts showing up when AI recommendations move across teams and influence real business workflows.
Then the context is often split between prompts, outputs, Slack discussions, approvals, policy checks, and someone’s judgment at that moment.
A Jira comment can explain a code decision, but it may not capture why an AI-driven decision was trusted later.
That is where I think teams need a separate decision layer, not just logs. Something that connects the AI output, human review, approval context, and ownership in one place.
@sk_uxpin For example, imagine a support ops team uses an AI agent to recommend refunds for high-value customers.
The agent suggests approving a refund because the customer had multiple failed orders, the support lead reviews it, someone checks the policy, and the final approval happens after a quick Slack discussion.
Three months later, finance asks why that refund was approved.
The model output may still exist, and the ticket may say “approved,” but the real decision context is scattered.
Who reviewed the case, what policy was checked, what risk was accepted, and why the team trusted the recommendation may not be captured anywhere.
That is the part I think normal tickets and logs don’t fully solve.
@grover___dev to me, this looks like a matter of an incomplete process - any refund approval or other important business decision should be fully documented so that the info is available in 6, or 36, or whatever months from now. In the end, the agent isn't the decision maker, it only suggests doing something, but then there's a human who makes the decision - and in the case the lack of documentation/information is not related at all to the agent
@grover___dev the decision layer was always there, wasn't it? It's the human :)
Vokal