The executives most exposed to AI risk are not the ones who said no.
They are the ones who said yes — approved the budget, endorsed the roadmap, celebrated the launch — and then handed it off.
I have seen this pattern more than once in my work with senior leaders across the region. A decision-maker does everything right on paper: hires capable people, brings in reputable vendors, allocates real resources. Then they step back, as good leaders do, and let their team execute.
The problem is not the stepping back. The problem is what stays behind when they do.
Delegation is a leadership skill. But there is a version of AI delegation that is not leadership — it is exposure. When you delegate AI execution without retaining judgment over its scope, its risk tolerance, and its failure modes, you have not handed off a task. You have handed off accountability. And accountability, unlike a project plan, does not transfer cleanly.
Decision One: What AI Is Permitted to Act On Without Human Review
Every AI system operates within a set of boundaries. The question is who defined them.
In most organizations, those boundaries were drawn by the team closest to the implementation — the product owners, the architects, the vendors who scoped the use cases. These are smart, capable people. But they are not you. They do not carry the same view of what a mistake costs.
When a system is permitted to act autonomously — to send a communication, make a recommendation that drives a business process, filter a decision — someone made a judgment call about where human review was necessary and where it was not. If you did not make that call explicitly, it was made without you.
This is not a technical question. It is a governance question. It belongs at your level.
Decision Two: What Constitutes Acceptable Failure
AI systems fail. This is not a defect — it is a property. The question that matters is not whether the system will make errors, but what kind of errors are acceptable and at what rate.
A system that occasionally surfaces the wrong content recommendation is a different problem from one that occasionally misclassifies a customer risk profile. Both are failures. The tolerance for each is radically different. And the tolerance threshold is a values decision, not a technical one.
I have sat in rooms where this conversation was avoided because it felt premature — "let's see how it performs first." That is a reasonable instinct in a pilot. It is not a governance posture for a deployed system. By the time you are watching it perform, the failure mode you did not define has already been implicitly accepted.
Someone has to draw this line. That someone is you.
Decision Three: Who Is Accountable When the System Optimizes for the Wrong Thing
This is the question most organizations have not answered, because it is uncomfortable.
AI systems optimize for what they are trained and instructed to optimize for. Sometimes the objective was specified correctly. Sometimes it was not. Sometimes it was correct at the time and is now misaligned with where the business has moved. When that misalignment surfaces — in a customer outcome, a regulatory conversation, a press inquiry — accountability does not flow to the algorithm.
It flows upward.
The question is not just who owns the system. It is who owns the judgment that the system's objective is still the right one. That review cannot live permanently at the technical layer. At some cadence, at some altitude, someone with decision authority has to look at what the system is optimizing for and confirm that it matches the organization's actual intent.
If that person is not you, you have delegated something that cannot be delegated.
The Reframe: Literacy, Not Micromanagement
None of this requires you to understand the model architecture or review the training data. That is not the ask.
The ask is that you be literate enough to make these three decisions deliberately — and to make them again when the context changes. What is in scope for autonomous action. What failure looks like and at what threshold it becomes unacceptable. Who answers for the objective.
These are strategic decisions. They require executive judgment. They do not require technical expertise. International guidance frames it the same way: UNESCO's Recommendation on the Ethics of Artificial Intelligence holds that the ethical deployment of AI systems "depends on their transparency and explainability" — and how much of either is enough for your organization is a judgment call that sits at your desk, not the vendor's.
The leaders I respect most in this space are not the ones who went deepest into the technology. They are the ones who stayed close to these three questions while letting their teams execute everything else. That is not micromanagement. That is leadership in a domain where the cost of judgment gaps is unusually high.
Visibility is not oversight. A dashboard showing AI usage metrics tells you what happened. It does not tell you whether what happened was what you would have chosen.
There is an honest question underneath all of this, and I think it is worth sitting with:
Have you made these three decisions explicitly — in writing, in a governance forum, in a conversation that produced a clear answer?
Or are you operating on the assumption that someone below you made them, and made them well?
I am not asking to create doubt about your team. I am asking because the gap between "I delegated the AI program" and "I delegated these three specific judgments" is where most of the exposure lives. And it is a gap that tends to be invisible until it is not.
If this maps to something you are navigating, I would be glad to think through it with you. You can reach me through jonahtebaa.com.
