Most leaders who deploy AI do not think of themselves as creating new jobs. They think of themselves as eliminating them — or at least, reducing the human effort required to get things done. Dr. Jonah Tebaa argues that this framing is precisely where organizations lose control of their AI investments.
The problem is not the technology. The technology does what it is designed to do. The problem is the assumption baked into every AI rollout: that delegating a task to a system means the task is handled. In Dr. Tebaa's experience working with organizations across the MENA region and internationally, what actually happens is more complicated — and more costly.
When the System Completes in Silence
Human operators escalate. When something is wrong, ambiguous, or outside the parameters of a task, a person signals. They ask. They pause. They flag the exception before it becomes a consequence.
AI systems do not work this way. They complete silently or they fail silently. A workflow either produces output or it does not — and in either case, the system has no mechanism to communicate whether the output is contextually right, whether it reflects a decision the business still stands behind, or whether an edge case was handled in a way that will create a problem three weeks from now.
Dr. Tebaa's observation is that this is where the damage accumulates — not in headline failures, but in slow drift that no one is watching for, because no one was assigned to watch.
Naming the Ownership Gap
In his work, Dr. Tebaa identifies what he calls the ownership gap: the structural distance between the executive who authorized an AI deployment and the person who must act when the output is wrong. In most organizations, that gap is never deliberately closed. The system goes live. The dashboard turns green. And downstream, in a function that was never involved in the deployment decision, someone begins quietly inheriting consequence without being told they own the job.
The distinction he draws is between monitoring and ownership. A monitor checks whether the system is running. An owner is accountable for what it produces — armed with judgment criteria, escalation authority, and a clear mandate to intervene when output drifts from intent. Most organizations design for the first and neglect the second entirely.
Article 12 of the EU AI Act, on record-keeping writes the same requirement into law: logs exist in order to ensure a level of traceability of the functioning of a high-risk AI system that is appropriate to the intended purpose of the system.
AI Deployment as Role Redesign
Dr. Tebaa's framework reframes AI deployment as an organizational design problem, not a technology procurement problem. Every automated workflow creates a role: the role of absorbing AI output and converting it into decisions. That role requires human judgment. It requires context. It requires someone with both the information to recognize when output is wrong and the authority to stop the workflow when it is.
The organizations he observes compounding returns are not the ones who have deployed the most automation. They are the ones who have been deliberate about who owns what the automation produces. They have named an accountable human — a specific person, with a specific mandate — and they have redesigned that person's role to make the work of oversight not an afterthought but a primary function.
The organizations still waiting for returns are running the same systems. They simply never posted the job the system created.
A Diagnostic in One Question
Dr. Tebaa uses a single question to assess AI deployment readiness in any organization: If this system produces the wrong output at 2am, who gets the call?
Not who is on call for the infrastructure. Who is accountable for the output — with the authority to act on it, stop it, or escalate it to a decision-maker. If the organization cannot answer instantly, the deployment is structurally incomplete regardless of how sophisticated the technology is or how well the rollout went.
This is not a counsel of pessimism about AI. Dr. Tebaa is direct that the ownership gap is not a reason to delay deployment — it is a reason to add one thing to every project plan that is almost universally missing: deliberate design of the human role that makes the system actually accountable.
The vendor does not design that role. The platform does not design it. The organization must. And in Dr. Tebaa's view, the leaders who understand that early are the ones whose AI investments produce the results they were promised.
For organizations working through these questions, Dr. Tebaa is available for direct consultation via jonahtebaa.com.
