The Handoff Is The Product
Most AI failures live in the gap between the machine and the human
When an AI deployment fails, the postmortem usually blames the model. In my experience that is almost never where it broke. It broke at the handoff.
Every AI system reaches a limit. It encounters something ambiguous, or high-stakes, or simply outside what it handles well. What happens in that moment determines whether the whole thing works.
Four questions nobody asks early enough
How does the human know they are needed? If the answer is that they check a queue, they will not. Escalation has to arrive where they already work.
What exactly do they see? The output alone is not enough. They need the input, the reasoning where available, and the confidence. Reviewing a bare answer is guessing.
What can they change? If they can only approve or reject, you will get approvals. Real review requires the ability to edit.
Where does their correction go? This is the one that gets skipped. If corrections vanish, the system rebuilds the same errors forever and the reviewers learn their work is decorative.
Why this is a leadership issue
Handoff design is not a technical decision, though it looks like one. It determines whether the people doing the work experience AI as a tool or as a judgment on their competence.
A well-designed handoff says: the machine handles volume, you handle judgment, and your judgment improves the machine. A badly designed one says: approve this quickly and do not think too hard.
The second version is cheaper to build and it is why so many deployments quietly stop being used.
The practical test
Before you deploy anything, sit with the person who will receive the escalations and watch them handle ten. If the process embarrasses them, or bores them, or leaves them unable to tell whether they are adding value, it will not survive contact with a busy week.
Handoff design is a module in my manager training program, AI For The People Who Run The Work.