Enterprise AI Fails at the Handoffs
The model may perform its task well while the wider system fails between people, tools, decisions and exception queues.
Enterprise work rarely ends with an answer. A recommendation must be reviewed, approved, entered into another system, communicated to a customer or routed to an exception owner. AI projects often optimise the model step and leave those handoffs untouched.
The result is a good answer stranded inside a bad process.
Map the complete unit of work
Start before the prompt and finish after the business outcome. Who assembles the context? Which systems must be consulted? Who checks the result? Where is the decision recorded? What happens when confidence is low or the source systems disagree?
This reveals whether the AI removes effort or moves it. A generated summary that takes ten minutes to verify and five minutes to paste into the system of record may save less than the demonstration suggests.
Design exception paths first
Happy-path automation is easy to overvalue. Production quality is often determined by the treatment of incomplete records, conflicting policies and unavailable integrations.
An exception should arrive with the evidence already gathered, the attempted action, the reason it stopped and a clear owner. Sending “AI failed” to a shared inbox is not an operating model.
Preserve accountability
Make the transition between machine suggestion and human decision explicit. Record what the person saw, what they changed and which action followed. This is important for audit, but also for learning: corrections are valuable signals about source quality, product design and model behaviour.
The unit of value is completed work with an acceptable outcome, not a model response. Teams that design the whole journey often discover that the useful AI component is smaller than expected. That is a better result than an impressive answer nobody can operationalise.