AI Support Is A Workflow Design Problem
The hardest part of AI support is usually not the model. It is deciding what the system can do, when it should stop, and how it fits the real workflow.
I think a lot of AI support products are described from the wrong center of gravity.
They get described as model products.
In practice, most of the durable product questions sit somewhere else:
What information is available at the right moment.
What the system is allowed to do automatically.
When the answer should become an action.
When the workflow should stop and escalate.
How the output fits into the tools the team already uses.
That is workflow design more than model novelty.
The model absolutely matters. Quality matters. Latency matters. But if the surrounding workflow is weak, a better model mostly just lets the product fail in more polished language.
This is why I tend to look at AI support products through a more operational lens. I want to understand the loop: intake, retrieval, synthesis, recommendation, action, escalation, auditability. If the product only gets credit for the text generation step, the evaluation is too shallow.
The interesting companies in this space are not just producing better answers. They are designing better operating loops.
That is also where product judgment starts to matter more than prompt cleverness.