What makes a vertical AI harness actually useful?

A lot of AI products start from the same place: put a model into a domain, connect a few data sources, and let users ask questions.

We started from a similar direction last year. But as we learned more from building in this space, we saw a bigger opportunity: better answers are only the beginning.
The harder part is making AI useful inside real workflows.

In most vertical domains, the work is not one question and one answer. It is a process.

Healthcare, legal, finance, recruiting, sales, research, and engineering all have their own data sources, permissions, memory, tools, review steps, and user control needs.

That is where the product challenge gets interesting.

The winning vertical AI products will build beyond the model: data layers, tool layers, workflow layers, context, scheduling, permissions, and feedback loops that make agents reliable in real work.

For high-trust domains especially, the question is not only:

“Can the AI answer?”

It is:

“Can the system reliably support the process?”

Curious how others think about this.
What matters most for vertical AI products:
better models, better data, better workflows, better UX, or the system that connects them all?