The most expensive mistake in an AI assistant project isn't a technical one. It's starting to build before you understand the problem.
We've seen it happen repeatedly: a team gets excited about AI, picks a use case that sounds right, and starts building. Three months later, the assistant is technically functional but the team doesn't use it — because nobody stopped to ask whether this was actually the right problem to solve.
Discovery changes that. Here's how we run it.
Start with the workflow, not the technology
The first question we ask is never "what do you want the assistant to do?" It's "walk me through what your team does, step by step, on a typical day."
We're listening for three things: the repetitive tasks (things done the same way every time), the knowledge-heavy tasks (things that require looking something up), and the bottlenecks (places where things slow down or get queued).
Those three categories are where AI assistants deliver the most value. Everything else is noise.
Map the inputs and outputs
Once we've identified the right workflow, we map every input the assistant will need to work with (emails, tickets, documents, structured data) and every output it needs to produce (responses, summaries, prioritized lists, flags).
This is where most briefs break down. Clients often describe what they want the assistant to do without specifying what it will have access to. An assistant that's supposed to draft context-aware replies but doesn't have access to conversation history will produce generic responses — and that's a design failure, not a technology failure.
Define the edge cases early
Every workflow has exceptions. We ask: what happens when a customer asks something outside the normal scope? What happens when the data is incomplete? What happens when the assistant doesn't know the answer?
Designing for edge cases early prevents two common failure modes: an assistant that confidently produces wrong answers, and one that crashes when something unexpected happens.
Set the success criteria before you build
We ask clients to define what success looks like before we write a single line of code — not in vague terms but in specific, measurable ones: average response time down by X%, the team handling Y% more tickets per day.
At the end of discovery, we produce a blueprint that specifies exactly what the assistant will do, what it will have access to, how it will handle edge cases, and how success will be measured. The client signs off before we build anything. That sign-off is the most valuable step in the entire process.