Start with the expensive workflow
The best AI integration isn’t the flashiest — it’s the one attached to your biggest recurring cost. Support triage, lead qualification, onboarding, content production. Find the workflow you pay for every single day, and put the model there.
The test is whether you can name the cost in hours or headcount before you build anything. If nobody can say what the current process consumes, you are not solving a problem yet — you are shopping for one.
Design the workflow, then add the model
A model is a component, not a product. We map the end-to-end flow first — inputs, guardrails, hand-offs to humans — then drop the model into the step where judgment is the bottleneck. The result feels less like a chatbot and more like the software just got smarter.
This ordering also protects you from the most expensive mistake in the category: automating a broken process faster. If the workflow is wrong, a model makes it wrong at scale and with more confidence.
Good AI integration is invisible. It just feels like everything got faster.
Instrument from day one
Every AI feature needs a feedback loop: what did it answer, was it right, what did the human do next. We ship with evaluation baked in so the system improves instead of quietly drifting.
The human’s next action is the most valuable signal you can log, and the one most teams forget to capture. Edits, overrides, and escalations are labelled training data generated for free by the work itself.
Know which failures you can afford
Put models where a wrong answer is recoverable and cheap: drafting, triage, summarising, suggesting. Keep them out of steps where a wrong answer is irreversible or unnoticeable until much later. That single distinction determines most of whether an AI feature is a success or an incident.
Where you do want AI in a consequential step, the answer is usually to have it prepare the decision rather than make it — the same way a good analyst hands you a recommendation rather than moving the money.