The temptation is to start with the model. A better question is: where does work already break down — repetitive handoffs, slow decisions, or data trapped in inboxes — and would intelligence reduce friction without creating new risk?
We only recommend AI where three conditions hold. First, the workflow is clear enough that you can define inputs, outputs, and escalation. Second, someone owns the outcome after the pilot ends. Third, success can be observed in the operating rhythm — time saved, fewer errors, better decisions — not in slide-deck novelty.
Opportunity mapping beats vendor shopping. Walk the process with the people who live it. Mark moments of copy-paste, judgment under uncertainty, and queues that stall because information is incomplete. Those are candidates. Decorative chatbots on the homepage rarely are.
Feasibility matters as much as ambition. Data quality, compliance, tool sprawl, and change capacity decide whether a pilot becomes production. Human-in-the-loop design is not optional theater; it is how teams trust the system and how exceptions get handled without silent failure.
Ship a scoped pilot, measure what you said you would measure, then harden ownership, interfaces, and review cadence. If intelligence does not improve decisions or reduce operational friction, we do not recommend keeping it. AI theater burns trust faster than it burns budget.
Used well, AI becomes part of the business system — embedded where it creates durable leverage. Used poorly, it becomes another silo. The difference is design discipline, not model size.