Start from a business metric, not a model
The AI programs that survive their first budget review share one trait: they were scoped against a number someone already cares about — cycle time, cost per transaction, revenue per account. Programs scoped against "AI capability" have no defender when priorities tighten. Before any model is selected, write down the metric the program moves and who owns it.
Fix the data path before scaling the ambition
Most mid-market AI failures are data failures wearing an AI costume. If the inputs a model needs live in four systems with three definitions of "customer," the model inherits that confusion. A modest investment in data contracts, lineage, and a governed pipeline pays for itself before the first model ships.
Buy the commodity, build the differentiator
Foundation models, transcription, document extraction — these are commodities and should be bought. The differentiator is the layer that encodes how your business decides things: the prompts, the retrieval over your own data, the guardrails, the workflow integration. Spend your engineering budget there.
Put a human checkpoint where errors are expensive
The question is not whether the model will be wrong; it is where being wrong is affordable. Map your workflow, mark the steps where an error costs real money or trust, and design review into those steps. Full automation is a milestone you earn with evidence, not a starting assumption.
Treat evaluation as a product feature
A model without an evaluation harness is a liability with good marketing. Define what "good" means before launch, measure it continuously in production, and budget for the drift you will find. Teams that do this ship faster over time because they can change models and prompts with confidence.