Practical AI without the rebuild: where it earns its keep first.

Every quarter we get the same call: leadership read an AI deck, the team built a proof of concept, and now nobody can explain what it would replace. The PoC works in a demo and dies in production.

The pattern that actually ships is smaller. Find a step in an existing workflow where a human is doing low-judgment translation — reformatting a document, classifying a ticket, summarizing a thread, extracting fields from a PDF — and put a model behind that single step. Leave the surrounding system alone.

Look for the manual seams

Workflows have seams: places where a human picks up output from one system and types it into another. Those seams are where AI compounds, because you don't need the model to be perfect — you need it to be better than rekeying.

Good first targets: inbound email triage, vendor invoice coding, support ticket categorization, meeting-note action extraction, RFP intake. Bad first targets: anything that ends in a customer-facing decision the moment the model speaks.

Keep the human in the loop on day one

The cheapest way to deploy a model is to have it draft and a human approve. You get the speedup immediately, you build a labeled corrections dataset for free, and you discover the failure modes before they become incidents.

Once approval rates stabilize above ~95% for a class of input, you can move that class to auto-approve and keep humans on the long tail. That's a real production rollout. Skipping the approval step is how you end up with a confidently-wrong model in front of a customer.

What to measure

Time-to-completion on the wrapped step, before and after. Override rate (how often the human edits the model's output). Cost per task including model calls. If you can't move all three in the right direction within a month, the seam was wrong — pick a different one.