Start with one workflow, not an AI strategy

Most AI initiatives stall somewhere between the vendor demo and the second steering committee. The strategy deck is polished. The pilot list is long. Nothing ships.

The teams that make real progress do the opposite: they pick one workflow, deliver it end-to-end, and let that first working system inform the next.

Why one workflow beats a strategy

A strategy without a working system is a hypothesis. A working system generates:

• Real usage data — you learn what people actually need, not what they said in interviews.
• Real operational constraints — data quality, permissions, and integration gaps surface fast.
• Real credibility — leadership stops debating whether AI can help and starts asking what to do next.

How to pick the first workflow

Look for a process that is (1) repetitive, (2) currently owned by a single team, and (3) has a clear "done" definition. Customer intake, invoice triage, and RFP first-drafts are common winners.

Avoid anything that requires reorganizing three departments before you can start. That's not a first workflow — that's a program.

What "done" looks like

Done means the workflow is running in production, the owning team knows how to maintain it, and there's a measurable delta from the previous state. Not "we ran a POC." Not "the model works in a notebook." Running, owned, measured.

Once you have that, the second and third workflows become an order of magnitude easier — because now you have a template, a governance pattern, and a team that knows how to ship.