Most AI initiatives stall because they start with a tool instead of a workflow. Here is the order of operations that actually gets AI into production — and why the Context Layer comes first.
The most common way an AI initiative fails is not a bad model. It is starting in the wrong place. A team picks a tool, runs a pilot, demos something impressive in a sandbox, and then watches it die on contact with real work. The problem was never the technology. It was the order of operations.
Here is the sequence we use to get AI into production and keep it there.
1. Start with the operating pain, not the technology
Before anyone evaluates a tool, name the actual problem. Is it a decision that takes too long? A handoff that drops context? A search that never surfaces the right document? A review step that is a bottleneck? Define the workflow problem in plain language first, then choose the AI surface that fits it. Picking the tool first is how you end up with a solution looking for a problem.
2. Encode the knowledge the work depends on
This is the step almost everyone skips, and it is the one that determines whether anything else works. The judgment that runs your business lives in people’s heads, scattered SOPs, and old email threads. An AI agent with no access to that context will be confidently wrong. So before you automate, you encode: turn that tribal knowledge into one authoritative, citable source the system is allowed to trust. We call this the Context Layer, and it is the foundation everything else stands on.
3. Ship one workflow end to end
Resist the urge to transform everything at once. Pick a single, real workflow and take it all the way to production — with a human on the approval gate where accountability matters, and evals in place to catch failures. One workflow live and trusted beats five half-finished pilots. It also teaches you more about your own operation than any amount of planning.
4. Prove it with evals, then expand
Evals are the tests that catch when the AI is confidently wrong. They are what let you trust an output enough to put it in front of a customer. Once one workflow is shipped, measured, and trusted, you add the next module on the same foundation. The vision can be fully AI-native; the delivery is one function at a time.
The short version
Where should you start with AI? Not with a tool, and not with the most exciting use case. Start with the workflow that hurts, encode the knowledge it depends on, ship it end to end with humans accountable, and prove it with evals before you expand. That order is the difference between a pilot that dies and a system that compounds.
If you want help finding the right first workflow, that is exactly what a Strategy Sprint is for.