
AI can only run a workflow you can describe. Four engagements, each a different buy. One defines a workflow end to end. One organizes your knowledge. One builds something new. One connects what you already run. Every one has a scope, a fee, and an end date.
Most organizations cannot automate a workflow because nobody can state what it is. Not the steps on the process document, the real one: the inputs, the decision points, the exceptions that take up half the time, and the handoffs where the work waits. Write that down properly and the automation becomes a build problem instead of an argument.
What you walk away with is that definition, in a form any team or any platform can build from. When a workflow needs what the rest of the company knows, it climbs to the top of the ladder, and nothing gets redone on the way up. The definition is yours either way.
In the workflows we have built so far, each one has given back dozens of executive hours a month, and the output got better, not just faster.
Organizations past the pilot stage that need a specific piece of work delivered rather than an open-ended advisory relationship.
Every engagement is scoped after a discovery call, then priced and dated before it starts. If the work turns out to be smaller than we thought, the scope shrinks and so does the fee.
The people who define your workflow built the platform that runs work like it at scale, so you get a straight answer about when a workflow needs the whole company’s context.

Everyone agrees a process is broken and nobody can describe it end to end.

The answer exists somewhere. Nobody can find it, and two teams have different versions.

You know what needs to exist, and no product on the market does it.

The systems are all in place. They do not talk to each other, so people move data by hand.
Most organizations start with a Workflow Analysis, because it is the cheapest way to find out which of the other three you actually need. If you already know what you want built, skip it and go straight to scoping.