The most common problem companies run into with AI isn't technical, it's scope. Projects that start with "let's integrate AI into our company" are still sitting in a meeting six months later, arguing about what to actually do. The approach that works is the opposite: start with one narrow, measurable scenario.
A measurable scenario is one that can answer three questions clearly: which process, in how much time, and by how much will it improve? "Routing approval requests to the right person" is measurable — you can measure today's average routing time and show exactly how much the system reduces it. "Let's make our company smarter" can't be measured, and so it's never actually finished.
There's a second benefit to this approach: it builds trust. Most teams are cautious about AI — reasonably so, given how much disappointment "AI handles everything" marketing has produced. A narrow pilot earns that trust by showing concretely what the system does and where it still needs a human decision.
In practice, this means following a sequence: first identify the single point in your current process that wastes the most time. Then define upfront what "success" looks like for that point — say, cutting average processing time in half. Run a four-to-six-week pilot and measure the result with real numbers. If it's positive, roll it out; if not, learn what didn't work and move to the next scenario at low cost.
This is the same sequence we follow in Axios's consulting process: a free initial call to clarify the need, a one-week fixed-price engagement to produce a prioritised roadmap, then a four-week fixed-price pilot to test one scenario against real data. You start with a measurable result, without a large upfront commitment.