Every week, somewhere, a scaling company launches an AI pilot for their revenue team.
They select a tool, brief the team, set a timeline, and wait.
Three months later, usage has dropped to two enthusiasts and a manager who feels guilty about the procurement decision. The pilot “showed promise” but didn’t scale. Nobody declares failure because nobody agreed on what success looked like. The initiative quietly disappears.
This is not bad luck. It is bad design. And it is almost entirely avoidable.
Failure mode one: starting with the tool
A vendor demos something impressive. Leadership gets excited. The tool gets procured. Then someone asks: what exactly are we using this for?
The answer is usually “to make the team more productive” — which is not a use case. It’s a hope. Without a specific workflow to change and a specific output to improve, adoption is random. Curious people use it. Everyone else doesn’t. Nothing systematic changes. The vendor calls it a successful implementation.
Failure mode two: making adoption optional
The new tool gets introduced alongside existing workflows rather than replacing specific steps within them. Reps are encouraged to try it when they have time.
They don’t have time. They have calls, proposals, CRM updates, and pipeline reviews. The AI tool is an interesting addition to an already overcrowded workday. It becomes the thing they’ll get back to when things slow down. Things don’t slow down.
Real workflow redesign means deciding in advance which steps AI handles and rebuilding the process around that assumption. Not additive. Structural. The old step is removed. The new step replaces it. This requires management courage that most pilot designs carefully avoid.
Failure mode three: no baseline
If you don’t measure the thing you’re trying to improve before you change anything, you cannot know whether the change worked.
This is so obvious it barely needs saying. It gets skipped constantly. The result is that the pilot produces anecdotes instead of data. Some people think it helped. Some people aren’t sure. The initiative can’t be defended or scaled because there’s nothing to point to. The next pilot starts from the same place.
What a successful pilot looks like
It starts with a specific, measurable problem. Not “improve productivity” — that’s a category, not a problem. “Reduce proposal turnaround time from four days to one day.” “Eliminate manual CRM entry after customer calls.” “Cut pre-call research time from ninety minutes to fifteen.”
It defines the metric before anything changes. It establishes the baseline. It redesigns the specific workflow — not the whole system, just the broken part. Then it runs the new workflow, measures the same metric, and compares.
If the metric moved, you have evidence. Scale it. If it didn’t, you have information. Fix it.
The pilot that works is narrow, fast, and relentlessly focused on one measurable change. Most companies try to prove too much at once, learn nothing useful, and conclude that AI doesn’t work in their context.
It works. The pilot design is what fails.
Still here? Good. You might be exactly my kind of client.
