The tools are capable. The requirements never got clear enough.

The gap between what a marketing team wants to do and what the systems can support is rarely a technology problem. It's a translation problem. What the business needs hasn't been communicated clearly enough to the people configuring the tools. What the tools can actually do hasn't been communicated clearly enough back. Everyone is working in good faith. The work still isn't connecting.

Jo sees this most concretely in AI adoption. Marketing teams are being handed tools with real capability and not enough context for how to fit them into existing workflows. The failure mode isn't resistance — it's imprecision. The use case is vague. The expected output isn't defined. When the tool doesn't perform the way the team hoped, the conclusion is that the tool doesn't work. Often the tool is fine. The requirement was never specified.

The same pattern shows up in attribution, in campaign infrastructure, in the gap between what analytics platforms report and what the business actually needs to know. The underlying question is almost always the same: what are we actually trying to measure, and have we said that clearly enough for the system to answer it?

That's the work.