Companies have gotten good at encouraging AI experimentation, and that part is working. Most marketers you know are past the “should we use AI” conversation. Everyone has a workflow, a few go-to skills, a tool they’ve built or borrowed.
You’ve probably seen some version of this on your own team: two people, working separately, build the same tool for the same problem. Neither one knows the other is doing it. Both finish. Neither is more efficient than if only one of them had built it, because nothing compounds when everyone’s still starting from zero and improving alone, one small tweak at a time. It’s five people cooking the same recipe in five separate kitchens, none of them tasting what the others made.
Getting real efficiency out of AI means treating it like any other process on your team. When everyone knows what everyone else is working on, and does their part well, less time gets spent solving problems that already have a solution sitting in someone else’s inbox. Handoffs get simpler too, because you’re not decoding a new format or process every time work moves between people.
Fixing it takes someone actually paying attention to what’s being built across the team, and being intentional about who’s working on what instead of letting it happen by accident.
Path One: Consolidation
If your team has already been experimenting, you likely have an army of AI tools built independently, some overlapping without anyone realizing it. The easiest starting point is coordinating what already exists. Have your team run a quick show-and-tell of what they’ve built.
Once you can see the full picture, look for the overlaps. Assign someone to pull the strongest features from each version into one tool that works for the whole team, instead of everyone maintaining their own. Take the five separate kitchens and combine the best parts of each recipe into the one the whole team cooks from.
The payoff here is depth. Multiple people already worked out multiple ways to solve the same problem. The final tool benefits from all of it, instead of any one person’s first attempt.
Path Two: Division
This path requires coordinating before anything new gets built. Catalog what your team needs, then assign each build to one person or a small group. Only that person spends the time developing the tool. Everyone else applies it to their own workflow and helps refine it as they go. One kitchen perfects the dish, and the rest of the team just uses it.
The payoff here is coverage. Instead of one problem solved multiple times, you get multiple problems solved at once, each one improving as the team uses it.
Getting Started
Depth and coverage aren’t a strict either/or. Blend them based on what the task needs, how urgent it is, how sensitive it is. Maybe a few people go deep on one thing while others divide up the rest. The exact mix matters less than making that choice on purpose instead of by accident.
Both paths ask for the same thing: talk about what you’re building before you build it.
AI is part of business now. What’s still up for grabs is whether anyone’s being deliberate about where and how it shows up in the actual work.
