The First Win Criteria
Your first AI project should have three characteristics:
1. It must be high-frequency and repetitive. It happens weekly, daily, or constantly. Not monthly. Not quarterly. Regular enough that the savings add up fast.
2. It must have clear success metrics. You can point at it and say "It works" or "It doesn't." Not vague. Not subjective. "It saves 8 hours a week" or "It catches 95% of errors."
3. It must have a low learning curve for your team. Ideally, your team already knows how to do the work well. We're just automating the work, not changing how you work.
If your project checks all three boxes, you're golden. If it only checks two, it can still work but it'll take longer. If it only checks one, pick a different starting point.
What to Avoid
Don't pick something innovative. Don't pick something cutting-edge. Don't pick the problem that's been on your roadmap for two years.
Pick the thing that's boring and repetitive. Pick the thing that someone on your team complains about every week. Pick the thing that doesn't require building new processes — it just requires replacing existing human labor with an agent.
I see companies try to "go big" on their first project. They want to build something that solves five problems at once. That's a recipe for a failed project. Scope creep kills projects faster than anything else.
Real Examples
We could automate Dig Solutions' entire proposal process. But that's complex and risky for a first project. Instead, we started with client reporting — it was clear, repetitive, high-frequency, and had obvious metrics. That worked. Now we're comfortable with more complex stuff.
A manufacturing company could automate demand planning, inventory forecasting, and supplier communication all at once. But they started with just the demand planner pulling data from three systems. One thing. Clear win. Now they're ready for the next level.
The Psychology of the First Win
Your team is skeptical. They've heard "AI will change everything" before. The best way to move skeptics is not with promises. It's with proof.
When they see an agent successfully doing work they used to do, the conversation changes. It shifts from "Is AI real?" to "What else should we automate?"
That's why picking the right first project matters so much. It's not just about ROI on that one project. It's about building momentum and getting buy-in for what comes next.
How to Know You've Found It
Talk to your team. Ask: "What's something you do every week that you'd automate if you could?" The first thing someone says is usually the right starting point. It's top-of-mind because it bothers them.
That's your golden ticket. That's the project that wins. Go with it.