Find your starting point
Show us one workflow that wastes time.
We’ll help you find the sensible next step—automation, training, an existing feature, or no project yet.
Request a Free ScanWhy 30 Days
Most AI projects drift. They start with excitement, get stuck in "learning mode," and fade away before anything ships.
Here's the thing — a time-bound sprint forces clarity. When you have 30 days, you have to pick one thing, make it work, and move on.
In practice, this approach serves two purposes: it builds momentum and it proves value fast. Both matter.
The 30-Day Blueprint
Week 1: Pick and scope.
Choose one workflow. Not your whole operation. One process that takes noticeable time and has clear inputs and outputs.
What to ask: Where do we spend the most repetitive hours? Where does the work feel mechanical?
Week 2: Build the proof of concept.
This isn't about building the perfect solution. It's about building something that works — even if it's rough around the edges.
In practice, the goal is answers, not perfection. Does this save time? Does it improve quality? Can we measure it?
Week 3: Test and refine.
Run it alongside your current process. Compare results. Adjust inputs, tweak outputs, learn what works.
What to ask: Are people actually using it? Is it making their work easier, or just different?
Week 4: Hand off or pivot.
If it works, document what you learned and plan the next step. If it doesn't, that's fine too — you spent a month learning instead of years wondering.
What Good Looks Like
What we've found is that successful first AI projects share a few traits:
- Narrow scope. One workflow, one goal.
- Clear success criteria. You know what "working" looks like.
- Real usage. Your team actually uses it by week 3.
- Measured results. Time saved, errors reduced, output improved — pick one and track it.
Watch Out For
Trying to do too much. Your first AI win shouldn't transform your business. It should prove a concept.
Waiting for perfect data. You don't need a clean data warehouse. You need enough examples to show the system what good looks like.
Skipping change management. Technology is the easy part. Getting your team to use it is the real project.
What to ask before you start: What's the minimum viable version of this? What happens if this pilot fails?
The hardest part is choosing one thing. Everything else follows.
Start small. Prove value. Scale what works.
Ready to start small? Let's find your first AI win together.