The 3-Day AI Strategy Sprint That Actually Works
Everyone's overthinking AI strategy.
I get it. The landscape is chaotic. The tech moves fast. And it feels like if you don't have a perfect plan, you're already behind.
But here's the thing - You don't need months of planning. You need 3 focused days.
Let me show you our exact process. It's the same one we use with companies to get their AI strategy from "overwhelmed" to "ready to execute" in 72 hours.
Day 1: Identify What Actually Keeps You Up at Night
Here's what most companies get wrong: they start with AI solutions instead of real problems.
Big mistake.
Day 1 is all about getting your entire leadership team in a room. And I mean everyone - from Product to People ops. Why? Because AI can impact every corner of your business, and you need all perspectives to get this right.
But here's the twist: we don't talk about AI at all for the first few hours.
Instead, we ask one simple question: "What keeps you up at night?"
We're hunting for the real problems. The ones that could sink your company if you don't solve them. The ones that make you stare at the ceiling at 3am.
This is where the magic happens. Because once you have real problems on the table, AI becomes a tool to solve them - not a solution looking for a problem.
Quick tip: Use sticky notes. Let everyone brain dump their challenges. You'll be surprised what surfaces when people get honest.
Day 2: Match Challenges to Realistic AI Experiments
Day 2 is where we get practical.
Look, you probably can't tackle every problem with AI right now. You've got constraints - time, people, money, technical skills.
That's actually good news.
These constraints help you focus on what matters. Here's how we do it:
First, we take those problems from Day 1 and run them through a simple filter:
Can AI meaningfully help solve this?
Do we have the resources to tackle it?
Can we test it in 90 days or less?
If the answer is "no" to any of these, it goes on the backburner. No guilt, no FOMO.
What you're left with is gold: 2-3 high-impact challenges that AI could actually help solve.
Now comes the fun part: designing experiments.
We're not looking for perfect solutions. We want quick tests that could validate if AI can help. Think:
Can AI help support handle 20% more tickets?
Could AI write first drafts of sales emails?
What if AI could screen resumes faster?
90-day experiments. That's your sweet spot.
Day 3: Create Ownership and Accountability to Make it Happen
Here's why most AI initiatives die: nobody owns them.
Day 3 is all about preventing that slow death.
Every experiment needs:
A clear owner (one person, not a team)
Success metrics that a 5th grader could understand
Kill conditions (when to pull the plug)
The owner isn't necessarily the most technical person. They're the one who feels the pain of the problem most acutely. They're the one who'll move mountains to solve it.
We spend Day 3 making sure every experiment has:
Weekly check-in rhythms
Resource commitments
Clear "this is working" metrics
A 30/60/90 day roadmap
By the end of Day 3, you'll have:
2-3 clear experiments to run
Owners chomping at the bit to start
A simple way to know if it's working
Here's the wild part: this actually works better than months of planning.
Why? Because you're starting with real problems, designing practical experiments, and creating true ownership.
Is it perfect? Nope. Will some experiments fail? Probably. But you'll learn fast and adjust faster.
And that's worth more than a 100-page AI strategy that sits in a drawer.
Want to try this with your team? Start with Day 1. Get everyone in a room. Ask what keeps them up at night.
You might be surprised what you discover.
