We all want the benefits of AI—faster decisions, deeper insights, automated efficiency. But if there’s one pattern I’ve seen repeated across industries, it’s this: the tech usually works. It’s the organization that doesn’t.
In nearly every AI initiative I’ve been called into midstream, the problem wasn’t lack of ambition or capability. It was a lack of organizational learning or upskilling. And that’s the real reason AI adoption stalls.
The tech usually works. It’s the organization that doesn’t.
Sam Palazzolo
So here are four of the biggest reasons I see AI adoption failing—and how the most strategic leaders counteract them.
1. Start Where the Energy Already Exists
Most execs assume AI adoption starts with a top-down strategy deck. It doesn’t. The real spark usually comes from someone on the front lines—marketing ops building smarter lead scoring, finance reducing reporting time, or a product team testing an ML model for recommendations.
The leaders who succeed don’t try to centralize too soon. Instead, they take a “gardener’s approach”: spot where things are already working, then scale those ideas by making the infrastructure reusable across teams. Think shared data access, faster experimentation cycles, and cross-functional visibility.
If you find a win, don’t isolate it—institutionalize it.
2. Incentivize Learning, Not Just Output
You want innovation? Don’t just reward ROI. Reward learning. Smart organizations create the right incentives around curiosity, iteration, and insight—not just outcomes.
That could mean recognizing internal champions, hosting innovation days, or promoting people who bring others along for the ride. When you celebrate the process—not just the success—you get more engagement from more people.
If you find a win, don’t isolate it—institutionalize it.
Sam Palazzolo
3. Run Experiments That Actually Teach You Something
Here’s a trap I see too often: building a shiny AI model, running a 6-month pilot, and then declaring “it didn’t work” without knowing why.
Real AI transformation doesn’t come from proof of concept. It comes from proof of learning. That means:
- Testing clear hypotheses (“Can we reduce response time by 40% without sacrificing accuracy?”)
- Running small-sample, short-cycle experiments
- Capturing why it worked—or didn’t
If you’re not learning something new in every sprint, you’re just burning time and budget.
4. Stop Celebrating Everything
When every experiment gets a trophy, the signal gets lost. Smart leaders know that too much recognition—especially for inconclusive or low-impact work—erodes focus, urgency, and standards. Recognition should be earned, not automatic.
This doesn’t mean punishing failure. It means being intentional about what gets amplified. Celebrate experiments that move the needle, generate transferable insight, or unlock repeatable processes—not just anything that checked a box.
Smart leaders don’t reward motion—they reward momentum. Recognition isn’t a participation trophy—it’s a spotlight for what’s repeatable, scalable, and strategic.
Sam Palazzolo
AI Adoption
AI doesn’t fail because the models aren’t good enough—it fails because organizations aren’t structured to learn fast enough. We walked through four breakdowns that stall adoption: ignoring grassroots innovation, incentivizing the wrong behaviors, mistaking activity for insight, and celebrating everything instead of what actually moves the needle. The common thread? Smart leaders build cultures that learn, adapt, and scale—faster than the tech evolves.
Sam Palazzolo
Real Strategies. Real Results.
PS – If you’re serious about scaling AI—and scaling your business—I share insights like this every week in my newsletter. Sign up at sampalazzolo.com and get a free copy of my 50 Scaling Strategies eBook (a $50 value) instantly.
