AI Strategy Mistakes to Avoid in 2026

And yeah, speaking of that waste—let's get real about the time and resources getting burned in these AI setups, because from my chats with dozens of folks over the last few months, it's the same patterns popping up across the board, from fresh startups to bigger outfits. I've talked to people just starting their own ventures, and what stands out is how some overthink everything—adding complex tech stacks or buzzwords like "agents" that sound cool but that folks don't even grasp the limits of. They end up saturated, chasing vague ideas without creating real value. But the ones who keep it simple? They start with basics, manage what they can handle, get quick feedback, and actually build momentum. That's the energy that drives things forward—nothing fancy, just steady value creation. Have you seen that split in your own circles?

Then there are the small businesses I've spoken with, the ones already running and dipping into AI for automation or digital stuff like marketing campaigns to hit new markets. What's fascinating here is how careful they have to be—every euro or dollar counts, so they can't just throw money at experiments. They need to pick spots where AI truly boosts efficiency without risking the core operation. I find that super interesting because it's all about balancing innovation with limited resources; get it right, and it scales their reach without breaking the bank.

But climb up to companies with hundreds of people? It's chaos—too many voices, decisions drag or go totally sideways, and often the fix is just buying a startup or some off-the-shelf product that doesn't quite fit. Helping those huge ones feels near impossible sometimes; the inertia is real. That's why I'm drawn to the small-to-medium enterprises— that's where the action is, and where I see the most mistakes that could be fixed. Like spinning up AI labs: some hire a single AI engineer who's totally disconnected from the rest of the team, tinkering in isolation. I can spot from a mile away that it'll flop—no integration, no buy-in.

Others bring in a couple of folks, set up a small team, and try copying existing tools, thinking they can own a slice of the "pipe" where data flows through their business. Sure, if your data's unique, you might squeeze some value, but it's rarely the significant edge they're hoping for—more like reinventing wheels that already exist. And then there are the companies frozen at the start line, no clue where to begin. Those are the ones that really need guidance on how tech can make them more efficient and create real value without overkill.

After 25 years in IT and 20 in AI, these patterns bother me because they're avoidable dead ends eating up time and planet resources. We need to start small with preaching: a quick workshop with 10-15 people to identify one problem, prototype with existing tools, test fast, and iterate. No ego, no FOMO—just solve what's in front of you. 2025 feels like the ramp-up, but 2026? That's when it'll get wild as more folks figure this out. If you've hit these snags in your own setup, what's one tweak that turned it around? Let's swap notes and cut the BS.

Chasing KPIs, not shiny tools? Get a readiness audit or build sprint—then scale.
Schedule: https://calendar.app.google/LKgjAbA2nSv9qV5UA

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