It’s easy to get excited about AI.
You see the demos. You read the headlines. You attend a strategy meeting and someone says, “We could use AI to optimize this.” Heads nod. Diagrams get drawn. A pilot project begins.
Then, three months later, it’s either quietly shelved… or barely usable.
Most companies aren’t short on ideas. They’re short on systems that can absorb those ideas.
Here’s what I’ve seen go wrong, over and over:
No usable data
The #1 blocker. Not just “we have no data” — but we have data that’s spread across five systems, cleaned inconsistently, or tracked only in someone’s inbox. No model in the world can fix bad inputs.
Solving the wrong problem
There’s often pressure to “use AI” without a clear use case. So teams try to optimize something nobody cares about, or automate something that wasn’t that expensive in the first place. The result? A technically interesting solution with no internal demand.
No one to own the thing once it ships
AI systems require maintenance — retraining, monitoring, feedback loops. But after the project is “done,” it’s often unclear who supports it. Is it IT? Ops? A single data scientist? Without long-term ownership, things quietly decay.
Lack of explainability
Especially in manufacturing and logistics, if people can’t understand why the system made a decision, they won’t trust it. That’s not resistance — it’s reasonable. When outcomes affect safety, cost, or uptime, “black box” explanations don’t fly.
So what actually works?
Start with a boring problem
Inventory prediction. Anomaly detection. Document classification. Something operational and clear, with a human fallback. Not visionary — but useful.
Work with the process, not against it
AI should slot into existing workflows. If you need to rebuild everything around it, it’ll break. The most successful projects I’ve seen are the ones that feel like small upgrades, not full revolutions.
Use heuristics and automation first
Sometimes a rule-based system or basic statistics get you 80% of the way there. That’s fine. AI can come later — and when it does, it has a clean interface to improve upon.
Assign real ownership early
Before you start the project, decide who will keep it running, retrained, and monitored. It’s not “done” when the model is deployed — it’s done when someone’s responsible for keeping it valuable.
Most AI projects fail quietly, not dramatically. They don’t crash — they just fade into the background, unused and unsupported.
But the ones that succeed? They look small at first.
They work within the system, not around it.
And they make something a little easier — not everything smarter.
That’s what actually works.