After three decades in AI, I've watched hundreds of projects launch with fanfare and quietly die in a spreadsheet somewhere. The failure rate for enterprise AI initiatives hovers around 80%, and it's not because the technology doesn't work. It's because teams make the same avoidable mistakes over and over.
Here are the patterns I see — and what to do instead.
The most common mistake is falling in love with a solution before understanding the problem. Teams hear about GPT, or diffusion models, or retrieval-augmented generation, and immediately start looking for places to apply them.
This is backwards.
What to do instead: Start with a pain point that has measurable cost. A process that takes too long, a decision that's made inconsistently, a bottleneck that limits scale. Then ask whether AI is the right tool.
Everyone wants to talk about models. Nobody wants to talk about data pipelines. But here's the reality:
| Activity | % of project time |
|---|---|
| Data collection & cleaning | 40–60% |
| Feature engineering | 15–20% |
| Model development | 10–15% |
| Deployment & monitoring | 15–20% |
That's right — the "AI part" is often the smallest slice. Projects that budget all their time for model development and treat data as an afterthought are setting themselves up to fail.
What to do instead: Before writing a single line of model code, answer these questions:
If you can't answer all five, you're not ready to build.
I've seen teams spend six months building a sophisticated deep learning system that performs 2% better than a rules-based approach they could have shipped in a week.
A simple baseline does three things:
# This is not glamorous. It is useful.
from sklearn.linear_model import LogisticRegression
baseline = LogisticRegression()
baseline.fit(X_train, y_train)
print(f"Baseline accuracy: {baseline.score(X_test, y_test):.3f}")
# Now you have a number to beat.What to do instead: Always ship the simplest thing that works first. Complexity is a cost, not a feature.
A model that works in a Jupyter notebook is not a product. The gap between "it works on my laptop" and "it works reliably at scale" is where most projects go to die.
Production AI means thinking about:
What to do instead: Involve your infrastructure team from day one, not after the demo gets approved.
AI doesn't replace workflows — it changes them. And if you don't design for the humans who will actually use the system, they'll find ways to work around it.
The best AI systems I've seen share a common trait: they make the person using them feel more capable, not less relevant. They augment judgment rather than replacing it.
What to do instead: Sit with the end users. Watch them work. Understand what decisions they make and what information they need. Build the AI to serve that, not to impress a stakeholder in a conference room.
Every one of these failures has the same root cause: treating AI as a technology project instead of a business project. The model is a component. The value comes from everything around it — the problem definition, the data strategy, the integration, the change management.
Get those right, and the AI part is the easy part.
— Teckxx, Founder of OK ROBOT
Sign up here to receive updates on new Blog posts and all things OK-ROBOT