I keep reading that 67 percent of companies are stuck in AI pilot mode. They can’t transition to production.
That’s wild. Two-thirds of AI projects never leave the pilot phase.
But when I think about why, it makes sense.
AI pilots are fun. You get some data. You train a model. It does interesting things. Everyone is excited.
But then you realize that scaling AI is different from piloting AI.
A pilot might use a small dataset. Production needs to handle the data you actually generate.
A pilot might use GPU that costs $1,000 a month. Production needs to be cost-effective for real usage.
A pilot might be manually monitored by data scientists. Production needs automated monitoring. It needs alerting when the model drifts. It needs retraining pipelines.
A pilot might be evaluated on accuracy. Production needs to account for fairness. Explainability. Regulatory compliance.
A pilot can be built by a brilliant data scientist. Production needs to be built so a mid-level engineer can maintain it.
These are completely different problems.
So companies train a model in a pilot. It works. Then they realize that making it production-ready is 10x more work than the pilot.
And at that point, the organization starts asking: “Is this worth it?”
For some projects, the answer is yes. For most, the answer is… we’re not sure.
So they get stuck. In pilot hell.
They’ve spent money. They’ve proven the concept. But they can’t justify the engineering effort to scale it.
I think the next wave of AI companies that will win are the ones that make scaling easier. Not the ones that train better models.
Tools like MLOps platforms. Data validation tools. Model monitoring. Retraining pipelines.
These won’t make headlines. But they’ll be worth billions.
Because companies don’t need better models. They need to get their existing models into production.