How to launch Industrial AI Use Cases That Actually Scale

There’s a common trap in smart manufacturing: the “use case” that doesn’t scale.

Many organizations kick off with a hyper-specific use case, say, preventing a particular failure in one machine.

Sounds logical, right?

But here’s the problem: that failure mode might not occur for months, maybe even years.

And until it does, your smart solution? It just sits there, creating no value.

So leadership loses interest, the team gets stuck, and scaling becomes a pipe dream.

This is why so many pilot projects in manufacturing digitization never make it past the pilot stage.

What if you redefined what a use case should be?

Instead of “predict this rare failure,” think:
⇨ “Improve overall reliability”
⇨ “Optimize energy usage”
⇨ “Reduce emissions”
⇨ “Enhance process efficiency”

These broader themes are always relevant, and they deliver continuous value.

And when value is visible and ongoing, scaling becomes not only possible, but necessary.

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