AI is just like math

AI is often mystified as if it were some kind of miracle, soon to become conscious, creative, and autonomous.

But the truth is simpler. And better.

Because AI isn’t magic. It’s math.

And those who understand it can use it pragmatically, without getting overwhelmed.

What we’re really talking about when we talk about AI

Prof. Dr. Ralf Otte puts it clearly: “Today’s AI is a smart application of mathematics.”

Nothing more, nothing less.

Here’s what it can do:

• Analyze data
• Detect patterns
• Make forecasts
• Perform complex optimizations

And what it can’t do (yet)?

Philosophize. Feel. Decide intuitively. And that’s perfectly fine.

Because in industrial settings, what matters most are: Reliable results based on solid data.

A reality check: What autonomous driving teaches us about AI

Autonomous driving shows us where AI reaches its limits.

While a human can learn to drive safely within a few lessons, AI needs millions of miles of data.

Why?

Because it doesn’t understand, it calculates.

The good news for mechanical engineering:

We don’t need “thinking” AI. We need calculating AI. And that’s exactly what it excels at.

What does this have to do with mechanical engineering? Everything.

Mechanical engineers think in solutions. In efficiency. In availability.

That’s where AI truly shines:

• Less scrap
• More accurate forecasting
• Automation of service processes
• Smarter planning

What it takes: clean data. Clear processes. And the courage to get started.

Real-life example: Predictive Planning with PartsOS Planning

At PartsCloud, we don’t just talk about AI, we use it.

Our software solution, PartsOS Planning, applies machine learning to forecast spare parts demand with high accuracy, even without machine sensors.

Here’s how we do it:

👉 We analyze historical consumption patterns
👉 We detect anomalies in demand fluctuations
👉 We cluster by machine type, region, weather, or usage
👉 We optimize inventory automatically with a single click

The result? No magic, just solid math and damn good data.

5 AI myths you can let go of today

“AI replaces people” – False. It complements people. If you understand the process, you remain irreplaceable.
“AI is always right” – False. But it’s consistent — and it learns.
“AI needs big data” – Not necessarily. Smart small data often works just fine.
“AI works by itself” – False. Good data = good results.
“AI is expensive” – Only if it delivers no value. Small projects = fast ROI.

How to get started with AI:

Short term:

✔️ Choose a process where you already collect a lot of data
✔️ Define a clear KPI (e.g. downtime costs, forecast accuracy, scrap rate)
✔️ Run a small, focused AI project

Long term:

✔️ Standardize your data sources and assign ownership
✔️ Build internal expertise and accountability
✔️ Form a small task force to drive data-driven projects
✔️ Invest in partners who bring hands-on experience — not just slides

The conclusion: AI isn’t the solution, but it’s a damn powerful tool.

Don’t wait for the perfect moment or the next training.

If you know your processes, you’re ready.

AI isn’t magic. It’s mathematics.

And those who use it wisely will win, one step at a time.