In our earlier articles, we laid the groundwork for Industrial AI — breaking down the difference between classic AI, generative AI, and agentic AI. But frameworks alone don’t tell the full story. How do these ideas play out when you’re inside a real industrial company, tasked with building teams, getting budget, and making data actually deliver value?

For that perspective, we sat down with Nathalie Rigouts, who until recently headed data and analytics at Borealis and is now Head of Business Applications Data and AI at Umicore. Nathalie brings a refreshing, pragmatic voice — someone who moved from finance into IT, and who knows first-hand the reality of building data capabilities in industry.

From Finance to Data & AI

Nathalie didn’t start in IT. Her background is in finance, where every month she wrestled with massive spreadsheets just to get accurate actuals. That pain, she recalls, was the start of her data journey:

“Every month again, I was struggling with getting the correct actuals. And then of course, you have to make your forecast.”

From implementing a financial planning tool, to establishing BI at Borealis, to eventually leading data and analytics, her path shows how close the link is between business need and IT capability. And she’s clear about the lesson: it’s not about technology for its own sake.

“It’s not about implementing Microsoft Copilot. You’re not going to gain any sustainable advantage there. But if you can have a deep understanding of the processes in your company, and where data-driven solutions can help, that’s when you start to create value.”

Start Small, Sell the Success

One of the recurring themes in Nathalie’s story is pragmatism. At Borealis, the team started in 2016 with literally one data scientist and a laptop. “Python notebooks on a laptop, and we started.”

The key, she says, is to find enthusiastic allies and solve problems that matter. And once you do, don’t stay modest: market the success internally.

“We often forget to sell our success. I would go everywhere and talk about small things we did. And that’s how you gain support for the next steps.”

From that first laptop, the team grew, but only because each step came with visible, tangible wins that created pull from the business.

Use Cases That Matter

So what are typical use cases in manufacturing? Nathalie sees three common ones:

* Predictive maintenance: “If equipment fails often, anomaly detection and predictive maintenance are obvious first steps. But it’s not an easy nut to crack. Often, you don’t have enough failures to feed a model.”

* Quality control with computer vision: mainstream, but effective. With enough annotated pictures, good vs bad quality can be classified quickly. The catch? Data Quality.

* Logistics optimization: untangling shipping routes and optimizing delivery to customers with AI-based optimization models.

These are concrete, valuable problems — and they also highlight the role of data governance. As she recalls with a smile:

“We had beautifully annotated data — but all in Finnish. That’s when you realize governance is not optional.”

GenAI: Efficiency or Attractiveness?

When it comes to Generative AI, Nathalie is cautious. The business case is not always straightforward:

“I tried to make the case for Microsoft Copilot. At €30 per user, that’s not small. Does it reduce workforce? No. At best, people spend more time on value-added activities. But what does that bring to the bottom line? Hard to say.”

Yet she also sees why companies can’t ignore it.

“Companies have to invest in it because it will determine their attractiveness as an employer. New graduates take these tools for granted. If you don’t offer them, you won’t attract talent.”

She distinguishes between two levels: workplace efficiency (nice, but hard to quantify) and domain-specific models trained on your own IP. The latter, she believes, is where the real value lies. For example in pharma, where LLMs trained on internal knowledge can speed up R&D. “That’s when AI becomes a true digital co-worker.”

Governance, Change, and Legislation

On governance, Nathalie doesn’t mince words:

“It’s always the people, the processes, and the tools. The main component around which all of them center is the value case.”

Her advice: don’t let your solutions depend on a single enthusiast, and don’t leave an escape hatch back to the old way of working. Change management is part of the job.

And on legislation, she takes a positive view:

“It’s an opportunity. It forces us to think about awareness, ethics, governance, documentation, monitoring. All things that make sense. Yes, it’s work, but it helps you get budget and build maturity.”

Closing Thoughts

What we loved about Nathalie’s perspective is how grounded it is. No buzzwords, no silver bullets: just the reality of building teams, solving problems, and learning along the way. Whether it’s predictive maintenance, quality monitoring, or navigating the GenAI hype.

Her closing reminder:

“Keep it simple, be pragmatic. We built beautiful solutions with just scripting business rules. The business was happy, and nobody needed a fancy machine learning model.”

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