A Dematic-hosted Innovation Roundtable explores why AI projects create more value when organizations start with the problem instead of the technology.

Many organizations want AI that supports their sustainability goals, but some still start in the wrong place. They begin with the technology, move quickly toward implementation, and only later ask what operational, environmental, or circularity problem they’re trying to solve.

That was one of the central messages from Geertrui Mieke De Ketelaere during a Dematic-hosted Innovation Roundtable in Antwerp focused on Innovation & AI for Sustainability & Circularity. 

“They rush it a little bit because there’s a lot of hype,” she says. “You really have to first think, what do you want to reach?”

That question matters because AI doesn’t fix weak objectives, poor data, or immature processes. It can help organizations find patterns and support better decisions, but only when the foundation is ready. If a company wants to train an AI system on its own operational data, that data must be accurate, complete, and useful.

De Ketelaere, an adjunct professor at Vlerick Business School and an expert in sustainable, ethical, and trustworthy AI, also cautions against treating AI as a shortcut around automation maturity. Companies that haven’t built a strong understanding of rule-based automation may struggle if they try to jump directly into AI.

Automation executes programmed rules the same way each time. AI can build on that foundation, but it doesn’t replace the need to understand processes, data, and teams.

Governance creates another pre-implementation testing point. Many companies introduce technology first, then address security, governance, and data access later. De Ketelaere says AI changes that order because data may leave the company, teams may gain access to information they shouldn’t see, and systems may influence decisions in ways the business doesn’t fully understand.

“With AI, you have to rethink the game and put governance and security up front,” she says.

That means asking harder questions before implementation. Who can access the data? Where does the data go? How will teams use the output? What decisions will the system support? Where does human oversight still belong?

For Dematic, this discussion reinforces a practical point: credible automation decisions start with understanding. AI can add value, but only when organizations define the objective, assess the foundation, and decide whether AI is the right tool for the job.

The Dematic Innovation Roundtable anchors a three‑part Q&A series that explores how organizations can apply AI more responsibly and effectively — without losing sight of risk, accountability, or long‑term value.

This series reflects Dematic’s role as a connector to outside expertise on issues shaping business, operations, and automation. It also reinforces a simple but powerful reality: The Mind Behind the Machine has to come before the machine itself.

Technology continues to evolve quickly. Understanding – grounded in experience, judgment, and accountability – is what turns that technology into lasting performance and meaningful progress.