Michael UngarCEO of the Industry 4.0 Club

AI Innovation: Build This, Avoid That for Success

13 Oct 2026 · 1 min

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1:29

In a rapid‑fire segment of an AI innovation panel, the moderator asks each panelist to name one capability companies should develop in the next year and one pitfall to avoid. The speakers provide concise, actionable advice: - Speaker B emphasizes building a culture of purposeful AI experimentation and warns against frantic, unreflective trials. - Speaker C stresses the need to qualify, fund, and be willing to terminate AI projects that don’t deliver business value, cautioning against treating AI as a shortcut for weak processes or poor data quality. - Speaker D highlights the importance of integrating legacy systems and data sources without overhauling IT, and reminds teams not to overlook the human element. The exchange showcases a pragmatic, people‑centric view of AI adoption, focusing on disciplined experimentation, project governance, technical integration, and human factors.

Let's wrap it up with a quickfire round. So in a short response, one capability companies must build in the next 12 months and one mistake to avoid. What would you say?
Capability is really innovation experimentation with AI. That's really important. And avoid is a kind of hectic experimentation without reflecting and without purpose.
How about you, Peter?
Yeah, I would say that one thing they should build up is the capability of qualifying the right projects, funding them, but also being willing to kill them if they are not delivering business value. And so that would be a good capability to have. The other one to avoid, I would say, is to treat AI as a shortcut. So if your process is do not align, or if your quality is poor of your data, it's not a shortcut to solve these kinds of things.
That's very good.
So, yeah,
very nice. Rüdiger, what do you say?
So I think the capability to build is connecting legacy systems and data sources. So it does not necessarily require a new IT system, but how to connect, how to make data available. And the mistake to avoid is to forget about the people.
Yeah, very good.