Data and AI – Logistics in Practice

AI and Data in Logistics: Between Gimmick and Real Value Creation

In another engaging episode of the Logistics in Practice podcast, Henrik Schneider from SCM for Logistics and Michael Buck from YEARNING.Consulting once again venture into an experimental format – in two ways. On the one hand, voices, insights, and observations on the topic of AI in logistics were not recorded in a classic interview format, but rather condensed and voiced by a synthetic speaker. On the other hand, they tackle a topic currently featured in almost every trade publication: Is artificial intelligence in logistics just hype, or already a true driver of efficiency?

Practical Experience Meets Technology Discourse

Michael Buck, a seasoned logistics and production consultant, shares practical insights from real projects, especially where data management, planning processes, and operational decisions intersect. Together with the synthetic AI statements, the contributors explore questions that are currently occupying many practitioners: Where do we stand in using tools like ChatGPT or Microsoft Copilot? Are Power BI, SAP IBP, and standard tools enough for a meaningful start – or is more needed?

Between Potential and Reality

The discussion becomes especially interesting when turning to daily business reality. Because there is often a significant gap between technical possibilities and actual use. Many companies have access to modern tools but use them only partially or not at all. The issue is not the software itself, but rather a lack of curiosity, internal training, and clear use cases. This episode makes it clear: data alone does not create progress – real value comes from combining openness, competence, and the courage to apply these tools.

Changing Structures, Roles, and Responsibilities

The conversation also touches on organizational aspects: How does the collaboration between departments and IT change when self-service BI and AI agents become standard? What conditions do employees need in order to take initiative themselves? And what role do training, change management, and trust play in this transformation?

Realistic Insights Instead of Futuristic Promises

This podcast deliberately avoids exaggerated future forecasts. Instead, it offers practical insights: Where does AI already provide noticeable relief – such as in master data maintenance, complex analysis, or forecasting? And where do new challenges arise, like overload, data privacy concerns, or the risk of a digital divide between tech-savvy and disconnected teams?

A Call to Proactive Action

The episode ends with a clear call to action: Companies should not wait for the next big technological leap, but actively engage with the tools already available. AI is no replacement for people – but it is a powerful lever in the hands of those willing to take responsibility and learn something new.

To the episode: Link to episode on Spotify

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