What happens when decades of supply chain planning collide with AI, volatility, and a world that no longer moves at a predictable pace?
That question sat at the heart of my conversation with Piet Buyck, a serial entrepreneur whose career spans early optimization engines, cloud-era planning systems, and now AI-driven decision environments. Speaking from Antwerp just days before the holidays, Piet brought a calm, grounded perspective shaped by years inside organizations operating under real commercial pressure. His journey includes building Garvis, an AI-native planning platform later acquired by Logility, which itself became part of Aptean. That arc alone tells a story about consolidation, scale, and where modern planning is heading.
We spent time unpacking ideas from Piet's book, AI Compass for Supply Chain Leaders, particularly his view that planning drifted too far into abstract numbers and away from real-world context. Long before AI became a boardroom obsession, he saw how centralized models created distance between decisions and reality. When disruption arrives, whether through pandemics, tariffs, or geopolitical tension, that distance becomes costly. Piet shared vivid examples of how slow, spreadsheet-heavy processes fail precisely when speed and clarity matter most.
One thread that kept resurfacing was data. Many leaders believe their data is "good enough" until volatility exposes blind spots. Piet pushed the conversation further, explaining that AI's value goes beyond crunching clean datasets. It can move understanding across silos, surface the reasons behind decisions, and make context visible without endless meetings. That idea of explainable, collaborative AI came up repeatedly, especially as a counterpoint to opaque automation that creates confidence without understanding.
We also tackled the human side. There is anxiety around skills erosion and entry-level roles disappearing, but Piet's view was more nuanced. AI shifts where time and energy go, away from gathering information and toward judgment, fairness, and accountability. In his eyes, the real challenge for leaders is choosing the right scope. Projects that are too small fade into irrelevance, while those that are too big stall under their own weight.
As we looked ahead, Piet reflected on how leadership itself may change as data becomes accessible to everyone. Authority based on instinct alone becomes harder to defend when assumptions are visible. The leaders who thrive will be those who can explain direction clearly, connect data to purpose, and bring people with them.
So after hearing how planning, AI, and leadership are converging in real organizations today, how do you see the balance between human judgment and machine intelligence playing out in your own world, and are we truly ready for what that shift demands?
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