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In this week’s episode of the Sustainable Supply Chain Podcast, I sat down with fellow Irishman Paul Byrnes, CEO of Mavarick AI, to explore how manufacturers can use AI and data to tackle the notoriously difficult challenge of Scope 3 emissions.

Paul brings a unique perspective, rooted in both deep data science and hands-on manufacturing experience, and he didn’t shy away from the hard truths: most companies still struggle with messy, unreliable data and limited supplier engagement. We unpack why primary data will soon become table stakes, why spend-based estimates can be 40% off the mark, and how engaging suppliers requires a simple but often overlooked question, what’s in it for them?

We also discussed where AI genuinely moves the needle:

• Boosting confidence in data accuracy by identifying gaps and “contaminated” entries
• Providing personalised training to help suppliers meet sustainability requests
• Uncovering and prioritising decarbonisation levers with clear ROI

Paul shared real-world examples, from medical devices to automotive, that show how targeted projects, rather than trying to tackle all 15 Scope 3 categories at once, deliver the best results. We also touched on the environmental footprint of AI itself, energy, water, rare materials, and how responsible computing and smaller, purpose-built models can reduce the impact.

For leaders wrestling with emissions strategy, Paul’s advice is simple: start by mapping your data landscape. Know where you’re rich, where you’re poor, and build from there.

This is a practical, candid conversation about making sustainability and profitability work hand-in-hand, and why efficiency wins are so often sustainabi

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