Uncertainty into advantage.

The shift from efficiency to resilience is reshaping how large organisations think about supply chains and the companies paying attention now will have a serious advantage. Most large companies have spent the last decade optimising their supply chains for one thing, cost. Find the cheapest supplier, tighten the margin, repeat. I sat down with Iggy Bassi, co-founder of Earthena AI, to talk about what's changed, and why the companies still running that playbook are quietly accumulating risk they can't yet see on a balance sheet. This episode of The Holm Edit podcast is one I'd recommend anyone working in FMCG, supply chain, or sustainability strategy to listen to. Here's what we covered. The market has moved. Most companies haven't. Climate 1.0 was a measurement problem. We got very good at counting backwards; reporting on what already happened, quantifying what had already been emitted, auditing what had already gone wrong. Useful, but not sufficient. Iggy explains that what organisations actually need now is intelligence that surfaces at the point of decision making. Not a dashboard you check quarterly. An engine that tells you, right now, that your lowest-cost sourcing option is also your most fragile one. “The competitive equation is shifting from least cost toward most resilience. Markets are going to reward companies that are smart about these constraints.” That shift has significant implications for how sustainability strategy gets built inside large businesses. It stops being a reporting function and starts being a core input into commercial decision making. The practical example that reframes the whole argument One point that I think will resonate with anyone who has sat in a procurement meeting was when Iggy described working with an FMCG business looking at sourcing options across a countries in Asia. Their instinct was to go with the lowest cost option. Standard practice. When they ran those options through the Earthena AI engine, factoring in grid stability, water stress, physical climate hazards, policy exposure, the lowest cost option came back as the lowest resilience option within a two-year horizon. It would have looked fine on a spreadsheet, but it would have been a liability in practice. That's the argument for this kind of intelligence, made concrete. Not a sustainability case. A commercial one. What the Quant Earth Engine actually does We spend time in the episode going into how Earthena's model actually works, the four layer structure that integrates physical hazards, carbon, policy, water, and social data alongside a company's own spend and supplier data, and uses natural language so that procurement and risk teams can interrogate it without needing to be data scientists. The generalisability point is worth flagging because approximately 80% of the solution works across different clients and sectors. The remaining 20% accounts for sector specific complexity. That structure matters because it means the infrastructure for this kind of decision intelligence doesn't have to be built from scratch by each organisation. On social data, labour rights, wage theft, reputational exposure, Iggy is candid that this has been harder to incorporate than environmental data. It's a significant gap in most existing tools, and one that your sustainability teams will be pushing on. A note on who this is and isn't built for I want to be transparent about one tension I've been sitting with since recording this episode. The tools being described here may be difficult for smaller organisations. The intelligence, the infrastructure, the capacity to act on what the engine surfaces, these require significant resource and digital maturity to deploy. That raises a question the conversation doesn't fully resolve, if only mature organisations can act on this kind of foresight, does it widen the gap between them and the smaller suppliers and producers in their value chains? Resilience as competitive advantage is a compelling frame. But resilience concentrated at the top of the supply chain, while risk continues to flow downward, is a different kind of problem. It's worth thinking about. And it's a thread I'll be pulling on in future episodes. Three things worth taking away Efficiency and resilience are not the same objective. Optimising for one can actively undermine the other. Uncertainty is permanent. The question isn't how to eliminate it, it's how to build systems that turn it into an advantage rather than an exposure. Intelligence needs to be embedded into workflows, not housed in dashboards. If the data isn't at the point of the decision, it won't change the decision. Listen to the full episode The full conversation with Iggy Bassi goes deeper into the technical architecture, the social and geopolitical data layers, and his advice for enterprise leaders navigating this transition. Well worth your 25 minutes!