122 VIEWPOINT Merchants are still acting as the human glue between disconnected systems – and this is rapidly becoming one of merchandising’s biggest problems. The solution lies in agentic AI NOAH HERSCHMAN: INTELO.AI Why better data is no longer enough Merchandising sits at the very heart of retail. And yet, it has consistently been handed the lowest-grade software in the building. This is one of the biggest problems facing retailers today – and it goes much deeper than many realise. The issue is not that merchants lack data. If anything, they are drowning in it. The issue is that the process of turning data into decisions remains almost entirely manual. Merchants are still acting as the human glue between disconnected systems – pulling figures from an enterprise resource planning (ERP) solution here, a business intelligence (BI) tool there, a planning spreadsheet somewhere else, then stitching it all together to run a single what-if scenario. By the time that scenario is ready, the market has already moved. Plans that take the best part of a week to build are partially stale before they are finalised. The cost of that latency is measured in lost margin, missed trends and reactive decisions made too late to matter. Legacy machine learning systems tried to solve this. But they made things worse – and in a specific way. They moved fast, but they moved opaquely. A recommendation would arrive and no one could explain it. The demand forecast, the transfer proposal, the markdown suggestion – all of it from a black box, and merchants were asked to trust it or ignore it. That bargain was never going to hold. When a recommendation misses badly and you cannot figure out why, you lose confidence in the whole system. The spreadsheets come back out. We have all seen it happen. Agentic AI is a fundamentally different proposition. The distinction is not speed (although the speed gains are real – what used to take three days now takes 40 minutes). The distinction is reasoning. It tells you what data it used, what assumptions it made, what tradeoffs it considered and what it recommends you do next. Critically, it ingests live signals in real time, such as market data, inventory movement and external signals like weather patterns or upcoming events, and acts on them continuously, not in weekly batch cycles. You can question it and ask it to rerun the scenario with different constraints. And every override, adjustment and accepted or rejected recommendation becomes a training signal that makes the system smarter. Leading retailers are already realising the benefits. Balenciaga, the global luxury fashion house, deployed Intelo.ai’s Collaborative Intelligence Agent Network to augment human decision-making and master the complexities of modern luxury retail. Clothing retailer The Children’s Place, improved sell-through by 42 per cent. These are not marginal gains – they are the compounding result of decisions being made faster, with better information and with institutional knowledge captured rather than lost. That last point matters more than it might appear. One of the most underappreciated costs “Merchandising is a blend of art and science – and AI will not replace the creative dimension”
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