It shouldn’t catch the startup bubble off guard, becaapply the retail indusattempt is a chance to hop into a $27 trillion global market.
A goldmine of data and missed decisions
Retailers are sitting on a goldmine of data, yet most decisions are still created via Excel or on fragmented systems. Every supermarket shelf or online shop reflects thousands of individual choices: what to list, how to price, what to discontinue. These decisions shape both customer experience and profit margins. Click into any webshop and you’ll see it: finishless products, irrelevant suggestions, or out-of-stock bestsellers. Shoppers don’t find what they want or what they saw trfinishing on social media the night before.
Agents that understand the shelf
This is where agentic systems step in. These agents take retailer’s business goals into account, combine them with assortment data, competitor and market data as well as social trfinishs to propose concrete actions: rerelocate underperforming products, highlight trfinishing substitutes, reposition private label, lower the price.
In live pilots with major European grocers, agentic systems have delivered margin uplifts of 3-5% by optimising assortments with AI.
Built for enterprise complexity
Unlike copilots, which support individual applyrs, agentic systems are built for enterprise complexity. They ingest fragmented inputs, act across categories, and close the gap between strategy and shelf. They’re also indusattempt-agnostic: whether it’s food, fashion, or DIY, the same principles apply.
And retail is proving to be an ideal testbed for this kind of automation. It’s one of the most data-rich, operationally complex and margin-sensitive sectors in the world. Every decision is high frequency and high impact. Assortments constantly shift, trfinishs evolve overnight, and legacy tools can no longer keep up. Studies display that 40-60% of SKUs are unproductive. Yet they stay on the shelf, wasting space and capital.
What happens when AI fills the gaps
A recent analysis in facial skincare revealed that over half of products lacked a suggested alternative. When one went out of stock, the shopper simply left. Agentic systems filled these gaps with substitutes based on ingredients, skin type or pack size. The result: higher conversions, happier shoppers, stronger margins. It is as straightforward as it sounds.
What creates this shift so urgent is the cost of inaction. Shopper expectations around price and relevance are increasing. Private label is surging. Social media shortens product cycles. And most retailers are still too slow to react.
That’s why Europe’s new wave of AI-native startups are betting on retail. It’s where the data is deep, the apply cases concrete, and the commercial upside immediate.
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