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By Patrick Elhindi

Not long ago, AI agents weren't part of how we ran operations. Now I can't imagine going back.

When you're managing eCommerce across many markets, the operational complexity stacks up fast. Different currencies, different promotional calendars, different fulfilment setups. We started using AI agents as part of our day-to-day operations, and it's changed more than we expected.

Not chatbots. Not product recommendation engines. Actual AI agents that sit across our existing tools and do real operational work. Here's what that looks like in practice.

Our company was an early adopter of AI. So when AI agents became viable for real-world business use, it wasn't a hard sell internally. We had people within the business who saw the potential early and were keen to explore it.

The catalyst wasn't a single crisis. It was the reality of running across multiple markets and channels. Getting a clear picture of what's happening across the business meant logging into multiple platforms, manually pulling reports, and cross-referencing data. Beyond that, finding time for deeper analysis, process optimisation, and improving accuracy across markets was time-consuming. There was always more to dig into, but the time just wasn't there.

Traditional automation helped with some of this, things like scheduled reports and basic integrations between platforms. But it only works for repeatable, predictable tasks. The moment you need to ask a question that doesn't fit a pre-built report, you're back to doing it manually.

AI agents changed that. Instead of building a new report every time someone asks a new question, we can query our ERP directly and get an answer in seconds. The question doesn't need to be anticipated in advance. It just needs to be asked.

Here are some of the ways AI agents have changed how we operate:

After a promotion or at the end of a trading period, understanding what actually happened used to take time. Pulling data from different platforms, stitching it together, and trying to compare performance across markets with different currencies and timelines.

Now, we can ask an AI agent to pull the data, break it down by region and channel, and surface what worked and what didn't. The analysis that used to take a day now takes minutes, so we can act on it while it's still relevant.

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