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.
Why We Started
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.
What It Actually Looks Like
Here are some of the ways AI agents have changed how we operate:
Analysing Performance Across Markets
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.
And because getting answers is fast, we're asking more questions, more often. That shift from weekly reporting to real-time interrogation changes how you make decisions.
Operational Queries
If we need to check stock levels, review transfer order status or investigate a discrepancy, we describe what we need in plain language and get an answer. It sounds simple, but when you're doing this dozens of times a day, the time savings compound fast.
Building Internal Tools
We've also used AI to build internal tools that would previously have required external development resources.
One example is a launch-readiness dashboard for new products. It tracks new SKUs across our markets, combines warehouse data with live checks from our eCommerce platform and PIM to show which products are ready to go live and which are blocked.
The kind of tool you'd normally either buy off the shelf or commission externally, built in-house with AI.
And unlike an off-the-shelf app, we can keep building on it. Every new request makes it more tailored to how our business works.
But it goes further than that. Over time, the features themselves surpass what any pre-built app on the market offers. You're not just customising, you're building something better.
Multi-Market Complexity
Every market has its own quirks: different pricing, promotional timing and channel mix. AI agents handle the complexity of querying across currencies and calendars without needing to remember which filters to apply where.
What Surprised Us
The wins we expected were speed and efficiency. We got those. But a few things caught us off guard.
The Barrier to Entry Was Lower Than Expected
We didn't need to rip out our existing tech stack or run a massive implementation project.
That said, it wasn't completely plug-and-play either. The team built some custom connectors to link our AI agents to our existing tools. But compared to a traditional software rollout, the effort was minimal, and the time to value was short.
It Changed What Questions We Ask
When getting an answer is easy, you start asking more questions. We went from reviewing sales data weekly to interrogating it in real time.
That's not just faster reporting. It's a different way of running the business.
It's Not Perfect
AI agents are great at structured queries and data retrieval, but they still need human judgment for context. They can tell you what's happening, but you still need to decide what to do about it.
The best results come from treating them as a tool that extends your capability, not a replacement for thinking.
Where This Is Heading
We're still early in this. But we're already seeing meaningful changes to how we operate. I think this is just the beginning for eCommerce brands.
The big shift I see coming is this: AI agents will become the default way eCommerce teams interact with their tech stack. Instead of learning how to navigate different platforms, you'll describe what you need, and an agent will go get it for you. The interface becomes the conversation, not the dashboard.
And before long, this won't be a competitive advantage. It'll be the bare minimum. Brands that aren't using AI to run operations will be slower, less informed, and work harder for the same results. The gap between those who adopt early and those who don't will only widen.
For eCommerce brands especially, this matters. Small teams wearing multiple hats, managing multiple markets, across multiple platforms. AI agents give those teams extra capacity without requiring headcount growth.
The question isn't whether AI agents will change eCommerce operations. It's whether you'll be the one leading or catching up.
