Why your eCommerce business needs an AI strategy

In the 1980s, it was “nobody ever got fired for buying IBM.” In 2024, it’s “nobody ever got fired for buying AI.” We’re at the cusp of (yet another) revolution in IT, and the impact of AI in business promises to be as profound as the arrival of smartphones and cloud computing.  

And with it, technology industry decision-makers are feeling an immense pressure to embrace AI — or at least, to build the foundations to successfully adopt AI in the near future. This pressure is especially evident in the eCommerce sector, where AI is increasingly touted as a means to help retailers adapt to changing consumer trends, a shifting mar-tech (marketing technology) stack, and reduce costs.  

In our latest blog, we address two fundamental questions: Why should eCommerce professionals care about AI, and what do they need to do to successfully embrace AI?  

Designing your AI strategy

When you speak to technology leaders about their motivations behind embracing AI — even at this early stage — you inevitably receive several different answers. A common refrain is the desire to automate processes and unlock efficiencies. A well-trained AI system can make decisions faster than a human, and at a far greater scale. Others talk about wanting to unlock capabilities that were otherwise unfeasible, whether due to cost or the technical complexity involved.  

eCommerce leaders will likely echo those sentiments, but they may also mention the challenges they expect to face moving forward with content personalisation. With Google’s ongoing retirement of the tracking cookie, the worldwide introduction of tougher privacy legislation, and the erasion of on-device tracking on mobile devices, it’s becoming harder to build individualised profiles of customers and potential customers. 

Many online retailers fear these changes will make it harder to market effectively, or to deliver the kind of personalised experiences that consumers expect. According to a 2021 McKinsey report, 71 per cent of consumers “expect companies to deliver personalised interactions.” A further 76 per cent feel frustrated when these interactions don’t happen.  

For eCommerce professionals, AI is a potential solution, allowing them to craft the kind of bespoke experiences and interactions that consumers want, simply by using the data they already hold. The same McKinsey report found that fast-growing online businesses could attribute at least 40 per cent of their growth from personalisation. As digital retailers struggle with inflation and the growing cost of living, AI could provide a welcome boost.  

The benefits of AI will inevitably trickle down into other, non-customer-facing parts of the business, too. A good example is inventory management, where AI can predict consumer trends, and help retailers minimise wastage.  

These two examples are far from a complete list, but they illustrate the diverse applications of AI. As you build your AI strategy, it’s worth asking the simple question: “What are my business’s pain points, and what could fix them?” 

Building the architecture for AI

Just like with other IT revolutions, the advent of AI will force online retailers to make vast changes to their operations. And that’s because AI isn’t a product you buy, but rather a tool that requires proper maintenance, deployment, and management. Like an exotic houseplant, it can only thrive in the right conditions. 

First, you need to establish the right architecture. Your business likely generates data from several touch-points — like web analytics tools, email marketing systems, and the various products that handle logistics. From an AI perspective, this data becomes most useful when it’s combined and aggregated.  

In practice, this means your organisation needs to become data-driven, with system interoperability a key focus. You need to build the middleware to gather and unify data from various sources, enabling accurate, reliable, and real-time insights. For many organisations, accomplishing this may require radical changes, particularly for those using legacy (and monolithic) eCommerce platforms.  

The second change is cultural. Your business needs to act like a tech company, recruiting talented AI and software engineers, and building systems in a way that facilitates future digital transformation, and therefore AI adoption.  

Domain-driven design can help here. By visualising your tech stack as a series of interconnected systems that address specific business needs, identifying opportunities to use AI becomes easier. Your software — which might consist of millions of lines of code — becomes more obvious and explainable, and you’ll find it easier to iterate and improve. 

This approach can introduce a sense of ownership among your engineering team, with specific individuals responsible for specific features. And so, when it comes to building the interoperability you need for effective AI adoption, you know who to turn to.  


Just like the arrival of cloud computing and the mass-adoption of the smartphone, AI demands that businesses — especially those in the eCommerce world — make some drastic changes. These changes aren’t just limited to your tech stack, but also to how your engineering talent works and collaborates.  

Change isn’t always easy, but starting now can pay dividends. We’re only at the start of the AI revolution, but we can already identify how it’ll help online retailers market in a post-tracking world, optimise their operations, and adapt to meet changing consumer habits.  


Want to know more?

If this sounds daunting, take comfort in knowing you’re not alone. SQLI has helped some of the world’s largest retailers embrace AI, become highly data-driven, and adopt modern system architecture practices. If you’re looking for a partner on your AI journey, get in touch with the team.