When everyone uses AI, where does competitive advantage come from?

Twenty years ago, Amazon’s recommendation engine became one of the defining competitive advantages of early e-commerce, reportedly driving around 35% of their customers’ purchases. But beyond its immediate value to Amazon, the recommendation engine established a template that the rest of the industry would spend two decades trying to replicate. 

In a similar way, the history of AI in e-commerce largely consists of innovations pioneered by a handful of large players gradually becoming accessible to everyone else. What Amazon and its peers once built as bespoke competitive advantages are now increasingly available as operational tools for mid-market retailers and beyond. 

As its accessibility grows, industry data suggests that AI is rapidly becoming a baseline capability rather than a source of differentiation. Recent research shows that 89% of retailers are now either actively using AI or running pilot programmes. Perhaps more tellingly, three quarters of small and medium-sized businesses are at least experimenting with AI tools, if not already using them. 

The widespread adoption of AI raises an uncomfortable question for retailers: if everyone has access to the same tools, can AI still deliver a meaningful competitive advantage? 

When access to AI stops being an advantage 

The democratisation of AI tools has created a paradox. The technologies that once differentiated Amazon from its competitors are now available, in some guise, to almost every retailer. Recommendation engines, customer segmentation, demand forecasting and dynamic pricing systems can all be procured as services or deployed from increasingly sophisticated – and widely accessible – platforms. 

Unfortunately, accessibility is not the same as effectiveness. 

Off-the-shelf AI solutions are designed to work reasonably well across a wide range of contexts. Trained on general datasets and optimised for common use cases, these tools are effective for routine tasks such as handling standard customer enquiries via chatbot. Beyond that, however, their limitations become clear. 

The choice is not simply between buying an off-the-shelf product and building a bespoke system. Increasingly, meaningful competitive advantage comes from making AI context-specific: combining widely available technology with the data, processes and operational knowledge unique to an individual business. 

Competitive advantage comes from context 

Take customer service. There is a significant difference between a chatbot that answers routine questions and one that has the data insight to genuinely understand a retailer’s products, policies and customers. 

Consider a major pet products retailer using an LLM-based system that combines its product catalogue with years of support history, using retrieval-augmented generation (RAG) to deliver accurate, grounded responses. The objective is not to replace human agents, but to help manage high enquiry volumes during seasonal peaks while allowing staff to focus on conversations that require judgement. 

The same principle applies to demand forecasting. Generic forecasting tools can be deployed quickly, but their value depends on how well they account for variables that matter to an individual retailer including product lifecycles, promotional sensitivity and regional demand variations. 

A model that forecasts with 70% accuracy may be acceptable in one context and commercially unusable in another. By comparison, when forecasts are built around these business-specific drivers, they drive better inventory decisions rather than simply producing technically competent noise. 

Research consistently supports this pattern: companies that build or substantially customise their AI systems outperform those relying solely on generic solutions. And consumers notice the difference too: a recent study found that 45% of consumers are frustrated by AI-powered ecommerce experiences, with the generic nature of AI recommendations among the leading complaints.  

This is not because simply bespoke AI is inherently better. The process of tailoring forces business to identify the problems that matter most and build solutions around the realities of their operations. That, rather than the technology itself, is often where the competitive advantage lies.  

From potential to commercial impact 

For e-commerce businesses, the primary question is no longer whether to adopt AI. The technology has matured enough that non-adoption carries its own risks. Instead, the more strategic question is which applications will generate the most meaningful returns for your specific business. 

In most cases, data – not algorithms – is the limiting factor. Even the most sophisticated model will underperform if trained on poor quality or unrepresentative data. Investments in data quality and infrastructure therefore often yield greater returns than chasing the latest advanced AI capabilities. 

Execution is equally important. The gap between a promising proof of concept and a system that operates reliably at scale is where many AI initiatives falter. Clear problem definition, realistic timelines, appropriate resourcing, and effective integration into existing workflows often determine outcomes more than the sophistication of the underlying model. 

The pattern is consistent: AI capabilities that once required exceptional resources are now widely accessible, but access alone does not create an advantage. The gap between generic tools and solutions tailored to a specific business’s data and operations remains the difference between adequate and effective. 

To truly unlock AI’s promise of competitive advantage, retailers must move beyond adopting AI tools and start building AI capabilities around their own data, processes and customers.