Retail's Quiet Winners: The Patterns Behind Them

AI is constantly in the headlines. Often, given the nature of our relationship with news, the headlines aren’t favourable. Whether it is stats like; “55% of customers don’t trust AI chatbots” to “Chevrolet dealership’s AI gets manipulated into selling car for $1” to “MIT say 95% of AI initiatives fail”, the effectiveness of the technology is constantly challenged.

The challenge is not the technology though, but in how it is applied. Outside of the headlines, there is a growing cohort of retailers, quietly succeeding in the implementation and scaling of AI capabilities They test rigorously, scale what works and put much of their focus in the change required and in bringing their people on the journey. They’re not pursuing AI for AI’s sake. Instead, they’re solving real business problems with proven returns and most of their AI is invisible to their customers.

The Quiet Winners

Decathlon, the world’s largest sporting goods retailer, is a great example. Through a pilot on their loyalty program in their Polish market, they conducted a comprehensive analysis of millions of members and their transactions. There was a clear goal to prove the program would generate profit, not just engagement, before it could be scaled.

Using AI to identify purchasing patterns, create buyer personas and test voucher mechanics, the insights transformed operations across the organisation. For example, now store teams receive AI-generated insights showing that a customer who buys hiking boots typically purchases camping gear within six weeks, or that cycling enthusiasts have distinct seasonal patterns. These predictive models inform inventory decisions, staff scheduling, and personalised communications. The program operates profitably across 69 countries and 2,080+ outlets, with customers simply experiencing more relevant service, never seeing the AI beneath their experience.

Adore Beauty deployed AI-powered recommendation engines that analyse both structured sales data and unstructured customer data to deeply understand nuances in customer preferences, such as lipstick shades.

The results speak to operational discipline rather than technological sophistication, with a significant improvement in EBITDA and a decrease in marketing spend. At the same time, customer retention reached a record 64.7%. The AI works across the value chain, from product recommendations to inventory optimisation to content personalisation. However, customers simply experience more relevant product suggestions and better service.

Lowe’s Home Improvement’s implementation of an AI-powered workforce management solution in 2024 shows how operational AI directly improves customer experience. The system analyses foot traffic patterns, sales data, seasonal trends, and local events to optimise staff scheduling across departments. The AI learns that garden centre traffic surges on Saturday mornings in spring, that lumber departments need extra coverage during contractor hours, and that appliance sales require more expert consultants on weekends.

Within eight months, Lowe’s saved over $1 million in operational costs. This includes the automation of 434,000+ hours of store roster changes, freeing Store Managers from administrative burden to focus on team development and customer service. Managers receive predictive staffing recommendations weeks in advance. This means customers can find knowledgeable staff when they need them, their wait times are reduced and service quality is improved. This is without ever knowing AI was involved.

The Pattern Emerges

These quiet winners share three interesting characteristics that separate them from the failures dominating headlines.

First, they focus on operational improvement over customer-facing innovation. This choice is strategic. Operational AI delivers measurable ROI: margin improvement, cost reduction, efficiency gains. These forms of retail AI are invisible to the customers they serve, improving the business that serves them, rather than intermediating between them and the business.

Second, they understand the value of their people and consistently focus on change management. BCG research reveals successful AI implementations follow a consistent resource allocation: 10% on algorithms, 20% on data and infrastructure, and 70% on people, processes, and organisational change. Decathlon empowered store teams through customer insights. Lowe’s freed managers from repetitive admin to focus on customer service. Each time, the technology amplifying human capability rather than attempting to replace it.

Third, they prove value before scaling. Decathlon’s pilot in Poland preceded careful expansion to three further test markets, then gradual global rollout. Lowe’s implemented in phases, measuring impact at each stage. This disciplined approach generated evidence for continued investment while limiting risk exposure.

Over the coming weeks, we’ll explore these patterns in more detail. We’ll examine why successful companies invest 70% of their AI resources in people and process change. We’ll investigate how pilot-then-scale methodology builds sustainable competitive advantage.

The Path Forward

The next 12-18 months will likely see more retailers quietly transforming their operations through AI, focusing on inventory optimisation, supply chain efficiency, and workforce productivity. The winners won’t announce grand AI strategies. They’ll announce improved margins, better customer satisfaction scores, and sustainable growth.

Success in retail is about being disciplined, pragmatic, and relentlessly focused on value creation. The quiet winners understand this. They’re building the future of retail through evolutionary improvements that compound over time. The question for every retailer is simple: will you join them in doing the work?