It’s AI Appreciation Day, so let’s look at where AI is delivering real value across the retail sector
by Cédric Chéreau, Managing Director at Eagle Eye
Artificial intelligence has become a widely discussed topic in retail. From tech-enabled shopping experiences to fully automated stores, the headlines are full of bold predictions, ambitious promises and theoretical breakthroughs. While these stories tend to capture attention, they often overlook where AI is already driving real, measurable change. The truth is, much of the progress happening today isn’t flashy or speculative; it’s practical. Retailers are using AI to solve everyday challenges: engaging customers more effectively, managing massive volumes of data and efficiently streamlining their operations behind the scenes.
Retailers seeing the greatest impact with AI are doing so by applying the technology strategically. In celebration of AI Appreciation Day, it’s worth highlighting the areas where AI is already creating value and successfully working within the retail environment, devoid of hype.
Personalization: From Mass Messaging to Precision Engagement
Traditionally, retail promotions relied heavily on mass marketing — distributing the same weekly offers across broad customer segments with limited (or no) differentiation. While digital channels enabled some degree of segmentation, the overall model remained centered on reach rather than relevance.
AI has fundamentally changed that approach. Today’s technology supports real-time, one-to-one personalization by analyzing individual behaviors, historical purchases, contextual cues and channel preferences. For instance, a customer known to shop for family meals on Sunday evenings may receive timely recipe recommendations and offers delivered via mobile while they plan their weekly grocery list. Another customer with a preference for premium coffee might receive early access to new product launches, communicated through their preferred channel.
This level of personalization extends beyond product recommendations; AI can now optimize the timing, language used in marketing messages and even the format of offers. Machine learning models can identify subtle behavioral patterns, such as correlations between weather and purchasing habits, to tailor communications that are not only relevant but anticipated. The result is more meaningful customer engagement, loyalty and spending.
Data Use and Privacy: Evolving From Data Collection to Data Partnership
Historically, many retailers collected vast volumes of customer data with limited transparency and insufficient consent mechanisms. Privacy policies were lengthy and difficult to interpret, and consumers often remained unaware of how their information was being used, stored or shared.
That’s no longer viable. Today, consumers are more aware, and more selective, about who they trust with their data. Global regulatory frameworks, such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA), have reinforced this shift by setting clear expectations for transparency, consent and accountability, making ethical data practices not just a priority but a requirement.
In response, retailers are adopting a more ethical, transparent approach to data use, treating it as part of a mutually beneficial partnership rather than a resource to be extracted. This means clearly explaining what data is collected, how it’s used, and what customers receive in return, such as more relevant offers, faster service, and enhanced loyalty benefits, while limiting collection to only what’s necessary to deliver those experiences.
Security plays a big role here, too. Retailers are investing heavily in strong data security measures to protect customer information from breaches and misuse and providing clear options to manage privacy preferences that give customers greater control over their personal information.
Ethics and Accountability: Advancing Explainability and Oversight in AI
In the early phases of AI adoption, many systems often functioned as “black boxes,” delivering outputs that were difficult to interpret, explain or trace. Retailers often deployed these systems without fully understanding or being able to explain how decisions were made, making it difficult to identify bias, question results or implement necessary adjustments.
Despite AI systems becoming more sophisticated, the challenge of opacity remains. Retailers are now investing in frameworks that allow them to better understand, audit and communicate how AI-driven outcomes are reached. This includes providing greater transparency into automated processes, identifying and documenting the limitations of each model, and building processes that enable oversight and review. Rather than claiming perfect explainability, responsible retailers are focusing on clarity around what can, and cannot, be understood while implementing safeguards to minimize bias and maintain ownership.
Retailers should think about AI governance in the same way they think about financial oversight: cross-functional ethics committees, conducting regular audits to evaluate algorithmic fairness and establishing clear lines of accountability for AI-driven decisions. These steps not only help mitigate legal and reputational risks but also reinforce customer trust, something that is increasingly important in today’s digitized marketplace.
Business Operations: From Pilot Projects to Strategic Integration
AI has often been viewed as a peripheral technology, tested in isolated pilot programs or confined to specific departments like IT. As a result, many initiatives struggled to deliver measurable impact or long-term value.
That’s changing. Retailers are now embedding AI into the core of their business strategy, not as a standalone tool, but as part of a larger system, using it to streamline operations, support faster decision-making and increase organizational agility. It plays a critical role across nearly every department, influencing marketing, loyalty, customer experience, inventory management and pricing decisions. Machine learning systems are also being used to forecast demand, identify inefficiencies and optimize operational functions in ways that human analysts may have otherwise overlooked.
At the individual level, team members are using generative AI tools like ChatGPT to support routine tasks, content creation and research, freeing them up to focus on higher-value activities. At the company level, AI is enabling cross-functional alignment by connecting previously siloed systems and workflows. Together, this drives enterprise-level efficiency at scale and can contribute directly to overall business performance.
The Real AI Opportunity Lies in Trust
There is no doubt that AI is transforming retail, redefining how retailers understand and serve their customers. The most impactful use cases are not those driven by novelty or hype, but those rooted in strategic intent— enhancing customer experiences, improving operational performance and building trust at every touchpoint. The retailers making the biggest strides are those who integrate AI thoughtfully and in alignment across the business.
So, as we recognize AI Appreciation Day, it’s important to not only acknowledge the technology’s potential but also commit to responsible implementation. Retailers that approach AI with transparency, accountability and a focus on customer value will be best positioned for long-term success. Remember, it’s not just about doing more, but doing it better.
Cédric Chéreau is Managing Director at Eagle Eye and has more than 20 years of experience in retail analytics, supporting retailers and FMCG companies from Europe and North America. He holds a Master of Science in Marketing from EDHEC.
Photo by Maximalfocus on Unsplash
