AI
Ethical AI applications in retail: Building trust through responsible data use
Retailers that embrace sound data management and collection principles position themselves for long-term success in an AI-driven marketplace.
November 26, 2025 by Jean-Matthieu Schertzer — Chief AI Officer, Eagle AI
As AI becomes more integrated into retail operations, from bespoke recommendations to dynamic pricing, retailers are beginning to grapple with how to use customer data ethically while delivering the personalized experiences consumers expect.
Poor data practices can erode customer trust, trigger compliance issues, and damage brand reputation.
But retailers who get it right can build lasting customer relationships while driving profitable growth.
"Getting it right" requires rethinking how to approach customer data. Rather than viewing data as a resource to be collected and exploited, leading retailers are adopting a partnership approach that prioritizes transparency, security, and mutual value creation with their customers.
It's clear: Transparency must be the foundation
The most critical principle in ethical AI implementation is transparent consent practices. Customers understand their data will be collected, but they expect retailers to be upfront about their intentions and how the data will be used. This might mean moving beyond lengthy, overwhelmingly complex privacy policies that few customers read or understand.
Retailers that understand (and respect) this dynamic are implementing clear, accessible consent mechanisms that explain data collection in plain language. They're creating control panels and portals where customers can easily understand what information is being collected, how it will be used, and what benefits they'll receive, and modify or withdraw consent if needed.
Collecting data with a purpose
Retailers should limit data collection to information that directly improves customer experience and operational efficiency. Indiscriminately collecting as much data as possible is both ethically questionable and practically wasteful. Just 5% of companies fully utilize the data available to them, suggesting that quality and purpose matter more than quantity.
To prioritize data quality, there are a few questions retailers should ask before collecting customer information. Will this data directly improve our ability to serve this customer? Can we demonstrate clear value in exchange for this information? Are we collecting only the data necessary to deliver on our promise?
This purposeful approach extends to AI model development. Rather than feeding algorithms to every available data point, retailers are curating datasets that focus on relevant customer behaviors and preferences, which improves model performance while reducing privacy risks.
Security as a competitive advantage
Strong security measures are essential to privacy. Considering the steady stream of headlines about data breaches in the news, we can assume that some consumers will choose retailers based on a strong track record of protecting data. This ties security investments directly to a retailer's customer acquisition and retention efforts.
This means implementing comprehensive security protocols that protect customer information throughout the full lifecycle. For AI initiatives, advanced encryption, access controls, and monitoring systems that proactively detect potential breaches are solid data security capabilities. Regular security audits and strict incident response plans are other best practices that can minimize damage when problems arise. Retailers that devote resources to these practices and capabilities set themselves apart with a clear edge in AI security.
Sharing data is a partnership, not a transaction
Security is only one component of responsible data use, however. The most successful retailers are moving away from extractive data relationships with their customers toward genuine data partnerships. In this model, customers willingly share information because they receive tangible, ongoing value in return. This might include personalized product recommendations that save time, exclusive offers that reduce everyday expenses, or early access to products they care about.
These partnerships work because they're built on mutual benefit rather than one-sided gain. Customers can see how their data improves their shopping experience, and they retain meaningful control over the information they share. This creates a positive feedback loop where engaged customers provide more data, enabling better personalization. AI accelerates that feedback, but it also magnifies the importance of having a collaborative approach to customer data from the outset.
Regulatory compliance as strategic planning
If richer, more profitable relationships with customers are the carrot, the stick is compliance with data regulations like GDPR and CCPA. As such, these considerations should be integrated into AI strategies from the beginning, not added as an afterthought. Smart retailers view regulatory requirements as guiding principles for responsible AI system development, not as red tape.
This means building systems that support customer rights by design, including easy data access, correction, and deletion capabilities. It also means maintaining detailed documentation of data processing activities and implementing governance structures that ensure ongoing compliance.
Mastering the AI-data privacy balancing act
Retailers that embrace sound data management and collection principles position themselves for long-term success in an AI-driven marketplace. They build customer trust through transparency, create competitive advantages through responsible innovation, and reduce regulatory risks through proactive compliance.
They also understand that ethical data use isn't a constraint on AI capabilities but a foundation for sustainable growth. But most importantly, they treat customers as partners rather than data sources, creating the trust necessary to fuel a positive, mutually beneficial brand-consumer relationship. And that's how they get the balancing act just right.
About Jean-Matthieu Schertzer
Jean-Matthieu is the Eagle Eye Group’s first Chief AI Officer, bringing his pioneering, forward-thinking AI expertise to retail solutions. As an Ecole Polytechnique alumnus, he has embraced various roles throughout his career, including research engineer and R&D data scientist. He is currently leading the overall AI strategy for Untie Nots and Eagle Eye’s leadership team to design, develop, and implement AI technologies in retail brands worldwide.
