In their conversation for The Bridge, Artefact’s Édouard de Mézerac, Group CEO and Global Lead Retail & Luxury, and Vincent Blaclard, Managing Partner & France Lead Retail, discussed the different ways artificial intelligence is reshaping the retail industry by redefining how brands understand customers, manage operations, and generate growth.
Edouard de Mézerac was appointed Group CEO in March 2025. He leads Artefact’s Industry Practices worldwide, with a particular focus on retail and consumer goods. Much of his career was spent in the United States and Asia, where he developed in-depth expertise in AI-driven luxury and retail ecosystems. He is an alumnus of HEC Paris.
Vincent Blaclard joined Artefact in 2021, and has since worked in the Retail practice with leading French retailers (Carrefour, Fnac Darty), while opening new logos: Lagardère Travel Retail, as well as Française des Jeux and Tarkett. He holds degrees from France’s Ecole Polytechnique and New York University’s Courant Institute.
The discourse surrounding AI in retail has matured. No longer framed as a race for technology, it is now a strategic debate about competitiveness, resilience, and differentiation. What began as isolated experiments in personalization and demand forecasting have evolved into a structural shift across the entire value chain. Retailers are now designing data-driven ecosystems that bring intelligence to every decision, from product design and pricing to customer engagement and supply chain efficiency.
The question for retailers today is not whether to use AI, but how to use it effectively and responsibly to create value that lasts beyond short-term gains.
Data collection for connected value
Data has always been the lifeblood of retail, but the way it is collected, structured, and activated has changed profoundly. The sector was among the first to digitize its customer relationships through loyalty programs, e-commerce, and omnichannel systems that track every purchase and interaction. Yet these systems also created fragmentation.
Today’s challenge is to reconnect the dots. Retailers are consolidating transactional, behavioral, and contextual data into unified customer platforms that feed both analytics and AI. The goal is to move from static reporting to dynamic intelligence: not just understanding what customers did, but predicting what they will do next.
As Edouard points out, the key to maturity lies in the ability “to connect every signal and make it actionable in real time.” That connection depends on robust architecture: cloud-based data platforms, real-time ingestion pipelines, and governance models that make data accessible and trusted across the business.
For many retailers, this is as much an organizational transformation as a technical one. Data ownership is shifting closer to operational teams, empowering marketers, category managers, and store networks to act on insights directly. AI is becoming an enabler of agility rather than a tool reserved for data specialists.
Rethinking performance across the value chain
The first visible impact of AI in retail came from process optimization: automating forecasting, adjusting pricing, and streamlining logistics. These initiatives remain essential, but there’s a shift toward broader performance. How can AI simultaneously improve profitability, sustainability, and customer relevance?
Retailers now view AI as a lever across three complementary dimensions:
- Operational excellence: automating repetitive tasks such as invoice matching, demand forecasting, or stock replenishment to reduce cost and increase accuracy.
- Customer experience: delivering personalized journeys and targeted promotions that enhance loyalty and average basket size.
- Strategic insight: identifying new growth opportunities, from assortment expansion to localized pricing and product innovation.
Each dimension depends on the same foundation: high-quality data and a clear understanding of business priorities.
Vincent explains: “AI delivers its full potential when it solves a specific business problem and is scaled across functions, not confined to innovation labs.”
This change of focus is driving new ways of measuring success. Instead of counting models deployed or dashboards delivered, leading retailers now track how AI changes decisions: faster responses to trends, fewer markdowns, higher conversion, or lower waste. The metric of value has moved from output to outcome.
The evolution of retail intelligence
Historically, retail has been driven by a cycle of observation and intuition. Merchandisers walked stores, studied patterns, and adjusted strategy based on experience. AI augments that intuition with granular, continuous insight. By integrating data from stores, e-commerce, suppliers, and social channels, retailers gain real-time views of performance and behavior.
Advanced analytics now support decisions at every level:
- Pricing: algorithms identify elasticity by segment and region, adjusting promotions in real time to protect margins.
- Assortment: clustering models define the optimal mix for each store based on local demand, seasonality, and shopper profiles.
- Supply chain: predictive models anticipate disruption and recommend inventory reallocation before shortages occur.
- Customer engagement: natural language models analyze feedback, reviews, and service interactions to detect sentiment and emerging needs.
Together, these capabilities create what Vincent calls “retail intelligence in motion”, a system where every decision feeds back into a continuous learning loop. The more data flows through, the smarter the organization becomes.
From personalization to prediction
Personalization has been the retail promise of AI for years, but true personalization requires depth, not just segmentation. The next stage is moving from predicting the next purchase to anticipating intent, recognizing what customers might need even before they express it.
AI enables this by correlating contextual signals: browsing behavior, store visits, weather, social trends, and product availability. Retailers can then adapt marketing and assortment dynamically, providing timely, relevant offers without overwhelming customers with noise.
Edouard describes this as the transition from reactive to proactive retail. “When AI identifies not only what a customer wants but why they want it, that’s when you move from marketing to relationship,” he says.
This predictive capability extends beyond marketing into inventory and operations. For example:
- Predictive AI can foresee demand surges linked to external factors such as local events or influencer trends, enabling faster stock repositioning.
- Dynamic pricing engines can test elasticity and simulate outcomes before implementation.
- Chatbots and virtual assistants trained on company data can guide customers through complex journeys, from choosing the right product to managing returns.
Each of these applications reinforces the same principle: AI is not just an analytical engine, but a predictive one capable of turning knowledge into anticipation.
Generative AI and new retail agents
The rise of generative AI marks another inflection point. It moves the sector beyond analytical intelligence into creative and conversational intelligence. Today’s retailers are experimenting with generative models to accelerate content creation, enhance sales support, and develop new forms of customer interaction.
Three areas stand out:
- Product and content creation. Generative AI can produce product descriptions, images, and marketing assets aligned with brand tone and SEO best practices, dramatically reducing time-to-market.
- Knowledge management. Internal copilots trained on company knowledge bases help teams find information quickly, whether it’s compliance guidelines, supplier details, or store procedures.
- Customer interaction. Intelligent agents provide personalized advice and after-sales support, blending automation with empathy.
The notion of AI agents is particularly transformative. Unlike traditional chatbots, these agents can reason, act, and learn across systems. A retail AI agent might process a refund, check stock availability, and update CRM records autonomously.
For Vincent, these agents represent “a new generation of automation, one that collaborates with humans rather than simply replacing tasks.” As these systems mature, they could become as essential as ERP or CRM platforms, redefining the digital backbone of retail.
The culture of responsible AI
As adoption accelerates, so do questions of responsibility. Retailers handle sensitive customer information, from purchase histories to location data, making ethical governance essential. Responsible AI in retail revolves around three priorities:
- Transparency: ensuring customers understand when and how AI is used in recommendations or pricing.
- Fairness: avoiding bias in algorithms that could lead to exclusionary pricing or targeting.
- Security: maintaining robust data protection and access controls.
Leading organizations are now extending their compliance models to cover AI governance. Many are applying principles similar to financial “three lines of defense”: developing models, validating them independently, and conducting external reviews.
This discipline is becoming a competitive asset, Edouard remarks: “Trust will be the new currency of AI-driven retail. The brands that manage data responsibly will earn customer loyalty as well as regulatory confidence.”
Regional maturity and innovation
AI adoption in retail varies widely across geographies. North America leads in industrialization, supported by advanced e-commerce ecosystems and strong data partnerships. The United States remains the reference point for experimentation, with retailers integrating AI directly into assortment, logistics, and store operations.
Asia, especially China and South Korea, continues to push boundaries in automation and customer experience. Super-apps and digital payment ecosystems enable vast, real-time datasets that power predictive retail models. Companies like Alibaba and JD.com demonstrate what end-to-end AI integration looks like, from supply chain optimization to livestream commerce.
Europe, by contrast, combines innovation with regulation. Retailers such as Carrefour, Zalando, and Tesco are deploying AI responsibly within ethical frameworks, focusing on transparency and sustainability.
Emerging markets in Latin America, the Middle East, and Africa are adopting AI with a focus on accessibility and inclusion, using predictive analytics to manage pricing volatility and improve access to essential goods.
Despite regional differences, the trajectory is consistent: AI maturity grows where infrastructure, regulation, and vision align.
Measuring transformation
AI in retail is moving from proof-of-concept to proof-of-value. The question is no longer whether AI works, but how to measure its real impact on performance and culture. Retailers now assess AI’s value across three dimensions:
- Financial: gains in margin, conversion rate, and operational efficiency.
- Customer: improvements in satisfaction, loyalty, and engagement.
- Organizational: adoption, collaboration, and the ability to scale innovation.
Some benefits remain qualitative. Vincent observes, “AI changes how people think. It brings a more experimental mindset to retail.” Employees learn to test, iterate, and trust data-driven decisions. The result is a more adaptive culture, capable of responding faster to market change.
Transformation also depends on leadership. Executives who treat AI as a core capability, not a side project, are those achieving sustainable results. The priority is to embed AI in the rhythm of the business everywhere: in forecasting meetings, merchandising plans, and store performance reviews, until it becomes invisible, a natural part of how decisions are made.
From technology to transformation
The phase of innovation for AI in retail has transitioned into one of integration. Leaders are distinguished not by the sophistication of their models but by their ability to align technology with strategy and people.
The next stage will likely be defined by AI agents that collaborate with humans across functions, making decisions in real time while learning from feedback. This evolution will not replace human expertise but amplify it, enabling teams to focus on creativity, empathy, and innovation – the qualities that truly differentiate a brand.
In the end, AI’s greatest contribution to retail may not be automation or prediction, but perspective, offering a new lens through which to see the business: more connected, more intelligent, and more responsive to change.
“The real transformation isn’t technical. It’s about learning to work differently, with data as the common language that connects every part of the organization,” concludes Edouard.
Watch the original interview in French:
