EXCLUSIVE Q&A: Capgemini – the future of retail AI is here
Artificial intelligence is already creating a paradigm shift in retail, and the preparing for the future means adapting now.
Mark Ruston, global retail lead at Capgemini, recently sat with Chain Store Age to dive into the rapid acceleration of next-gen AI in retail and why getting ready for the future means transforming your enterprise now.
How are retailers using AI to optimize forecasting and fulfillment?
Retailers have been using AI and machine learning algorithms to improve demand forecasting for several years. What we are now beginning to see, however, is a shift beyond AI‑assisted forecasting toward AI‑driven supply‑chain decision systems.
Agentic AI is a big part of this shift. Instead of generating a static forecast, these systems continuously test scenarios across demand, inventory, pricing, and fulfillment. This helps retailers better balance the core priorities of any supply chain - service level, cycle time, resilience, and cost, in real time and under constantly changing market conditions.
In fulfillment, investment is also accelerating in physical AI. The deployment of autonomous mobile robots, robotic picking systems, computer‑vision cameras, IoT sensors, and edge devices, all orchestrated by AI platforms and digital twin environments, is delivering measurable gains in fulfillment efficiency, accuracy, and throughput inside warehouses and stores.
This evolution is not about replacing people from the supply chain. The most effective organizations are designing for human–AI chemistry, where AI systems augment human judgment rather than replace it.
Agentic and physical AI excel at sensing, simulating, and optimizing across vast decision spaces at machine speed, while humans remain essential for setting intent, managing exceptions, applying contextual judgment, and making trade‑offs that reflect brand, customer, and risk considerations.
In practice, the winners will be those retailers that design new operating models where planners, operators, and frontline teams work in continuous partnership with AI, combining machine intelligence with human experience to drive better, faster, and more resilient decisions.
How can AI help retailers provide a seamless customer experience behind the scenes?
AI gives retailers powerful new ways to improve customer experience. However, with great power comes great responsibility. The best experiences are the ones where AI is quietly reducing friction, anticipating needs, and connecting journeys in ways that feel natural rather than intrusive.
When used well, AI can orchestrate experiences across channels, linking online and in‑store interactions, maintaining consistent pricing and availability, resolving issues, and personalizing services without the customer having to repeat themselves. When used poorly, it can result in experiences that break continuity and frustrate customers.
That means retailers need to avoid AI ‘doom loops,’ where customers get stuck in automated journeys with no clear way to reach a person. Seamless customer experience depends on AI knowing when to assist and when to step aside.
Trust is the final and decisive factor. Our research shows consumers are deeply concerned about how AI uses their personal data. Seventy-one percent of consumers are concerned about how generative AI tools use their data, and 76% want clear rules on when and how AI acts on their behalf.
At the same time, roughly two‑thirds of consumers are open to providing data when clear value, safeguards, and transparency are in place.
What can retailers do to stay relevant as consumers increasingly use shopping bots?
Our research finds that almost six in 10 consumers now use generative AI tools instead of traditional search for product recommendations, up sharply from prior years.
[READ MORE: Study: Consumers using generative AI for search help, recommendations]
Retailers need to prepare now for a world where AI agents increasingly shape discovery, comparison, and purchasing decisions. Shopping agents behave very differently from human shoppers. They are needs‑driven and rational, optimizing for availability, price, delivery speed, reliability, and service, not brand storytelling or marketing influence.
As a result, loyalty shifts from superior marketing to superior products and customer service. Retailers that win will be those with strong operational truth: accurate inventory, dependable fulfillment, transparent pricing, and frictionless returns.
To compete in this environment, retailers must move beyond traditional SEO and invest in Generative Engine Optimization (GEO), optimizing for how AI agents search, evaluate, and decide. That means exposing high‑quality, machine‑readable data such as real‑time availability, delivery service level agreements, pricing, returns policies, and sustainability attributes.
The next major unlock will be agent‑to‑agent commerce, where consumer agents negotiate directly with retailer agents. Emerging standards are pointing toward a future of automated discovery, negotiation, and checkout.
What are the biggest retail AI trends for the next six to 12 months?
Today, most consumers use AI primarily for discovery and research, with purchases still completed through traditional digital channels. Over the next six to 12 months, that model will begin to shift toward more end‑to‑end, agent‑driven journeys, where discovery, comparison, and purchase are increasingly orchestrated by AI agents on the consumer’s behalf.
This marks the transition from AI‑assisted shopping to agentic commerce, where agents act autonomously to optimize for price, availability, delivery speed, and service outcomes. While adoption may look incremental today, this is a classic platform inflection point and once trust, payments, and standards converge, uptake will accelerate quickly.
Second, the majority of retail AI initiatives have focused on efficiency and cost take‑out, improving labor productivity, forecasting, loss prevention, and automation. Retailers are now recognizing that AI also represents a significant revenue growth lever.
As a result, we will see a rebalancing of AI portfolios across Revenue Growth Management (RGM), frontline AI, and customer experience. Frontline AI is a good example. Imagine AI copilots that make every store associate knowledgeable and effective. That has a double impact: lower operating costs and increased productivity, alongside higher in-store conversion, basket size, and customer satisfaction. The same shift is emerging in pricing, promotion effectiveness, and personalized service at scale.
The third major trend is the acceleration of physical AI, which is the fusion of AI models with the physical world across manufacturing, logistics, and fulfillment.
Over the next year, retailers will see increased deployment of autonomous mobile robots and robotic picking systems in distribution centers; computer vision and edge AI for inventory accuracy, shrink detection, and quality control; AI‑driven yard, labor, and slotting optimization in warehouses; and digital twins to simulate fulfillment, capacity, and disruption scenarios in real time.
