How AI is reshaping holiday shopping in 2026
Capgemini ·
Explore how AI is changing holiday shopping in 2026, from product discovery and gift recommendations to demand forecasting, fulfillment and returns. The post How AI is reshaping holiday shopping in 2026 appeared first on Capgemini .
Most retailers follow a familiar holiday playbook. In 2026, with AI playing a meaningful role on both sides of the shopping equation for the first time, they may need to rewrite it, especially since consumers seem to be moving faster than retailers.
New research reveals that 65% of consumers plan to use AI for at least one part of their holiday shopping in 2026, but only 8% of retailers say they’re ready to deliver.
Retailers need to master both sides of the equation: shaping demand and responding to it. The perfect recommendation means little if the product is unavailable or fulfillment is too slow. At the same time, even the best-run operation cannot capture a sale if the brand never enters the shopper’s consideration set.
Connecting those two sides is where the opportunity lies — and this year, the stakes are particularly high. As consumers rely more heavily on AI, the habits, brand preferences and expectations established now could shape how consumers, and the AI assistants acting on their behalf, shop for years to come.
“What should I buy Dad for Christmas?” is increasingly a question for an AI assistant rather than a sibling. With a bit of back-and-forth, that shopping assistant can often deliver a better answer by evaluating a wider set of options, cross-referencing sources such as consumer reviews and expert roundups, and applying personal preferences and constraints. AI shopping assistants can help consumers discover gift ideas, compare products, and identify the best-fit option based on factors such as the recipient, occasion, budget, brand preferences, size, and availability.
Using AI in this way changes what it means for brands to be discoverable. Recent research shows that a full 74% of shoppers use AI for product discovery, but less than 3% of the citations used by LLMs were brand-owned pages.
For brands and retailers, this shift raises the stakes for how they show up in the digital ecosystem beyond their own website. It also opens the door for smaller, less familiar brands with a strong digital presence and greater visibility in AI recommendations.
While optimizing an owned website for AI product discovery remains a necessary first step, it is no longer sufficient. Winning in AI discovery requires brands to build a consistent and credible presence across the broader digital ecosystem, ensuring AI systems can find, interpret, and trust information from sources such as publishers, buying guides, consumer reviews, and online communities. This broader approach, often referred to as generative engine optimization (GEO), focuses on improving the clarity, consistency, and credibility of the information AI systems use when generating recommendations.
The practical starting point is to map the AI discovery journey just as brands once mapped the search journey. Identify the questions consumers are likely to ask, test whether the brand appears for different needs and personas, examine which competitors are being recommended instead, and trace the sources shaping those answers. Then brands can work to close the gaps, strengthening product information on their own sites while earning credible reviews, editorial coverage, and authentic conversation in the third-party environments AI relies upon.
When it comes to gift-giving, we often say, “It’s the thought that counts.” But as shoppers increasingly turn to AI for help, it’s the feeling behind the thought that may be tougher to replicate.
Designed to weigh objective criteria like price, quality, and availability to find the “best” choice, AI models often fall short when it comes to the symbolism, and connection that accompany a gift. For example, Lipton tea may be the best everyday choice when Mom goes to the grocery store, but perhaps not the ideal gift for her. A highly rated digital kitchen scale may be a lovely gift for an avid baker, but come off as an inadvertent swipe for someone on a diet.
For brands, this puts a premium on two things AI cannot determine from product specifications alone: shopper intent and brand identity. Intention means giving AI enough context to understand not just what a shopper is looking for, but who they are buying for and why. Brand identity provides the emotional layer, the associations, values and meaning that make one choice feel more personal or appropriate than another.
Brands with a strong identity have an opportunity to not only land on the short list of AI recommendations, but also “win the override” when they don’t. Unlike traditional measures of satisfaction or loyalty, the Conscious Override Rate (COR) measures how often shoppers deliberately choose a preferred brand over an AI’s top recommendation, reflecting the strength of brand loyalty and preference beyond algorithmic influence.
That makes this holiday season particularly consequential. As shoppers use AI for product discovery at meaningful scale for the first time, brands are competing not simply for this year’s purchase, but also influencing long-term preference.
AI can help retailers create a better path from discovery to purchase, but winning the recommendation is only half the battle. A highly relevant recommendation means little if the product is unavailable, the delivery promise is unreliable or unexpected fulfillment costs appear at checkout.
AI can play an important role in removing these points of friction. But while consumers are willing to use AI, they aren’t necessarily looking for an “AI experience.” They want AI to be felt, not seen—working behind the scenes to make shopping easier and more relevant.
That starts before the purchase. AI-powered demand planning can help anticipate not only what consumers will buy, but where and when demand will materialize. The bigger opportunity is to connect those signals of customer intent directly to operational decisions—from forecasting and allocation to replenishment and last-mile capacity.
Fulfillment then turns inventory into experience. Once a shopper is ready to buy, AI helps retailers determine which location should fulfill the order, through which channel and at what speed and cost. That requires connecting inventory visibility with warehouse, store, transportation and last-mile capacity through an intelligent control tower.
And the journey does not end at the doorstep. Holiday shopping inevitably becomes holiday returns, making reverse logistics another critical capability. Retailers can leverage AI to coordinate returns or troubleshoot questions; on the operation side, intelligent solutions can also be used to automate tasks like inspecting merchandise and restocking.
This holiday season, AI may be rewriting the playbook. But winning still comes down to one equation: create the demand, then be ready to deliver.
--> Report State of Grocery Retail 2026 Eight trends reshaping the category Download now --> Latest insights Agentic commerce in consumer products and retail Capgemini Research Institute Reimagining customer experience: Human-led, AI-powered Report What matters to today's consumer 2026 Point of view Retail’s next evolution: Preparing for agentic customer experience Read all stories Author Mark Ruston VP, Global Retail Lead, Capgemini Mark Ruston is Capgemini’s Global Retail Lead with 22+ years in consulting and transformation. He helps Tier 1 retailers and CPGs bridge strategy and execution, driving growth and measurable outcomes. With global experience and deep supply chain expertise, Mark champions AI to boost productivity and reduce waste — positioning operations as a key driver of consumer experience.
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