How is AI Changing Retail Strategy Now?

Artificial intelligence in retail, especially agentic AI, first seemed to promise a leap forward in customer experience. In 2026, though, AI is likely to have the most influence on customer behavior. We’re already seeing behavior shift in ways that are pushing retailers to rethink their discovery, partnership, and channel strategies. In this article, we’ll look at several aspects of retail that we expect AI to reshape this year and explore how retailers can adapt their strategies to leverage these trends.

From Search Economy to Answer Economy

Consumers are getting more comfortable using different kinds of AI tools to find answers or shop online. They don’t feel a loss in combing through a page of search results to find the information and products they’re looking for. This convenience and a sense that these tools deliver greater levels of personalization have led to a 693 percent increase, at the end of 2025, in traffic from generative AI tools to retail sites.

This transition is moving consumers and retailers past the search economy and into the answer economy. That shifts the balance of influence from factors like search ranking and popularity with influencers to how readily accessible and formatted information is for AI agents. These new AI agent shoppers are being granted the power to decide what information is relevant to a human user by recommending products, and even directly making purchases.

Discovery Gets Disrupted

As AI agents present shoppers with short, curated answer sets, discovery narrows. Unless retailers actively shape how their products are represented to AI systems, many brands will simply disappear from consideration. It will be critical for brands to tailor their content to formats that are easy for AI agents to quickly parse, such as command-line interfaces (CLI); provide meaningful answers in themes important to a brand to increase generative engine optimization (GEO); leverage agentic shopping tools built by AI model companies; and partner with an AI platform for content discovery.

Even retailers without a formal LLM partnership are already being crawled and documented by AI search bots. In these cases, the LLMs’ shopping and search features compile product data without paying ad or referral fees. All these use cases are reshaping discovery by training consumers to buy from curated lists in response to their questions. That poses a risk for retailers. For example, a shopper who asks a LLM for mascara and gets three options may never encounter any other brands unless the LLM is trained and prompted to suggest other alternative brands.

GEO Redefines Brand Visibility

With AI playing a growing role in product selection, retailers and brands need to be willing to put in the work to help LLMs recommend them and their products. GEO, also sometimes referred to as AEO (answer engine optimization), evolves brand visibility beyond SEO keywords into key content, social proof and brand authority, structured for recognition by AI agents.

As AI agents shift from presenting options to delivering single, synthesized answers, brand visibility is no longer about ranking, it’s about being selected. Rather than focus so heavily on the search channel, retailers may need to invest more resources in earning media mentions, generating press coverage, and highlighting feedback from existing customers and credible influencers on review sites and social media. A strong GEO strategy builds a coherent semantic footprint across topics, signals, and proof points so AI models recognize a brand as a trusted authority and the best product offering. In an AI-mediated market, the brands that actively shape how they're understood by models are the ones shoppers see.

Frontline and Operations Get New Customer Experience Capabilities

Innovative retailers are starting to remake the in-store experience with AI, too. AI agents aren’t just reshaping work for people who sit at desks; they're also reshaping in-store work by turning frontline roles into AI-augmented service positions where employees can access expertise, training, and operational intelligence in real time from a headset while on the move navigating store aisles, checkout counters and more.

The frontline experience is being augmented by employees with real-time intelligence at the point of service. Frontline workers can partner with AI agents that draw on store and operational data to anticipate customer needs, prevent service failures, and deliver accurate answers instantly. Through headsets or mobile devices, employees can access product knowledge, personalized guidance, and on-the-job coaching, which helps accelerate new employee ramp-up, improves consistency of tasks, and helps more staff perform at a top-tier level during live customer interactions. For example, a new barista in a busy quick-service restaurant can ask the agents in their headset for real-time answers to product questions and get proactive support to get up to speed quickly and never miss a beat.

Retail Strategy Adaptations for 2026

Based on these trends, retailers should consider following new strategies for how to market to humans and AI agents.

Market to humans and AI agents.

As AI’s role in e-commerce expands, retailers need to design their content and websites to stay visible to humans and AI agents. This requires intentional choices about how brands balance traditional demand generation with AI-mediated discovery. A strong GEO strategy increases visibility in AI search summaries and chatbot product recommendations, using the earned media and customer feedback strategies mentioned above.

It also requires understanding how the major LLMs assess authority and summarize results. We already see early adopter luxury clients working to understand how they appear in summaries by different LLMs so they can move toward optimal visibility. At the same time, brands need to maintain their existing practices for marketing to humans.

Optimize partnerships, platforms, and channels.

As part of the strategy shift toward blending SEO and GEO, retailers should create a feed of rich, context-aware data that’s ready to serve. From there, leaders face a strategic tradeoff: distribute broadly to shape AI-driven discovery or concentrate data within owned channels to retain control over customer relationships. Each path carries implications for brand control, platform dependence, and long-term differentiation, making data access and partnership strategy a core executive decision.

We've seen major entertainment and retail brand license characters on an AI video platform, which will raise visibility and create a new revenue stream. Another major retailer is taking a different approach by building a moat to prevent third-party AI agents from mapping its sites in favor of controlling its narrative by rolling out its own AI product comparison and recommendation tools.

Leveraging AI’s Influence for Better Retail

With AI now so popular with a growing number of consumers and because it’s being embedded in so many channels and platforms, the technology is functioning like an influencer in key ways. Many consumers turn to AI first for suggestions and they trust its recommendations. As a result, influencing AI through strategic GEO, partnerships, or proprietary systems has become a primary lever for retailers to maintain and grow their visibility with consumers and their AI agents. That means finding ways to influence AI through direct partnerships, strategic GEO, or both, is now inseparable from how demand is created and captured.

Jess Leitch is head of frog North America, part of Capgemini Invent, leading teams in the design and launch of new products, services and businesses. Mike Haddon is vice president of consumer products, Retail & Services Practice Lead, Capgemini Invent. He helps Fortune 500 companies achieve profitable growth by aligning strategic engagements across operations, marketing, supply chain, and commerce.