AI in Shopping: Implications and a Value-Creating Roadmap for Retailers

The impact of AI-enabled commerce on retailers and the foundational steps industry leaders are taking now to gain share amidst disruption

This post — the second in a series of three — builds on previous predictions for how consumer shopping behavior is likely to evolve as AI’s role expands across the path to purchase.

Even a passing familiarity with the spate of announcements in recent months makes clear that while the AI-in-shopping foundation remains distinctly unsettled, the longer-term direction of travel is clear. The proverbial toothpaste is out of the tube and AI’s role in consumer shopping will only increase. But just as the ecosystem shifts are multifaceted and dynamic, so too are the strategic, technical, competitive and operational considerations for retailers.

In boardrooms across the globe, executives continue to debate the presumed pace of change and evaluate the likely impact on their business. While pragmatism is warranted, inaction is not a viable strategy and a series of ‘no-regret’ moves should be taken now. 

This post highlights key implications for retailers and outlines an ‘acceleration playbook’ designed to generate valuable in-market learnings, drive competitive advantage, and guide execution roadmaps.

So what does this all mean…

Foundational transformations in consumer shopping behavior and near-total realignments of commerce channels are exceedingly rare. When this level of change does take place, however, the competitive implications are profound. Making the wrong strategic pivot or delaying action — often justified by flippant dismissals like, “It’s way too soon and the technology is overhyped” — can lead to an erosion in market share, a loss in brand affinity with consumers, and a backlog of technical and operational initiatives that can take years to address.

While by no means exhaustive, the following framework captures the key considerations that should be driving retailer roadmaps throughout 2026:

Evolving Strategy: While some retailers like Amazon are initially blocking shopping agents, that strategy is not a viable one for most other companies. 

Neither neutral disengagement nor outright aversion to AI-enabled shopping is a productive strategy. Retailers should view this as a unique moment in time when market share is up for grabs and established shopping behaviors are becoming fluid. 

Practically, this means developing a holistic strategy that builds on unique sources of durable competitive advantage and prioritizing interoperability with complementary and non-competitive players across the ecosystem.

Competing Models: Despite the most fantastical predictions, legacy shopping behaviors and the retailer infrastructure servicing those purchases will not be made obsolete by AI overnight.

The murky evolution to come, however, leaves retailers with a conundrum: how to lean in heavily to the AI ‘future’ while continuing to support and invest in the non-AI ‘present’. While the specifics will vary by retailer, the solution is largely the same. Place strategic bets on AI capabilities that drive competitive advantage and serve the bleeding edge of consumers while avoiding a degradation of relevance for the majority of consumer shopping journeys that will stubbornly resist disruption. 

Rerouted Journey: As highlighted in the first blog post in this series, AI tools will have a profound impact on the consumer path to purchase. 

Research and discovery for higher-consideration purchases will rapidly gravitate towards powerful AI solutions while a growing subset of transactions will shift from retailer-owned channels to AI offerings (even where the retailer remains the merchant of record). These changes will force retailers to reassess not just their marketing activities but also execution across physical stores and owned website/app properties. 

Shopping journeys will often start with an AI prompt around a ‘job to be done’ instead of consumers defaulting to ingrained preferences for specific brands or retailers. That will push retailers to refocus on ‘indirect’ means of influencing purchase decisions and confront a potentially sizable drop in website traffic and store visits. Worryingly, this loss in traffic would directly degrade the value proposition of a retailer’s media network and put at risk the accompanying high-margin revenue dollars.

Growing Disintermediation: It is hard to imagine AI not substantially reducing the level of influence retailers have over consumer shopping behavior based on the early impact of AI. 

We are already seeing significant research and discovery activity shift to chatbots which is rapidly decreasing retailer relevance in the early stages of the path to purchase and moving them further away from the consumer. The tangible impact is a reduced ability to steer consumer behavior and a marked loss of signal-rich behavioral data. As transactions migrate initially to ‘Buy Now’ tools in AI chatbots and eventually toward true agentic offerings, retailers will need to confront an uncomfortable reality where commoditization is a genuine risk as they evolve into a commerce ‘utility’ operating in the background. 

To combat this, retailers should double down on a strong brand with a unique in-store and ecommerce value proposition while investing in owned AI tools — like Amazon Rufus or Walmart Sparky — that can rival those offered by AI platforms.

Reshuffled Competition: When a consumer engages AI on a shopping-related mission, the subsequent decisioning that leads to a personalized brand/product/retailer recommendation will be markedly different from how consumers historically approached this task. 

Whereas humans are prone to make decisions based on emotion and ingrained preference, AI dispassionately draws on extensive data and advanced tooling to return an ‘optimal’ answer unconstrained by these uniquely human considerations. The output, not surprisingly, will often elevate options that would previously have been outside of a consumer’s consideration set but score highly based on obscure metadata and supporting information from across the web. This massively disrupts the competitive status quo and may lead to a commoditization of retail with weaker retailers morphing into ‘dumb fulfillment pipes’. 

To combat this, retailers need to reposition their offering and value proposition for an AI era. In so doing, they can continue driving direct store and website traffic, win the ‘AI buy box’ to secure an outsized share of chatbot recommendations, and establish a strong position for the coming agentic wave. A retailer that maintains a strong brand, attracts site visitors with AI shopping tools that rival those of third party platforms, exposes robust metadata to AI agents, and optimizes its business to favorably influence AI decisioning will continue to thrive in the coming years. 

But importantly, success will not be determined solely by relative advantage against incumbents in that modernization race. Just as ‘ghost kitchens’ popped up to service consumer demand flowing through food delivery apps, the retail space may see new ‘brand-lite’ retailers emerge. These players would offer sharp pricing, strong inventory availability, robust metadata, seamless connectivity with AI tools, fast fulfillment and consumer-friendly returns policies. 

Stepping back, it is also not unreasonable to hypothesize that the rise of AI could dull the edges that currently distinguish one retailer from another. Retailers competing for transactions flowing through AI will share a similar understanding of the variables — like pricing, inventory availability, fulfillment speed, product metadata, and consumer satisfaction — driving success. Retailers will then aggressively invest in closing any competitive gaps, ultimately leading to a convergence that eliminates major sources of differentiation.

Muddled Operations: As consumer behavior shifts and the path to purchase increasingly flows through AI tools, retailers will be confronted with rising complexity across three core operational areas: inventory and fulfillment; customer service; and returns.

For shopping missions that lean heavily on AI for research and discovery, transactions will continue to migrate from physical stores to ecommerce channels. That channel realignment will require a reallocation of inventory across a retailer’s network and a scaling up of fulfillment capacity to serve increased ecommerce shipment volumes. To meet sky-high consumer expectations and retain the trust of AI shopping agents, retailer in-stock data will need to be consistently accurate and updated in real-time across a range of channels and partners. 

And while the hope is that AI proves adept at guiding consumers to the right product choice, there will likely be a spike in issues that require the involvement of customer service. Department staffing and SOPs will need to be enhanced to deal with the volume and complexity of these inquiries. If the volume of product returns stemming from those CS interactions is sufficiently high, increased costs will have a material impact on retailer profitability, returns processing workloads can sap team bandwidth if not properly automated, and reverse logistics needs have the potential to snarl company operations. 

…and what should retailers do now to capitalize on this disruption?

Projections and hypotheses can be helpful in steering strategy but at some point leaders need to make a call and aggressively shift their organization into ‘build’ mode.

Outlined below is a playbook comprised of five complementary initiatives that can serve as a ‘quick start guide’ for retailers looking to accelerate cross-functional efforts to respond to the rise of AI in shopping:

Product Metadata (Product 360)

AI shopping tools rely on access to robust data to support decisioning and complete a user’s request. In the context of shopping-related queries, the data most central to that process is product metadata, including title, description, product specs, customer reviews, images, videos, pricing, features/benefits, brand information and product availability. 

OpenAI specifically highlights on their website that when considering which products to surface, ChatGPT considers “structured metadata from first-party and third-party providers (e.g., price, product description) and other third-party content”.

Similarly, Perplexity emphasizes that “[m]erchants who provide deeper product details such as availability, reviews, pricing, and specifications are more likely to be recommended by our answer engine”.

This emphasizes the importance of retailers making clean and updated product metadata for their full catalog available at all times in a machine-readable format.

Ecosystem Connective Tissue

To succeed as AI reshapes the commerce landscape, retailers must resist the urge to ‘go it alone’. Seamlessly integrating with AI offerings — chatbots initially and agents longer-term — and complementary ecosystem players will become table stakes as consumer paths to purchase and industry operating models adjust to this powerful new technology.

Enabling this in a balanced, pragmatic way requires a phased execution roadmap:

1. Make your first-party website ‘AI friendly’ with guidance on what to crawl, clean HTML that is easy for an agent to parse, extensive structured data, high levels of site performance and reliability, and read-only APIs for efficient access to priority data.

2. Secure commerce partnerships with the top AI platforms to natively integrate commerce functionality into their consumer-facing chat interfaces. Prioritize simple, single-item ‘Buy Now’ transactions before expanding to multi-item carts and loyalty program integrations.

3. Stand up an MCP server that standardizes agent access to retailer data assets and tools.

4. Explore partnerships with non-competitive players that fill retailer gaps in assortment or geographic coverage (similar to Amazon’s ‘Available from the Web’ feature within the Rufus chatbot).

5. Once momentum builds behind agentic commerce and consumers show signs of interest, build agent-to-agent capabilities with tools like Google’s Agent2Agent protocol (or other protocols that emerge over time).

Details on Snowflake’s managed MCP server are available here.

AI Parity in First-Party Site and App

Retailers should not be intimidated to aggressively lean into this AI wave and invest in offerings that enhance the consumer appeal of their first-party website and mobile app. Not doing so would effectively cede competitive advantage to third-party AI platforms and risk accelerating the commoditization highlighted earlier in this post. 

The most likely end-state is a consistent presence of chatbot-style AI shopping tools available directly through retailer sites and mobile apps. Importantly, this aligns with consumer preference as we see significantly greater trust in on-site retailer agents versus those from unaffiliated third-parties. As traction builds over time with owned AI tools, retailers can monetize that chatbot traffic in an effort to counteract the associated decline in existing retail media network revenue.

We know empirically that, done well, these investments drive strong in-market results. Consumers that engage with Mylow, a Lowe’s shopping assistant powered by ChatGPT, have conversion rates double that of unexposed online shoppers.

In the interim, retailers need to pull other levers to maximize the attractiveness of shopping directly to maintain commerce gravity with consumers. Offering exclusive products, aggressive promotions, and unique loyalty perks can outweigh the draw of third-party shopping agents.

Optimization for AI Decision Criteria

The precise factors underlying each platform’s product recommendation engine and merchant selection algorithm are not publicly disclosed and remain fluid. The platforms themselves do, however, share some high-level details that can start to paint a picture of the critical variables driving these decisions. 

Perplexity highlights that product listings are ranked based on a combination of authority and relevance, while OpenAI offers more detail and specifically mentions price, reviews and ease of use. In both cases, the query itself along with known and inferred context about the user play critical roles. 

Once a product option is identified — either because it was recommended by the chatbot or it was explicitly passed by the user in the prompt — the mandate for retailers shifts to securing the top spot in the platform’s merchant selection ranking. Although the precise factors and weightings in the merchant selection logic are unknown, OpenAI does disclose that merchants “are ranked based on factors like availability, price, quality, whether they are the maker or primary seller of that item, and whether Instant Checkout is enabled.” We can also assume that other CX drivers like fulfillment speed, return policy and customer support responsiveness are also factored in. 

This has echoes of the decisioning — based on price, seller performance, and fulfillment speed and reliability — that drives selection of the default merchant for Amazon shoppers. Known as the Featured Offer, or more commonly ‘winning the buy box’, this is the seller that will fulfill a customer’s Buy Now order.

So for a retailer, the path to ‘winning’ in the AI era relies on an authoritative assortment (which maximizes the surface area of products that could meet a given consumer need), a breadth of machine-readable metadata, sharp pricing, consumer-friendly fulfillment and returns options, and credibility-building content from consumers and experts alike.

Yet today it remains difficult to access reliable and actionable data illuminating success with generative AI technologies. Because the platforms provide very little in the way of analytics, many turn to third-party ‘AI visibility’ tools that provide visibility into metrics like brand mentions, average ranking position and share of voice. 

Guided by this understanding of performance, retailers can implement a recurring cycle of root cause analysis, roadmap development, scoping and execution to optimize their end-to-end business and maximize commercial impact of consumer engagement with AI shopping tools.

Rewired Organization

While an exhaustive review of organizational transformation in the context of AI is well outside the scope of this content, it would be a mistake to gloss over the foundational shifts that must happen within most companies to properly capitalize on the expanding role of AI in commerce.

Transformation experts consistently highlight the outsized emphasis that must be placed on people, processes and culture relative to more technical domains like data, technology, and modeling. Boston Consulting Group, for example, relies on a 10-20-70 heuristic for resource allocation in digital and AI transformations. In this framework, 70% of transformation effort should focus on the organization (covering activities like process redesign, incentives, skills, culture and governance) with the remaining 30% dedicated to technology and algorithms.

While the ratio will shift based on context and relevant company nuance, the message is clear: over-invest in rewiring the organization and avoid the trap of assuming tech can deliver returns on its own.

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The final post in this series will offer a similar set of resources for consumer goods companies looking to capitalize on the role of AI in shopping. Despite varying implications and fundamental differences in how retailers and consumer goods companies are planning to address this dislocation, common threads do exist: a shared focus on strategic foresight, a willingness to take calculated risks, and a commitment to leverage data and technology to forge the advantaged business model of the future.