How AEO Tools Can Help Brands Win in ChatGPT-powered Buying Experiences

From Search Bars to ChatGPT Carts: Why the Shift Matters Now

The retail landscape just witnessed its most significant transformation since the dawn of e-commerce. Walmart announced a partnership with OpenAI that enables customers to complete purchases directly through ChatGPT using Instant Checkout. This isn't just another tech integration; it's the beginning of a fundamental shift in how consumers discover and buy products.

"For many years now, eCommerce shopping experiences have consisted of a bar and a long list of item responses. That is about to change," stated Doug McMillon, President and CEO of Walmart Inc. The integration introduces an "Instant Checkout" feature, letting users browse, plan, and complete purchases within the chat interface itself, transforming traditional product searches into conversational transactions.

This shift carries enormous implications for brands. By 2028, 33% of enterprise software applications will incorporate agentic AI, a substantial increase from less than 1% in 2024. For retailers, this means the traditional search bar and the SEO strategies built around it will no longer be the primary gateway to customers. Instead, brands must optimize for AI-generated answers that synthesize information from multiple sources and present curated recommendations directly to consumers.

The urgency is clear: 75% of retail executives view generative AI as instrumental to their business's revenue growth. Those who fail to adapt risk becoming invisible in this new conversational commerce ecosystem, where AI assistants decide which products to surface based on entirely different signals than traditional search algorithms.

Agentic AI & Conversational Commerce: Market Momentum and Numbers

The rise of agentic AI represents more than incremental innovation; it's a paradigm shift in how businesses operate and interact with customers. Agentic AI introduces systems that can independently initiate actions, make decisions, and execute complex workflows with minimal human intervention. In retail, this means AI that doesn't just respond to queries but actively manages inventory, personalizes shopping experiences, and completes transactions autonomously.

The numbers paint a compelling picture of this transformation. 20% of digital storefront interactions will be conducted by AI agents, fundamentally reshaping the customer journey. Gartner predicts that by 2028, 33% of enterprise software applications will include agentic AI, up from less than 1% in 2024, with at least 15% of day-to-day work decisions being made autonomously through AI agents.

For e-commerce specifically, 15% of daily business decisions will be autonomously handled by Agentic AI systems by 2028, up from less than 1% in 2024. This dramatic acceleration reflects both technological advancement and market demand for more sophisticated, automated commerce solutions.

Walmart's Sparky AI assistant launched in June on the company's mobile app, with plans to automatically reorder household essentials and book services. This demonstrates how agentic capabilities extend beyond simple product recommendations to handle complex, multi-step transactions.

Retailers rolling out AI shopping assistants

The competitive landscape reveals a widespread adoption of AI shopping assistants across major retailers. Amazon's Rufus launched in beta in early 2024 and rolled out to all customers in the Amazon Shopping app by July 2024, with desktop access following in September 2024. The assistant is built on Amazon's product catalog, customer reviews, and community Q&As.

By launch, shoppers had already asked Rufus "tens of millions of questions" on topics ranging from product details to order tracking. The system handles everything from product comparisons to occasion-based shopping recommendations, demonstrating the breadth of conversational commerce applications.

Beyond the retail giants, specialized AI assistants are emerging across verticals. L'Oréal Paris launched Beauty Genius for personalized beauty advice, while Guitar Center introduced Rig Advisor to help customers compare musical gear. This proliferation of AI shopping assistants signals a fundamental shift in retail strategy from passive product listings to active, intelligent shopping companions.

AEO, GEO and Old-School SEO: What Actually Changes in AI Answers

The rapid adoption of generative AI-powered engines like ChatGPT, Perplexity, and Gemini is fundamentally reshaping information retrieval. This shift challenges established SEO practices and necessitates a new paradigm called Generative Engine Optimization (GEO). Unlike traditional SEO's focus on ranking for blue links, GEO ensures content gets chosen and included in AI-generated answers.

Instead of fighting for the #1 link on Google, brands are now vying to be quoted or cited by AI assistants. This fundamental shift means that traditional ranked-based metrics are obsolete, creating an urgent need to understand, measure, and optimize for content influence on synthesized answers.

The distinction matters because AI systems exhibit systematic biases that differ from traditional search engines. Research reveals that AI engines show overwhelming bias towards earned media (third-party, authoritative sources) over brand-owned and social content. This stark contrast to Google's more balanced mix means brands can no longer rely solely on their own website content to reach customers.

Generative Engine Optimization isn't just "SEO with a twist." It's a new frontier that intersects branding, content strategy, technical optimization, and community influence. The practice specifically focuses on influencing how AI-driven systems access, interpret, and include your content in their automatically generated responses.

Core Capabilities Modern AEO Platforms Offer

Modern AEO platforms provide sophisticated capabilities that go far beyond traditional SEO tools. The Hostie AI case study demonstrates the transformative power of these platforms: "Average AI Search Rank: 2.1, an improvement of 19.4 percent, ensuring Hostie consistently appeared in the top three answers." Their mention rate increased by 33.9%. This dramatic improvement came from automated content farms optimized specifically for AEO, targeting brand, competitor, commercial, and problem queries.

The Stripe Optimized Checkout Suite exemplifies how AI personalization drives results. Processing $1.4 trillion in payment volume, Stripe's AI models achieve a 12% revenue increase and 7.4% conversion rate boost when dynamically surfacing relevant payment methods. Their system reduces fraud rates by 30% while maintaining conversion rates, demonstrating how sophisticated AEO platforms balance multiple optimization objectives.

Generative AI makes interactions more conversational, assistive, and agentic. Modern platforms help brands adapt as users expect back-and-forth interactions with agents that act like personal assistants and increasingly act on users' behalf. All disciplines, including B2C and B2B marketing, must adapt to this AI-integrated reality.

Enterprise content management vendors are actively innovating their platforms by infusing them with generative AI. Well-governed content platforms provide a strong foundation to organize and secure content while improving content hygiene essential for maintaining accuracy in AI-generated responses.

IndexNow gives content a faster path to visibility across web and shopping experiences. With a single real-time notification, brands can instantly tell participating engines about changes whether price updates, product restocks, or new launches. By pairing these signals with structured product data, IndexNow helps ensure content reflects the latest reality across every touchpoint.

The Microsoft 365 Copilot integration demonstrates enterprise-scale AI implementation, delivering 122%-408% ROI while saving users 25 hours annually on meeting-related activities. This showcases how AEO platforms can transform internal operations alongside external customer interactions.

Structured data & schema pipelines

Structured data forms the technical foundation that enables AI systems to understand and cite content accurately. One fundamental way to optimize for generative engines is using Schema.org markup in JSON-LD format. This standardized vocabulary helps AI engines parse product information, prices, availability, and reviews with precision.

Entity optimization and knowledge graph signals are crucial for GEO success. AI systems prefer content that can be easily extracted and cited, making proper schema implementation essential for visibility in AI-generated responses.

Measurement dashboards: semantic dominance & mention rate

Modern AEO platforms introduce entirely new success metrics that reflect the reality of AI-driven commerce. Semantic Dominance measures how much your source actually matters to the final answer, moving beyond surface-level attribution to assess substantive semantic impact. This metric answers the vital question of influence rather than mere presence.

The DocuBridge case study illustrates these metrics in action: "Mention Rate: Reached 6.4%, up +1.9% vs. prior period, the highest among tracked competitors." They achieved a 67.7% head-to-head win rate in AI search matchups, meaning DocuBridge was favored in two-thirds of direct comparisons against rivals.

Inside Walmart's Instant Checkout: Playbook for Other Brands

Walmart's ChatGPT integration represents the most ambitious implementation of conversational commerce to date. Using OpenAI's Agentic Commerce Protocol, Walmart's system supports a simplified purchase flow where customers can browse, receive AI-guided recommendations, and complete purchases with one-tap checkout all within the chat interface.

The market response was immediate and significant: Walmart's stock gained nearly 5% following the announcement, signaling investor confidence in the retailer's AI-first vision. This reaction reflects understanding that conversational commerce isn't just an incremental improvement but a fundamental reimagining of online shopping.

The technical framework uses OpenAI's "Instant Checkout" system, developed in collaboration with payments company Stripe. This integration enables Walmart to offer instant checkout for everything from meal ingredients to household items, with the AI handling product selection, basket confirmation, and payment processing seamlessly.

Walmart's existing AI infrastructure provides crucial support for this initiative. The Retina AR platform powers 10 AR experiences that have seen a 10× increase in customer adoption, reduced return rates, and improved conversion rates. Their Wallaby GenAI product features retail-specific large language models designed for customer-facing experiences. Even a single irrelevant payment method can reduce conversion rates by up to 15%, highlighting the importance of AI-driven personalization at every touchpoint.

Implementation Blueprint: Getting AEO-Ready in 90 Days

Business leaders can help their organizations prepare for successful generative AI implementations by developing a thorough planning framework. The key is balancing speed with strategic thinking; moving fast enough to capture opportunity while building sustainable capabilities.

Generative AI tends to favor content that sounds credible and authoritative. Numbers add weight and specificity, making AI models more likely to cite concrete details. This means brands must engineer content for machine readability, dominate earned media to build AI-perceived authority, adopt engine-specific strategies, and overcome inherent "big brand bias" for niche players.

The technical implementation starts with schema markup. In 2025, teams that win treat schema as an entity-centric system: a consistent, linked graph that mirrors real-world relationships behind your product, data, and brand. Google still relies on structured data for understanding and eligibility of many rich results, with JSON-LD the recommended format.

Measurement requires new frameworks. With 60% of Google searches ending without a click in 2024 and traditional search-engine traffic predicted to drop by 25% by 2026, brands need metrics that capture AI influence. Generative engines will influence up to 70% of all queries by the end of 2025, making AEO readiness critical for survival.

The Reframe case study demonstrates rapid implementation success: within three months, they reached 99.5K clicks and achieved a 7.0% mention rate in AI search, leading competitors. "Sentiment Score: 76.0%, showing positive AI response tones when mentioning Reframe," while their 68.3% head-to-head win rate proves dominance in comparison queries.

Governance, Bias & Compliance: Pitfalls to Avoid

Gartner reports agentic AI is transforming business decision-making, but this transformation necessitates careful risk management in data quality, governance, and employee integration. The technology's power to act autonomously creates new vulnerabilities that brands must address proactively.

"The hype around generative AI might be pressuring organizations to say that they're ready when they are not," warned Leor Bachar from Deloitte Digital. This pressure can lead to premature deployments that damage brand reputation or create compliance issues. Organizations must resist the temptation to rush implementation without proper safeguards.

For Agentic AI to be used effectively and responsibly, robust guidelines and control mechanisms must be implemented. This includes establishing clear decision boundaries for AI agents, implementing audit trails for autonomous actions, and maintaining human oversight for high-stakes decisions. The goal isn't to constrain AI capabilities but to ensure they align with business objectives and ethical standards.

Key Takeaways for AEO Success

The transformation from traditional search to AI-powered commerce is accelerating rapidly. "Average AI Search Rank: 2.1, an improvement of 19.4 percent, ensuring Hostie consistently appeared in the top three answers," demonstrates the tangible results achievable through proper AEO implementation. Brands that embrace this shift now will capture disproportionate value as conversational commerce becomes the default shopping experience.

The Reframe case study reinforces this potential: "Sentiment Score: 76.0%, showing positive AI response tones when mentioning Reframe." This positive sentiment translates directly into purchase consideration, as AI assistants naturally recommend brands they can describe favorably based on available data.

The path forward is clear: brands must move beyond traditional SEO thinking to embrace the reality of AI-mediated commerce. This means investing in structured data, creating content optimized for machine comprehension, building authority through earned media, and implementing measurement frameworks that capture semantic influence rather than simple rankings.

For brands ready to lead in this new era, platforms like Relixir offer comprehensive AEO solutions that automate the discovery, creation, and optimization of AI-ready content. As the Walmart-ChatGPT partnership demonstrates, the future of commerce is conversational and brands that optimize for AI visibility today will dominate the shopping experiences of tomorrow.

Frequently Asked Questions

What did Walmart’s ChatGPT partnership introduce for shoppers?

Walmart announced a partnership with OpenAI that enables customers to complete purchases directly through ChatGPT using Instant Checkout. The integration introduces an "Instant Checkout" feature, letting users browse, plan, and complete purchases within the chat interface itself, transforming traditional product searches into conversational transactions. This isn't just another tech integration; it's the beginning of a fundamental shift in how consumers discover and buy products.

Why is the shift to AI-powered buying urgent for brands?

The urgency is clear: 75% of retail executives view generative AI as instrumental to their business's revenue growth. Those who fail to adapt risk becoming invisible in this new conversational commerce ecosystem, where AI assistants decide which products to surface based on entirely different signals than traditional search algorithms.

How will agentic AI change retail operations by 2028?

Gartner predicts that by 2028, 33% of enterprise software applications will include agentic AI, up from less than 1% in 2024, with at least 15% of day-to-day work decisions being made autonomously through AI agents. For e-commerce specifically, 15% of daily business decisions will be autonomously handled by Agentic AI systems by 2028, up from less than 1% in 2024. 20% of digital storefront interactions will be conducted by AI agents, fundamentally reshaping the customer journey.

What changes from SEO to GEO should brands expect in AI answers?

The rapid adoption of generative AI-powered engines like ChatGPT, Perplexity, and Gemini is fundamentally reshaping information retrieval. This shift challenges established SEO practices and necessitates a new paradigm called Generative Engine Optimization (GEO). Unlike traditional SEO's focus on ranking for blue links, GEO ensures content gets chosen and included in AI-generated answers. Instead of fighting for the #1 link on Google, brands are now vying to be quoted or cited by AI assistants.

Which data and metrics matter most for AEO visibility?

One fundamental way to optimize for generative engines is using Schema.org markup in JSON-LD format. Entity optimization and knowledge graph signals are crucial for GEO success. Semantic Dominance measures how much your source actually matters to the final answer, moving beyond surface-level attribution to assess substantive semantic impact.

How does Relixir help brands compete in AI-mediated commerce?

For brands ready to lead in this new era, platforms like Relixir offer comprehensive AEO solutions that automate the discovery, creation, and optimization of AI-ready content. The path forward is clear: brands must move beyond traditional SEO thinking to embrace the reality of AI-mediated commerce. This means investing in structured data, creating content optimized for machine comprehension, building authority through earned media, and implementing measurement frameworks that capture semantic influence rather than simple rankings.