The launch of Google’s new AI shopping tools—including conversational search, agentic checkout and the ability for an AI to call stores for you—marks a turning point. These innovations raise a fundamental question for retailers and brands: What happens when the “customer” is no longer a human browsing or clicking, but an algorithm executing on behalf of a human?
Google expects this new model to simplify shopping at scale, using its Shopping Graph—with more than 50 billion product listings—and its Gemini AI models to power agentic checkout and store-calling.
Yet the transition toward “agentic commerce” is fraught with risk and opportunity. Drawing on their expertise in machine learning, generative AI and enterprise AI applications, the members of Senior Executive AI Think Tank explore this new form of commerce, how this shift could upend traditional consumer relationships and what merchants must do now to stay visible—and profitable.
“Brands without clean data risk becoming invisible commodities in this automated marketplace.”
Why Data, Not Branding, Becomes the New Storefront
Sathish Anumula, Sr. Customer Success Manager and Architect at IBM, argues that agentic commerce “fundamentally changes the economy.” According to him, as AI agents begin to make buying decisions, merchants must transition their strategy.
“The ‘customer’ is effectively becoming an algorithm,” Anumula says, “forcing merchants to shift from courting humans with emotional branding to courting machines with structured data.”
He predicts a shift from keyword- or SEO-centric marketing to what he calls “GEO”—Generative Experience Optimization—where product data, metadata, APIs and machine-readable content become the real determinants of visibility. New business models will also emerge—“from pay-per-click to pay-per-conversion, as agents don’t click ads”—meaning traditional digital-marketing budgets may no longer deliver the same value when traffic is mediated by AI.
“Brands without clean data risk becoming invisible commodities in this automated marketplace,” he warns.
The Perils of “Hallucinating Agents”
Charles Yeomans, CEO and Founder of Atombeam, cautions that current AI agents remain fundamentally statistical pattern-matchers.
“Would you hand your credit card to someone who confidently makes things up and forgets everything about you between conversations?” Yeomans asks. “That’s what AI agents do, because hallucinations apply to them too.”
He argues that the real challenge isn’t AI placing orders—it’s merchants realizing they’re no longer optimizing for human psychology, but for algorithmic behavior.
“The winners will be whoever cracks the code on influencing brittle systems that don’t actually understand what they’re buying,” Yeomans says.
In other words: Marketing, reviews and promotions will shift. SEO becomes “AEO,” and reviews get written for algorithms rather than people. This warns of a critical risk: Over-relying on AI agents before they fully understand user intent or preferences could erode consumer confidence, as a growing body of research supports that consumers demand transparency, trust signals and clarity before they let AI handle sensitive tasks like payments.
“The big shift is that Google is moving from facilitating the sale to participating in it.”
Platforms as Gatekeepers
Jim Liddle, serial entrepreneur and enterprise AI strategist, sees agentic commerce as repositioning platforms from passive enablers to active participants.
“The big shift is that Google is moving from facilitating the sale to participating in it,” he notes.
Because the platform that controls the AI agent essentially controls the ongoing customer relationship, this means merchants are no longer negotiating with consumers—they are negotiating with platforms.
“The merchant becomes almost interchangeable—just whoever the agent selects to fulfill the criteria,” Liddle adds.
That interchangeability means merchants may need to compete more aggressively on price, inventory, fulfillment and reliability to be the default agent pick. Liddle notes that while merchants may be drawn by “measurable increase in trade,” those gains could be undermined if the platform adds fees or slices margins.
The New Fundamentals: Data, Pricing and Trust
Uttam Kumar, Engineering Manager at American Eagle Outfitters, argues that agentic commerce will render the traditional middle-funnel marketing—browsing, discovery, content engagement—largely obsolete.
“The merchant-consumer relationship will become transaction-driven and hyper-efficient, largely bypassing the brand-building middle-funnel,” he says.
Instead, the new anchors of brand discoverability will be flawless product data, competitive pricing, fast fulfillment and algorithmic trustworthiness.
“When the AI agent becomes the primary shopper, the product’s metadata and structured data quality is the new storefront presentation and the price is the new conversation,” Kumar adds.
In practice, this means that merchants must invest heavily in data hygiene, inventory synchronization and fulfillment reliability.
Machine-Readable Transparency Matters
Dileep Rai, Manager of Oracle Cloud Technology at Hachette Book Group (HBG), says that in an agentic-commerce world, the buyer-seller relationship shifts from influence to verification.
“AI agents will compare truth, not claims: real inventory, verified policies, delivery reliability and total cost,” Rai argues.
In other words, merchants’ new competitive edge won’t come from marketing language or storytelling—it will come from clear, machine-readable data and transparency.
He argues merchants must surface structured product data, real-time inventory feeds, fulfillment guarantees, ethics signals and transparent pricing—all in a format the AI agent can understand and verify.
“While consumers move from ‘shopping’ to ‘setting intent,” Rai adds, “merchants will need new incentives, agent-specific pricing, guaranteed fulfillment and API-level trust contracts, to thrive in a world where AI, not humans, makes the first move.”
“As AI agents transact on behalf of users, value is no longer defined by what captures attention but by what aligns with encoded intent.”
From Persuasion to Computation
Aditya Vikram Kashyap, Vice President of Firmwide Innovation at Morgan Stanley, describes agentic commerce as a shift from persuasion to computation.
“As AI agents transact on behalf of users, value is no longer defined by what captures attention but by what aligns with encoded intent,” he says.
Under this model, merchants will compete less on flashy promotions or advertising and more on verifiable, continuously available signals: price integrity, fulfillment reliability, provenance, sustainability and systemic resilience.
“Competitive advantage will not belong to those who speak loudest, but to those whose value cannot be questioned,” he adds.
Kashyap argues platforms will become custodians of intent—designing incentive models that favor merchants with demonstrable reliability and integrity, not just marketing savvy.
AI Intermediaries: Opportunities and New Responsibilities
Roman Vinogradov, VP of Product at Improvado, underscores that agentic commerce transforms the relationship between platforms, merchants and consumers, shifting power toward platforms and opening new paths—but also new responsibilities—for merchants.
“With AI making purchases on behalf of users, the emphasis will move toward trust and seamless experiences,” he says.
He envisions new business models—“subscriptions or loyalty programs that reward both consumers and platforms for engagement”—and also argues that merchants must “invest in data analytics to understand consumer preferences better.”
Embracing this change requires adaptability and proactivity, Vinogradov says. Brands that invest in infrastructure—from data pipelines to integrations with AI platforms—may gain first-mover advantage.
Accountability, Fraud and Risk
Bhubalan Mani, Lead for Supply Chain Technology and Analytics at GARMIN, raises the critical issue of liability in agentic commerce—a dimension often overlooked in hype around convenience and automation.
In his view, when AI agents make purchases autonomously, traditional accountability breaks down. Who is responsible if the product arrives late, is incorrect or fails to meet expectations—the platform, the merchant or the AI agent?
Merchants must also face another reality, he argues: “Fraud systems classify agent traffic as bots, blocking revenue.” Payment networks are already working on authentication protocols that distinguish “trusted agents,” however.
Merchants who build verification standards, dispute frameworks and agent-readable data— essentially a foundation of accountability—will likely gain competitive advantage.
“Success hinges on solving liability before chasing volume,” he adds.
Winning the Algorithmic Mindshare
Chandrakanth Lekkala, Principal Data Engineer at Narwal.ai, believes “this shift threatens traditional merchant strategies like branding, discovery and direct customer relationships.”
He argues that, to succeed in this new world, merchants will need to adopt fresh tactics: “Critical business models include becoming AI-preferred suppliers through structured data optimization, paying for AI recommendation placement, offering exclusive agent-only deals and providing seamless API integrations.”
This could also lead to a rise in subscription models, advertising packages designed for AI agents, and premium merchant positioning for better visibility. Merchants might offer dynamic pricing or real-time inventory triggers to capture automated purchases before other sellers do.
For merchants not ready to adapt, the risk is clear: become invisible in AI shopping ecosystems.
How to Prepare for Agentic Commerce
- Prioritize clean product data. Without detailed metadata and structured feeds, your brand risks invisibility when AI agents make purchases.
- Validate AI decisions with built-in checks. Since agents can “hallucinate,” ensure orders are confirmed against real inventory and user intent before completion.
- Prepare for platform-controlled customer relationships. As AI agents mediate transactions, focus on optimizing for agent selection and managing platform-driven visibility.
- Invest in accurate metadata and fulfillment. With AI agents as primary shoppers, product data quality, pricing and delivery reliability become the new measures of brand trust.
- Make trust machine-readable. Ensure product data, inventory and policies are transparent and structured so AI agents can verify reliability before making purchases.
- Demonstrate verifiable value. Focus on objective signals like pricing accuracy, fulfillment reliability and sustainability, as AI agents reward measurable trust over marketing.
- Build AI-friendly partnerships and data pipelines. Optimize product accessibility and platform integration to improve visibility and engagement with AI agents.
- Establish clear liability and verification standards. Implement agent-readable data, authentication protocols and dispute frameworks to manage risk in autonomous AI transactions.
- Become an AI-preferred merchant. Optimize structured data, offer agent-exclusive deals and ensure seamless API integration to win automated purchases.
Thriving in an AI-Powered Marketplace
Agentic commerce isn’t a futuristic concept—it’s arriving now, and just in time for the 2025 holiday season. As AI agents begin calling stores, tracking prices and completing purchases autonomously, the traditional dynamics between brands, merchants and consumers will shift significantly.
For merchants and brands, success will no longer depend on creative marketing, emotional branding or flashy ads. Instead, success will come to those who treat their data, their operations and their reputations as machine-readable products. In this world, trust, transparency and reliability become the new currency.
As we look ahead, the question is not whether agentic commerce will scale—it’s how quickly merchants and platforms will adapt. Those who embrace the shift now will likely control the digital shelf of the future.
