Amazon and Google Redesign Shopping Around AI Judgment

Let’s start with Amazon. In a recent article published on its site the company outlines how it is applying generative and agentic artificial intelligence (AI) to simplify online shopping by reducing the effort required to find, compare and evaluate products. Amazon frames the challenge as one of scale: With hundreds of millions of items available across dozens of categories, choice can become friction.

To address this, Amazon has expanded AI-driven search and discovery tools that move beyond keyword matching to interpret customer intent, using signals such as reviews, price sensitivity, delivery speed, return rates and prior browsing behavior. The goal, according to the company, is to help customers arrive at confident decisions faster, especially in complex categories where comparisons are time-consuming.

The article also details how these capabilities are being delivered through new, more conversational and agent-like interfaces. Amazon points to tools such as Rufus, its shopping-focused AI assistant, visual search through Amazon Lens, and “Buy for Me,” an agentic service that can complete purchases on customers’ behalf even when products are not sold directly on Amazon. These systems are designed to let customers delegate parts of the shopping journey, from tracking prices and summarizing reviews to reordering items or completing transactions. Amazon emphasizes that these features are tested iteratively and refined based on customer feedback, reflecting a broader strategy of using AI not as a standalone novelty but as infrastructure embedded across the shopping experience, particularly as post-holiday returns and repurchasing decisions accelerate.

“We believe AI is going to change virtually every customer experience we know,” the post reads, “and we will continue to test, learn and develop features in our store that help customers discover and evaluate products, making shopping even more convenient.”

The Google View

Google has also been in front of the Prompt Economy at retail. In a report published by Google Cloud, the company argues that retail is entering a new phase of agentic adoption. Drawing on an interview with Kapil Dabi, Google Cloud’s market lead for retail and consumer industries in the Americas, the article explains agentic AI as technology that can reason, understand context and take action in ways that more closely resemble human decision-making.

For retailers, this shift allows AI to interpret nuanced requests, such as visual style or situational needs, rather than relying on keywords or predefined rules. Google positions this evolution as foundational to improving discovery, personalization and engagement across increasingly complex shopping journeys.

The article emphasizes that the most immediate impact of agentic AI is not just on customers but also on employees and operations. Google highlights examples in which retailers use AI agents to augment store associates, contact-center staff and planners by handling information retrieval, reasoning through options and coordinating across systems. This enables employees to focus on human judgment and relationship-building, while AI manages context, data and execution.

Google also notes that successful adoption depends less on technical capability than on organizational readiness, including process redesign, data foundations and workforce upskilling. Over time, the company expects retailers to deploy multiple coordinated agents that work together behind the scenes, presenting customers with a single, seamless experience that anticipates needs rather than merely responding to them.

Global Reach

It’s important to note that agentic’s hold on retail isn’t limited to the US. In a report published by Tata Consultancy Services, the firm argues that retail is reaching the limits of traditional AI-driven automation and must shift toward agentic AI to remain competitive. The paper frames agentic AI as a structural redesign of retail operations, moving from systems that assist humans to systems that can make decisions and act autonomously. Instead of relying on large, monolithic AI platforms, TCS describes a model built on smaller, specialized AI agents that manage discrete tasks such as pricing, inventory, workforce planning and supplier coordination.

The report emphasizes that the strategic value of agentic AI lies in its ability to handle complexity at scale. TCS highlights use cases such as proactive cart recovery and real-time supply chain disruption management, where autonomous agents detect risk signals, select the appropriate response and refine their strategies over time without manual intervention. The firm stresses that successful adoption requires more than technology investment. Retailers must rebuild their data foundations, redesign workflows and adopt a phased roadmap that begins with AI-assisted decision support and advances toward fully autonomous orchestration.

According to TCS, organizations that treat agentic AI as a foundational capability rather than an incremental upgrade will be better positioned to deliver hyper-personalized experiences while running leaner, more resilient operations.