AI agents are becoming active participants in online shopping. As intelligent assistants gain the ability to discover products, compare options, and support purchasing decisions, brands need stronger product data, trusted customer signals, and AI-ready digital experiences. Companies that prepare for AI-driven commerce today can improve visibility and strengthen future growth opportunities.
Could the next online purchase happen without a customer ever visiting a product page? The rise of agentic commerce in ecommerce is creating a significant shift in how products are discovered, evaluated, and purchased. AI assistants are moving beyond answering questions and generating content. They are beginning to participate in shopping journeys by helping consumers compare products, assess options, and make recommendations based on individual preferences.
For years, brands focused on search engines, marketplaces, websites, and mobile apps to connect with customers. A new layer is now emerging. AI agents can analyse information across multiple sources and guide consumers toward purchasing decisions. As Google and OpenAI expand their commerce capabilities, brands need to understand how these platforms are shaping the future of retail and what actions will be required to remain visible and competitive.
Understanding the Rise of Agentic Commerce
Agentic commerce refers to shopping experiences where AI systems actively assist consumers throughout the buying process. Instead of simply responding to searches, AI agents can interpret intent, evaluate alternatives, recommend products, and support purchasing decisions.
Traditional e-commerce often requires consumers to conduct extensive research. They compare products, read reviews, visit multiple websites, and evaluate pricing before making a decision. Agentic systems streamline many of these activities through intelligent automation.
This development changes the relationship between consumers and brands. AI becomes an intermediary that helps shape purchasing outcomes. The emergence of agentic commerce in ecommerce reflects broader advances in generative AI, large language models, recommendation systems, and autonomous decision-making technologies. As these capabilities continue to mature, shopping experiences are becoming more conversational, personalised, and efficient.
Google and OpenAI: Two Different Commerce Visions
Although both companies are investing heavily in AI-powered shopping experiences, their approaches are fundamentally different.
Google's strength comes from its search ecosystem and vast understanding of product discovery behaviour. OpenAI's strength comes from conversational intelligence and natural language interaction. These differences have important implications for retailers.
Google's approach to AI-driven shopping
Google continues to build on its existing commerce infrastructure through AI-enhanced search, product discovery, and recommendation experiences.
The development of the Google AI shopping agent reflects Google's focus on helping users discover products through intelligent search experiences. Google can combine search intent, merchant information, product feeds, customer reviews, pricing data, and user behaviour signals to deliver highly relevant recommendations.
For brands, this means product visibility will continue to depend heavily on structured data, accurate inventory information, merchant feeds, and strong digital presence across Google's ecosystem.
OpenAI's approach to e-commerce
OpenAI is approaching e-commerce through conversational experiences that allow users to interact with AI assistants in natural language.
The growth of OpenAI ecommerce integration points toward a future where consumers may ask AI assistants to identify products, compare alternatives, evaluate reviews, and recommend purchases through a single conversation.
This model shifts shopping behaviour away from traditional search patterns. Instead of opening multiple tabs and conducting extensive research, consumers may increasingly rely on AI-generated recommendations based on trusted product information and contextual understanding. Brands that create content and product information that AI systems can easily interpret may gain stronger visibility within these emerging e-commerce environments.
How AI Is Changing the Customer Journey
The traditional customer journey follows a familiar path involving awareness, research, evaluation, purchase, and loyalty. AI agents are reshaping each stage of that process.
The modern agentic AI customer journey can involve continuous interaction between consumers and intelligent assistants. AI systems can understand customer preferences, analyse previous purchases, identify suitable products, and make recommendations before consumers actively begin shopping. This creates a more proactive shopping experience.
AI agents may eventually monitor product availability, track pricing fluctuations, identify replacement opportunities, and suggest purchases based on anticipated needs. Retailers that understand these behavioural shifts will be better positioned to engage customers throughout the buying lifecycle.
Google vs OpenAI: What the Differences Mean for Brands
The future of commerce may involve two parallel discovery paths. Google's model remains heavily connected to search behaviour. Consumers begin with a query, and AI improves how products are surfaced and evaluated. OpenAI's model focuses on conversation-driven discovery. Consumers begin with an intent, and AI helps identify the most relevant solutions. For brands, the implications are significant.
Success in Google's ecosystem will depend on product feed quality, structured data, merchant visibility, and search optimisation. Success in OpenAI-driven environments will depend on how effectively AI systems can understand product information, customer value propositions, and contextual relevance.
Retailers should avoid viewing these ecosystems as competing channels. Customers are likely to use both. A shopper may discover a product through Google and later ask an AI assistant for recommendations before completing a purchase.
Brands that maintain strong visibility across both environments will have greater opportunities to influence future buying decisions.
The New Battle for Product Visibility
For decades, retailers optimised for search engines. Agentic commerce introduces a new challenge: optimisation for AI agents.
Traditional online shopping often followed a straightforward process. Consumers searched for products, reviewed results, compared options, and visited multiple websites before making a purchase.
Agentic commerce introduces a different path. AI agents can analyse available options, evaluate product information, and present a shortlist of recommendations directly to consumers. This shift increases the importance of machine-readable content, structured product data, customer reviews, and trusted brand signals.
As AI-generated recommendations become more common, product visibility may depend less on attracting clicks and more on providing information that AI systems can confidently evaluate and recommend. This change represents one of the most significant developments in digital commerce since the rise of search engine optimisation.
What Brands Must Do Now
Brands that bring agentic commerce in ecommerce should focus on strengthening the foundations that influence AI-driven recommendations. Developing an AI-powered online retail strategy can help organisations align product data, customer experience, and digital visibility with the growing role of AI agents in commerce.
Key priorities include:
- Audit product content to improve AI readability and consistency.
- Maintain accurate product specifications, pricing, and inventory information.
- Strengthen review generation and reputation management programmes.
- Improve structured data and metadata across commerce channels.
- Monitor how AI systems describe and recommend products.
- Prepare commerce infrastructure for agent-assisted purchasing experiences.
Industry research supports this approach. In its analysis of the emerging agentic commerce era, Forrester highlights the importance of adapting visibility strategies as new commerce channels develop. As AI-powered shopping experiences continue to evolve, discoverability will increasingly depend on how effectively AI systems can access, evaluate, and interpret product information.
What Agentic Commerce Could Look Like in the Next Five Years
The next phase of commerce may extend far beyond product recommendations. AI agents could automatically manage routine purchases, replenish household essentials, compare subscription services, monitor price changes, and identify opportunities to improve value for consumers. Shopping may become increasingly proactive rather than reactive.
Brands may also need to optimise experiences for autonomous purchasing workflows where AI agents participate directly in product selection and transaction processes. Organisations that begin preparing today will be better positioned as these capabilities mature. Product data quality, AI readiness, customer trust, and digital visibility are becoming increasingly connected components of retail success.
Strengthen Your Commerce Strategy for an AI-Driven Future
Search engines changed how customers discovered products. AI agents are changing how products are evaluated, recommended, and purchased. Brands that start preparing now can strengthen visibility, improve AI readiness, and position themselves for the next phase of digital commerce.
Vajra Global helps organisations assess emerging commerce trends, strengthen product data strategies, and develop practical roadmaps for AI-driven retail growth. Connect with Vajra Global to explore how your business can stay visible and competitive as agentic commerce continues to evolve.
