OpenAI's Instant Checkout accelerates shift to agentic commerce in B2B and retail

The integration of AI-driven agentic commerce within ChatGPT is transforming procurement and retail, offering streamlined transactions but posing new security and infrastructure challenges for enterprises.

Shopping within ChatGPT has rapidly evolved from simple product recommendations to a fully integrated experience known as agentic commerce, where users can discover, compare, and complete purchases directly inside the chat interface. This development, driven by OpenAI...

’s Instant Checkout feature, signals a fundamentally new approach to procurement and digital commerce, especially for chief experience officers (CXOs) in B2B and enterprise settings.

Traditionally, buyers would turn to search engines like Google, navigate to a retailer’s website, and carry out transactions there. Tom Coshow, senior director analyst at Gartner, explains that this paradigm is shifting. “Now the commerce comes to the large language model chatbot instead of the other way around,” he said, highlighting the chatbot’s role as a consolidated shopping interface. Instead of bouncing between multiple sites, buyers interact with an AI agent that can present product carousels, narrow down choices, and, crucially, execute transactions via stored payment credentials, all within the conversation.

This creates a highly streamlined procurement process that could reshape both consumer and business purchasing workflows. In B2B environments, the AI can embed extensive buyer-specific data, such as price thresholds, preferred vendors, and delivery timing, to tailor decisions and automate orders, effectively acting as a digital procurement assistant. “All of that can become a block of memory that you want baked into your AI procurement agent,” Coshow noted.

However, the infrastructure needed to support agentic commerce remains a significant challenge. Katie Riddle, global retail strategy lead at Verizon Business, points out the demanding requirements for bandwidth, ultra-low latency, and computational power to provide the fast responses customers expect. She emphasises that many retailers are only beginning to explore these capabilities. For CXOs, initial priorities include stress-testing networks to ensure sufficient data flow, connectivity, and edge computing performance. Integrating disparate data sources such as CRM systems, point-of-sale platforms, product databases, and payment processors into unified data lakes is also critical for seamless AI interaction.

Security concerns form a crucial part of the equation. AI agents handling agentic commerce processes can access a broad attack surface that includes sensitive payment tokens, customer profiles, inventory, and vendor interfaces. Riddle reports that leading enterprises are experimenting with advanced security frameworks like network slicing and zero-trust protocols to protect these assets. However, mid-tier companies often lag in basic cybersecurity practices, such as cloud adoption for POS systems, endpoint management, and credential hygiene, leaving them vulnerable to common threats like ransomware and data breaches through unrecognised access points.

Coshow further warns that the most sensitive aspect is payment processing. While the expectation is that merchants, payment processors, and OpenAI will apply rigorous safeguards equivalent to those in traditional e-commerce, there remains inherent risk due to the unpredictable nature of large language models. AI systems can hallucinate or generate inaccurate information under uncertainty, which could have severe consequences in contexts like contract negotiations or invoice processing. This necessitates layered safeguards, including deterministic validation steps and strict purchasing rules embedded in the AI agents, to prevent erroneous or unauthorized expenditure.

Industry observers anticipate that by 2027, AI agents will be deeply integrated into B2B procurement, though with human oversight remaining essential to ensure control and accountability. Meanwhile, the retail sector faces a new competitive landscape as AI agents begin to influence consumer purchasing decisions algorithmically. Retailers are compelled to adapt by improving the machine readability of product data, building open APIs for real-time inventory updates, maintaining transparent pricing, and ensuring reliable fulfillment. These adaptations are necessary to compete for “algorithmic attention” in a market increasingly dominated by AI-driven shopping experiences.

Practical implementation challenges are also being addressed, such as limitations with initial versions of Instant Checkout that support only single-item checkouts and are available in select regions. Companies are exploring phased approaches, which include obtaining platform approvals, selecting pilot product lines, ensuring accurate product feeds, integrating payment providers, and conducting extensive sandbox testing to monitor system performance and user experience.

Meanwhile, some major retailers are forging strategic partnerships with AI innovators to pioneer AI-first shopping models directly within ChatGPT, broadening product offerings and shaping the future of retail.

Overall, the rise of agentic commerce represents both opportunity and disruption. While it promises a more personalised, efficient, and conversational buying experience, it demands significant investments in infrastructure, security, and operational rethinking from CIOs, CXOs, and the entire retail ecosystem to fully harness its potential.

Source: Noah Wire Services