Agentic AI and the Future of Ecommerce: Bridging the Gap Between B2B and B2C Through Generative Engine Optimization and Autonomous Systems

The landscape of digital commerce is currently undergoing a fundamental shift as businesses transition from traditional automated systems to "agentic" artificial intelligence. This evolution, while promising to redefine how goods are bought and sold, is progressing at significantly different speeds across the business-to-business (B2B) and business-to-consumer (B2C) sectors. According to Paul do Forno, global commerce practice lead at Deloitte, the disparity between these two sectors is not merely a matter of adoption rates but a reflection of the inherent complexities found in industrial and professional procurement compared to consumer retail.

During a series of industry analyses and Deloitte workshops conducted in late 2024 and looking toward 2025, data revealed a stark reality for the B2B sector. Less than 24% of B2B suppliers reported utilizing agentic AI within their selling processes. This figure highlights a significant lag, as B2C companies—driven by the need for hyper-personalization and rapid customer service—have moved more aggressively toward autonomous systems. Do Forno describes the spectrum of agentic AI as a journey from "zero agentic capabilities" to "full autonomy," where AI can reason, plan, and execute complex tasks without human intervention. While B2C is expected to reach the autonomous stage relatively quickly, B2B remains in what do Forno calls a "bifurcated world," historically trailing behind consumer-facing innovations.

Understanding Agentic AI in the Commerce Ecosystem

To understand the implications of this shift, one must first define what distinguishes "agentic" AI from the generative AI models that became mainstream with the rise of Large Language Models (LLMs). While traditional AI might provide a recommendation or generate a block of text, agentic AI acts as a digital worker. It possesses the agency to interact with external systems, check inventory levels, process financial documents, and negotiate terms based on pre-defined parameters.

In the context of ecommerce, this means moving beyond simple chatbots that answer FAQs. Agentic AI is designed to attack specific "friction points" across different sales channels. Rather than a single, monolithic chat solution attempting to handle every customer need, the most successful implementations involve specialized agents targeting precise business processes. This modular approach allows companies to solve individual problems—such as order tracking or procurement approvals—as part of a broader, phased digital transformation strategy.

Solving the B2B Reordering and Procurement Bottleneck

One of the most immediate and impactful applications of agentic AI in the B2B space is the optimization of the reordering process. In a typical B2B environment, procurement is often bogged down by legacy communication methods. Buyers frequently submit orders via emails, PDF attachments, and manual purchase orders (POs). This creates a massive administrative burden and a high margin for error.

Do Forno identifies the conversion of PDFs into structured digital orders as the primary entry point for B2B companies adopting agentic AI. "I’ve got my PO. It’s attached as a PDF. Can you convert this into an order?" is the fundamental question many businesses are now answering with AI agents. These agents can read the unstructured data in a document, map it to the company’s product catalog, verify pricing against specific contract terms, and enter the data into the Enterprise Resource Planning (ERP) system.

Beyond simple data entry, these agents provide "availability to promise" (ATP) functionality. For example, if a buyer requires a specific shipment within a week, an AI agent can autonomously scan multiple inventory systems, warehouse locations, and logistics schedules. If the requested product is unavailable, the agent does not simply report a failure; it can suggest an alternative product that meets the buyer’s technical requirements and delivery timeline. This level of proactive problem-solving mimics the role of a highly skilled human sales representative but operates at a scale and speed that manual processes cannot match.

The Technical Infrastructure for Agentic Commerce

For B2B organizations looking to integrate these advanced capabilities, the path forward requires a robust technological foundation. Do Forno emphasizes that agentic AI cannot exist in a vacuum. It requires a "core commerce platform" or a cloud-based infrastructure as its base.

Before a company can deploy autonomous agents, it must have its data centralized and accessible via APIs. This core layer allows the AI to connect to various external and internal nodes, including:

• Digital Marketplaces: Ensuring product data is consistent across third-party platforms.
• Punchout Systems: Integrating directly with the procurement software used by large enterprise buyers.
• Supply Chain Visibility Tools: Providing real-time data on manufacturing and shipping status.

This "baby steps" approach is essential. A company must first move away from siloed legacy systems and adopt a modern commerce stack. Once the core is established, the organization can begin building agentic layers across different channels, gradually moving from simple task automation to more complex, multi-step autonomous workflows.

From SEO to GEO: The New Frontier of Discoverability

Perhaps the most significant shift identified by Deloitte is the transition from Search Engine Optimization (SEO) to Generative Engine Optimization (GEO). For decades, ecommerce success was dictated by a company’s ability to rank on the first page of Google through keyword optimization. However, as more buyers turn to LLMs like OpenAI’s ChatGPT, Google’s Gemini, and Perplexity for product discovery, the rules of the game are changing.

In an agentic AI-powered landscape, "discoverability" is no longer about matching keywords; it is about providing the context and intent that an AI model needs to "reason" through a buyer’s problem. Do Forno notes that for B2B companies, high-quality content—particularly detailed FAQs and technical documentation—is becoming more important than ever.

When an AI agent is tasked with finding a solution for a complex build—such as the components needed for a specific construction project—it doesn’t just look for a single product. It looks for a suite of products that work together. If a company does not provide the "intent" or the "use case" for its products, the AI model will likely overlook them. For instance, if a product can be used in extreme outdoor environments, that specific context needs to be autogenerated or manually produced and associated with the product data. By feeding the AI scenarios, use cases, and expert associations, companies can influence how agents discover and recommend their products in long-tail, contextual queries.

The Complexity of B2B Product Data and Regulatory Standards

The transition to agentic AI and GEO is significantly more difficult for B2B companies than for B2C retailers due to the sheer complexity of industrial data. In the consumer world, a product might vary by color or size. In the B2B world, a single part might have a dozen permutations involving material composition, tolerance levels, and regulatory certifications.

Regulatory standards play a massive role in B2B data management. Certain materials may only be legally used in specific jurisdictions or for specific industrial applications. If a company’s product data does not explicitly state these limitations or permissions, an AI agent cannot accurately surface the product to a qualified buyer. Furthermore, B2B buying is often governed by complex permissions; a buyer might only be authorized to purchase a subset of a catalog based on their corporate contract.

"Fitment" is another critical factor. In industries like automotive or aerospace, the AI must know exactly which parts are compatible with which machines. How a company identifies and labels these permutations determines whether an AI agent will surface the product or ignore it in favor of a competitor with clearer data.

Broader Implications and the Path Forward

The move toward agentic AI represents a "long ways away" for full autonomy in B2B, but the initial steps are already being taken by industry leaders. The implications of this shift are profound. Companies that successfully implement agentic AI will likely see a dramatic reduction in sales friction, lower administrative costs, and higher customer retention rates due to the ease of the reordering process.

Moreover, the shift toward GEO means that marketing departments must pivot. The focus is moving away from "gaming the system" with keywords and toward providing deep, authoritative, and context-rich information that can be consumed by machine learning models. This requires a tighter integration between product development, engineering, and marketing to ensure that the "intent" of every product is clearly articulated in the digital space.

As Deloitte’s research suggests, the "GEO of it all" is making visibility more complex. For many B2B companies, the immediate goal is simply to remain "under consideration" as AI agents become the primary gatekeepers of the procurement process. Those who fail to build the necessary core commerce foundations and enrich their data for an AI-driven world risk being rendered invisible in the next era of digital trade. The journey toward autonomous commerce is not just a technological upgrade; it is a fundamental reimagining of the relationship between buyer, seller, and the intelligent systems that connect them.