Business-to-business (B2B) and business-to-consumer (B2C) e-commerce companies are exhibiting distinct paces in their integration of agentic artificial intelligence (AI) into core business operations, a disparity highlighted by Paul do Forno, global commerce practice lead at Deloitte. His observations indicate a significant lag in B2B adoption, suggesting that fully autonomous agentic AI in this sector remains a distant prospect, while B2C is poised for much quicker advancements.
The Nascent Stage of Agentic AI Adoption
Do Forno’s assessment stems from a Deloitte workshop held in late 2025, where a striking finding emerged: fewer than 24% of participating suppliers reported having utilized agentic AI within their selling processes. This statistic underscores the nascent stage of agentic AI implementation, particularly within the B2B landscape. Do Forno characterized the spectrum of agentic AI capabilities as ranging from rudimentary agentic functions to full autonomy, noting that the B2B sector, in particular, is still "a long ways away" from achieving autonomous capabilities, even as B2C is expected to accelerate its integration. This dichotomy, he explained to Digital Commerce 360, reflects a historical trend where "B2B always has been behind" in embracing digital innovations compared to its B2C counterpart.
Deloitte’s ongoing research reveals that companies currently leveraging agentic AI are primarily deploying it to address specific, isolated problems as part of broader digital transformation initiatives. Instead of monolithic AI solutions, the focus is on "attacking very specific friction points for different channels." This targeted approach suggests a cautious, incremental strategy, where businesses are testing the waters by automating discrete, high-impact tasks rather than undertaking wholesale systemic overhauls. This contrasts with the often more rapid and widespread adoption seen in consumer-facing technologies, where user experience and immediate gratification drive innovation.
Understanding Agentic AI: Beyond Traditional Automation
To fully appreciate the scope of this development, it is crucial to define agentic AI. Unlike conventional AI or rule-based chatbots, an agentic AI system possesses the ability to reason, plan, and execute actions autonomously to achieve a specified goal. These agents can interact with multiple systems, gather information, make decisions, and even learn from their interactions, adapting their strategies over time. For instance, while a traditional chatbot might answer a question based on predefined scripts or a knowledge base, an agentic AI could proactively initiate complex processes, such as navigating various databases, engaging with other digital systems, and making purchasing decisions, all without direct human intervention at each step. This level of autonomy represents a significant leap from reactive, query-response systems to proactive, goal-oriented intelligent agents.
The foundation for agentic AI often lies in advanced large language models (LLMs) like OpenAI’s ChatGPT, Google Gemini, or Perplexity. However, agentic capabilities extend beyond mere language generation; they involve an orchestration layer that allows these models to act as decision-makers and task executors across diverse digital environments. This evolution marks a pivotal moment in AI development, promising to reshape how businesses manage complex operations, engage with customers, and optimize supply chains.
The Enduring B2B-B2C Digital Divide
The persistent lag of B2B in digital adoption, including agentic AI, is not new. Several inherent complexities distinguish B2B e-commerce from B2C. B2B transactions are typically characterized by:
- Higher Order Values and Volume: Purchases are often larger, more infrequent, and critical to business operations.
- Complex Pricing and Contracts: B2B often involves negotiated pricing, volume discounts, custom quotes, credit terms, and long-term contracts, which are difficult to automate.
- Multi-Stakeholder Purchasing: Buying decisions frequently involve multiple departments, approvals, and user roles, extending the sales cycle.
- Regulatory Compliance and Industry Standards: Many B2B sectors operate under strict regulations (e.g., healthcare, manufacturing, chemicals), requiring meticulous product data and transaction auditing.
- Legacy Systems and Integration Challenges: Older, siloed enterprise resource planning (ERP) systems, customer relationship management (CRM) platforms, and supply chain management (SCM) tools often present significant hurdles for integrating new, advanced AI solutions.
- Customization and Configuration: Products and services often require extensive customization, technical specifications, and compatibility checks, demanding sophisticated configurators that go beyond simple product variations.
These factors contribute to a more intricate digital ecosystem in B2B, making the seamless integration of autonomous AI agents a considerably more challenging endeavor. In contrast, B2C e-commerce, while still complex, generally deals with more standardized products, simpler pricing models, individual purchasers, and less stringent regulatory environments, allowing for faster experimentation and deployment of AI-driven solutions.
Agentic AI Addressing B2B Pain Points: Practical Use Cases
Despite the challenges, the potential for agentic AI to revolutionize B2B operations is substantial. Do Forno highlighted several immediate applications where agents can alleviate common friction points:
One compelling example is reordering and inventory management. Imagine a B2B buyer needing a specific component within a week. An AI agent could autonomously query various internal systems—inventory databases, supplier networks, logistics platforms—to ascertain availability. If the exact product is out of stock or cannot meet the delivery timeline, the agent could proactively identify suitable alternatives, check their availability, compare specifications, and present these options to the buyer, complete with delivery estimates. This "availability to promise" capability, as Do Forno noted, is already being actively developed and implemented. This moves beyond merely showing stock levels to intelligently finding solutions based on dynamic constraints.
Another significant area is streamlining the purchase order (PO) process. B2B transactions frequently involve exchanging POs via email, PDFs, or even faxes. An agentic AI can be trained to ingest these diverse formats, extract critical information (product codes, quantities, pricing, delivery instructions), and automatically convert them into structured orders within the company’s e-commerce or ERP system. Do Forno posed a common scenario: "’I’ve got my PO. It’s attached as a PDF. Can you convert this into an order?’" This represents a primary entry point for agentic AI in B2B, drastically reducing manual data entry errors and accelerating order fulfillment.
Beyond these, agentic AI could transform other B2B functions:
- Personalized Product Configuration: For complex products requiring multiple components or specific technical specifications, agents could guide buyers through configuration processes, ensuring compatibility and compliance, and even suggesting optimal setups based on usage scenarios.
- Proactive Maintenance and Support: Agents could monitor equipment performance, predict maintenance needs, and automatically initiate service requests or order replacement parts, minimizing downtime for clients.
- Supplier Relationship Management: Agents could monitor supplier performance, track contract compliance, identify potential supply chain disruptions, and even negotiate terms for routine purchases.
- Dynamic Pricing and Quote Generation: For complex B2B services or customizable products, agents could generate real-time, dynamic quotes based on current market conditions, customer history, and specific project requirements.
Foundational Requirements for Agentic Commerce
For B2B companies eyeing the integration of agentic commerce, a robust technological foundation is non-negotiable. Do Forno emphasized that a "core commerce platform or cloud" is the essential prerequisite. Without this stable base, efforts to deploy AI agents will likely falter. This core platform serves as the central nervous system, enabling data flow, transaction processing, and system integrations necessary for agents to operate effectively. From this foundation, companies can then extend capabilities to "roll out to the marketplaces, connect to the marketplace, connect to the punchout — connect to all these things." This methodical approach, likened to "baby steps," underscores the importance of a solid infrastructure before venturing into advanced AI applications across multiple channels.
Discoverability in an Agentic AI Landscape: The Rise of Generative Engine Optimization (GEO)
The advent of agentic AI profoundly impacts how products and services are discovered by both human buyers and AI agents. Do Forno stressed that "discoverability" is paramount for success. Beyond basic chatbot responses, with the right foundation, AI agents can assist shoppers in building complex orders. This necessitates a shift in content strategy from traditional search engine optimization (SEO) to a more sophisticated "generative engine optimization" (GEO).
While SEO primarily focuses on keywords to rank content for human search engines, GEO expands upon this by emphasizing context, intent, and rich, structured data that AI models can interpret. Do Forno explained, "What’s different is you need to understand the intent versus just the keywords." Keywords might offer a cursory presence, but deep understanding of buyer intent, complex use cases, and product interdependencies is crucial for AI agents to make relevant recommendations.
For example, if a B2B buyer is looking to "build a house," an AI agent needs to understand that this intent encompasses a vast array of associated products—from foundational materials to finishing touches, regulatory-compliant components, and even related services. Companies must "break all that down" into digestible, interconnected data points for the AI. This means expanding product information beyond basic descriptions to include:
- Detailed FAQs: Addressing common questions and scenarios.
- Use Cases and Applications: Providing concrete examples of how products are used, ideally with associated scenarios and problem-solving contexts.
- Component Relationships: Clearly mapping how individual products contribute to larger assemblies or solutions (e.g., 12 products comprising a full component for a build).
- Expert Associations: Linking products to known experts or industry standards, lending credibility and context.
- Regulatory and Compliance Data: Crucial for B2B, detailing legal restrictions, handling requirements, and certifications for specific materials or products.
If marketing teams have only scratched the surface with basic SEO and haven’t pushed out a comprehensive catalog of rich, contextualized product information, there’s significant work ahead. The goal is to provide the AI with enough granular, interconnected data to handle "contextual, longtail queries" and influence agent discovery.
Do Forno pointed out that "the visibility is actually now way more complex — the GEO of it all." This complexity arises from the need to anticipate and cater to the diverse ways AI agents will interpret and connect information, going beyond simple keyword matching to understanding the underlying purpose and application of products. This demands a proactive approach to content creation, ensuring that product data is not only accurate but also rich, interconnected, and readily interpretable by intelligent agents.
Challenges in Data Management and Regulatory Compliance
A significant distinction between B2B and B2C product data, according to Do Forno, lies in the pervasive involvement of regulatory standards. "If a certain material can only legally be used in certain instances or handled a certain way, that makes the product data ‘super complex’," he stated. This adds layers of information that must be meticulously cataloged and presented to AI agents to ensure compliance and prevent erroneous recommendations.
Furthermore, B2B purchasing channels introduce additional complexities. A buyer might not have access to certain products due to internal approval processes or specific contractual agreements, requiring the AI to understand and respect these nuanced buying rules. "Fitment matters, too," Do Forno added, noting that a single product can have "a dozen permutations for a product, including color, size, shape and more." How a company effectively identifies and categorizes these permutations for AI consumption directly impacts how often and accurately an agent can surface a product for a specific buyer’s needs. This necessitates sophisticated product information management (PIM) systems capable of handling vast amounts of interconnected, attribute-rich data.
Broader Implications and the Future Outlook
The trajectory of agentic AI adoption in e-commerce suggests a future where intelligent agents play an increasingly central role in facilitating transactions, managing logistics, and enhancing customer experiences. For B2B companies, embracing this technology offers a pathway to unlock significant operational efficiencies, reduce costs, and gain a competitive edge in increasingly complex markets. Those that invest early in building robust core commerce platforms, enriching their product data for GEO, and strategically deploying agentic solutions to address specific pain points will be best positioned to thrive.
The current disparity between B2B and B2C is not merely a reflection of different adoption rates but also an indication of the unique challenges and opportunities each sector presents for AI innovation. While B2C may lead in consumer-facing applications, the transformative potential of agentic AI in B2B lies in its capacity to automate highly complex, high-value processes, fundamentally altering how businesses operate and interact within their supply chains. The journey towards fully autonomous agentic AI in B2B may be long, but the initial steps outlined by Deloitte suggest a clear direction for strategic investment and development.
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