The Gap Between Winners and Losers
For decades, B2B commerce has been built on something remarkably consistent: two people, shaking hands and building a relationship over time. Even as digitalization reshaped B2C retail, the fundamental human dynamic of B2B held firm.
But that dynamic is now being challenged. Agentic AI systems are beginning to take on the role of the B2B purchaser, and when the buyer is an algorithm, the entire playbook changes. Decisions are no longer shaped by loyalty, relationships, or habit, they are driven by data quality, transparent pricing, and frictionless system access.
Although B2C commerce has had a head start in digital transformation, B2B is catching up fast and the use cases are fundamentally different. AI is significantly widening the gap between B2B companies that act now and those that wait. The reason is structural, meaning that if a company builds a commerce solution that cannot support agentic commerce, either because the platform lacks the capability or because the solution is not designed to expose data and functionality in the right way, it will be locked into that setup for years. By the time they are ready to switch, competitors who moved earlier will already be selling to companies that use AI agents as buyers. The window to act is now, and it is closing faster than most organizations realize.
How AI Agents Buy Differently
Human buyers are, in many ways, pleasantly irrational and predictable. They are loyal. They order in batches, at the same time each month, from the same supplier they have trusted for years. They can be influenced by a long-standing relationship or simply the inertia of habit.
AI agents are none of these things. Agents will be far more disloyal than humans, constantly scanning for better prices, faster delivery, and more favorable terms. They will not batch orders the way humans do. Instead, they will fire a purchase the moment a predefined trigger condition is met. They will also split and fragment orders in ways that feel completely alien to traditional B2B logistics.
Even more critically, agents do not respond to brand affinity or relationship-based marketing. They respond to data quality, API accessibility, and frictionless integration. In practice, that friction is often introduced in the implementation layer which is how data, pricing, and logic are exposed, and not just in the platform itself. If your systems create friction, the agent will go elsewhere, automatically and immediately.
Most B2B Companies Are Not Agent-Ready
Most B2B businesses still have a deeply analog core. Product data lives in disconnected systems. Negotiated prices exist in Excel files on individual sales reps’ laptops. Inventory conditions are buried in ERP systems not designed to be read by machines. Customer-specific agreements, including deals, SLAs, and unique arrangements built up over years of relationship selling, exist in Word documents, emails, and individual memory.
If that data is not systematized, no AI in the world will make a difference. This is not a technology problem but an organizational one, and it is the single biggest reason why so many AI initiatives in B2B have failed to deliver real returns.
Three Foundations You Cannot Skip
Before any AI initiative can succeed in a B2B context, three structural foundations must be in place. Without them, even the most sophisticated AI tooling will deliver nothing.
1. Structured product data. Your product information must be machine-readable, consistently structured, and accessible to agents. If an AI agent cannot efficiently read and interpret your catalog, you simply will not exist in agentic commerce — not only to enable transactions, but to ensure your products can be discovered and compared in AI-driven buying journeys.
2. Real-time inventory and logistics data. Agents need to make informed decisions about availability and delivery times, including what is in stock across different warehouses, lead times for out-of-stock items, and the delivery commitments defined in customer contracts.
3. Transparent, systematized pricing. All negotiated prices, customer-specific contracts, and pricing logic must be centralized and accessible in one place, rather than scattered across spreadsheets, PDFs, and individual sales agreements.
These three pillars need to co-exist on a single platform and be consistently exposed to agents. However, just as important is how they are exposed in the solution layer, because if the data is not structured and accessible in practice, it will not be usable by external systems. Having product data only in your PIM, inventory only in your ERP, and pricing only in a folder of Excel files is not a foundation for the future. It is a recipe for irrelevance.
From Custom APIs to Universal Standards
The technical landscape is evolving from a "Wild West" of custom APIs to universal standards. While the Model Context Protocol (MCP) has emerged to help agents talk to legacy systems, the real game-changer is the Universal Commerce Protocol (UCP). Announced by Google, UCP is set to become the de facto standard for the entire commerce journey, covering everything from product discovery and price negotiation to the final checkout, making discoverability a critical competitive factor. This protocol levels the playing field, allowing agents to interact with any seller regardless of their underlying tech stack. For B2B leaders, platform choice is now a high-stakes decision: if your platform cannot speak these universal languages, your inventory becomes invisible to the world’s most efficient buyers. But even when the platform supports these protocols, limitations in how data, pricing, and functionality are exposed in the implementation layer can lead to the same outcome in practice. This also places new demands on partner selection, as few organizations have the in-house capability to design and implement commerce solutions that are truly ready for machine-driven interactions.
Why So Many AI Projects Still Fail
Despite the growing enthusiasm around AI in B2B, a significant proportion of AI initiatives are not delivering the results organizations expect. Reports continue to show that many AI investments fail to generate meaningful improvements in profitability or revenue. Three factors explain why:
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Lack of organizational ownership. When AI is driven only by IT, it stays in IT. For AI to drive real commercial outcomes, it needs cross-functional ownership across logistics, sales, marketing, and operations. Shared ownership, in practice, means no ownership.
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Working without the right partners and platforms. The pace of change in AI is too fast for any single company to navigate alone. Organizations that try to build everything internally miss the compounding advantage of working with partners who are actively building across many client environments simultaneously.
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The IT security veto. Security is essential. But when IT security functions as a blanket veto on all AI initiatives, the entire organization stagnates. The right answer is governed experimentation, not prohibition
The Companies That Succeed
Although AI has a learning curve for many B2B companies, it is already delivering measurable B2B value. There are many examples of B2B companies succeeding with AI initiatives. For example, Norwegian building retailer Brødrene Dahl has made significant progress in digitalization, with a large share of its business now running through digital channels, giving its trade customers 24/7 access and the efficiency benefits that come with it.
Companies that have already transitioned from manual sales processes to online ordering are in a dramatically stronger position to benefit from AI. With AI layered on top (smart recommendations, predictive reordering, dynamic product discovery) that kind of uplift could plausibly double. The point is not just that AI creates value on its own. It amplifies the value of the digital foundation you have already built.
Relationship Marketing Is Not Dead
But what happens if AI agents do not respond to relationships? Does relationship-based B2B marketing die?
The answer is nuanced. Agents themselves cannot be influenced by relationships. But the humans who configure those agents can. A procurement director or supply chain manager decides which suppliers an agent is allowed to consider, the constraints under which it operates, and the preferences it carries into the market. That person is absolutely reachable through relationship marketing.
The implication is a fundamental shift in where relationship investment pays off: from influencing individual purchase decisions to influencing the parameters that govern potentially thousands of automated decisions downstream. Relationship marketing is not dead. Its target has simply moved upstream.
Questions to Ask Your Organization
If you are a B2B decision-maker reading this, there are a few questions worth taking back to your organization immediately.
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If you released an AI agent on your own product catalog today, where would it get stuck? What friction points would it encounter, and what would those reveal about your data?
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Which repetitive internal workflows could realistically be automated by agents right now, and what is stopping you from starting?
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Is your product and pricing data structured and accessible enough for an agent to actually use?
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And perhaps most uncomfortably: which parts of your business risk becoming irrelevant within the next 12 months if you do not act?
The market is moving. Your competitors are already asking these questions. The companies that will win in AI-driven B2B commerce are not necessarily the ones with the most advanced AI. They are the ones with the cleanest data, the most systematized processes, and the cultural willingness to actually execute.
Key Takeaways
The businesses that start preparing now by systematizing their data, aligning their platforms, and ensuring that their commerce solutions are designed to expose data, pricing and processes in a machine-readable and frictionless way, will be well-positioned when it arrives. Those who wait until the glacier is upon them will find it is already too late to act. The partnership between Avensia and Norce is built precisely around this reality: combining commerce platform capability with strategic and implementation expertise to help B2B companies make this transition in a practical, measurable way.
The first step does not have to be a massive transformation program. It can be as simple as letting an agent loose on your own store and seeing where it gets stuck. The answer will tell you everything you need to know about where to start.
Want to learn more about how to succeed in an agentic commerce era? At Avensia, we help clients move from static funnels to adaptive, agent-led sales systems.
Most B2B sales models are not built for how buyers behave today.
We help companies move from static sales setups to adaptive, AI-supported models that drive measurable growth. Contact us if you want to explore what that shift looks like for your business.
