B2B buying has become more digital, more self-directed, and more expectation-heavy. Buyers want speed, relevance, and confidence when they search for products, compare options, request information, or place repeat orders.
But B2B commerce is not B2C commerce with a bigger basket size.
In industrial, manufacturing, and complex B2B environments, the buying journey is shaped by customer-specific pricing, contract terms, technical product data, compatibility requirements, documentation, compliance needs, delivery expectations, and multiple stakeholders. A buyer may not simply be looking for “a product.” They may be trying to solve a technical problem, replace a component, match a specification, confirm compatibility, or gather the information needed to support an internal purchase decision.
That is why many digital B2B experiences still fall short. They were built around products and transactions, not around buyer intent, operational complexity, and the knowledge needed to make a confident decision.
This was the central theme of the recent Smart Commerce Executive Webinar: B2B Buyers Have Changed. Most Digital Experiences Haven’t. In the discussion, Hariss Amin, Senior Manager, AI Customer Technology, Accenture; Jason Fernandes, Senior II Product Manager, Coveo; and moderator Mika Syrenius explored what B2B leaders need to fix now, how AI changes the buying journey, and why the fundamentals of data and structure matter more than ever.
The Mistake: Copying B2C Without Accounting for B2B Complexity
It is easy to say that B2B buyers now expect B2C-like experiences. In one sense, that is true. People are used to fast search, relevant recommendations, intuitive navigation, and instant answers in their personal lives. They bring those expectations into work.
But applying B2C patterns directly to B2B can create the wrong experience.
A consumer buying a replacement part for a household appliance may only need to confirm the model number, check compatibility, compare price and delivery options, and complete the purchase. A B2B buyer sourcing a replacement component for a production line needs those same basics, but the decision often depends on customer-specific pricing, contract terms, technical specifications, compatibility data, product documentation, compliance certificates, approved alternatives, delivery timelines, warranty and service information, and quote documentation that can support internal sign-off.
In other words, B2B buyers do not just need a smoother storefront. They need a digital experience that understands context.
That is where intent-driven discovery becomes critical. Instead of forcing buyers to know the exact product name, part number, or internal taxonomy, modern B2B commerce needs to interpret what the buyer is trying to achieve. The experience should help buyers move from problem to solution, even when they describe their need in their own language.
This is especially important in industrial and manufacturing environments, where product knowledge often sits across catalogues, manuals, support portals, sales teams, service teams, and ERP systems. When that knowledge is fragmented, the buyer experience becomes fragmented too.
AI Will Not Fix Poor Data. It Will Expose It.
The webinar discussion made one point especially clear: AI is only as useful as the data it can access.
The future of agentic commerce — where AI agents help buyers search, evaluate, order, or manage procurement tasks — depends on structured, reliable, accessible information. If product data is incomplete, support content is disconnected, or internal knowledge is locked away in silos, AI cannot create a trusted buying experience.
Amin described the data challenge simply: an AI agent, like a new employee, needs something reliable to learn from. If the underlying data is messy, incomplete, or inaccessible, the agent cannot deliver useful outcomes.
This is the part of AI transformation many companies underestimate. The visible layer may be a smarter search bar, a generative answer, or an automated workflow. But underneath that sits a much more practical foundation: clean product data, connected knowledge bases, governed access, APIs, clear ownership, and well-documented processes.
For many organizations, preparing for AI in commerce does not start with a large-scale automation project. It starts with getting the basics right.
- What data do buyers need?
- Where does that data live?
- Is it structured?
- Can it be accessed?
- Is it trusted?
- Who owns it?
- How does it connect to the buying journey?
Companies that can answer those questions will be in a stronger position to use AI in a meaningful way. Companies that cannot may find that AI simply highlights the gaps they already had.
Relevant reading: The AI Agent Readiness Checklist for Ecommerce
Intent-Driven Discovery Goes Beyond the Search Bar
Search is often the first place organizations look when improving digital commerce, and for good reason. Search queries reveal what buyers are trying to find, what language they use, and where the current experience fails.
But the webinar also highlighted that the same intelligence can create value across the entire customer journey.
For example, AI-powered retrieval and generative answering can support buyers after purchase, not just before it. When a customer has an issue, they do not always want to raise a ticket or wait for a service agent. They want a clear, reliable answer in the channel they are already using.
That could be a support portal. It could be a chatbot. It could be WhatsApp or Messenger. It could be an internal tool used by customer service or sales teams.
This matters because B2B service and sales are often deeply connected. A salesperson may know one product category extremely well but need help identifying a related solution from another part of the business. A customer service agent may need to search across product documentation, order history, support articles, and internal knowledge to resolve a case quickly.
In that context, better discovery is not just about conversion. It improves self-service, accelerates support, reduces internal friction, and helps teams serve customers with more confidence.
The opportunity is broader than “make search better.” The opportunity is to make enterprise knowledge usable wherever buyers, sellers, and service teams need it.
Relevant viewing: From AI Search to AI Agents: How UKG Transformed Self-Service
Agents and Humans Will Coexist, but the Work Will Change
The webinar also addressed a question many B2B leaders are now asking: what happens when AI agents become part of the buying process?
The realistic answer is not that human salespeople disappear overnight. B2B buying is too complex, too accountable, and too multi-stakeholder for that.
Instead, agentic commerce is likely to emerge in stages.
On the buyer side, agents may help with product discovery, repeat ordering, comparison, or preparing purchase requests. On the seller side, AI may help automate internal tasks, surface relevant knowledge, support service teams, or guide buyers through complex decision paths.
But in many B2B settings, humans will remain central to authoring, reviewing, approving, and signing off decisions. This is especially true where orders are high-value, technical, regulated, or tied to negotiated commercial terms.
The role of people may shift upward. Instead of spending time on repetitive information gathering, manual searching, or administrative work, people may spend more time reviewing AI-generated outputs, validating recommendations, managing exceptions, and making judgment calls.
That is a healthier way to think about AI in B2B commerce. Not as a replacement for human expertise, but as a way to make expertise more available, repeatable, and scalable.
Relevant viewing: The Rise of Agentic Commerce: Smarter Experiences that Act on Shopper Intent
What B2B Leaders Can Do in the Next 90 Days
The most practical part of the discussion focused on what companies can do now, even if they are not ready for advanced AI or autonomous commerce.
Three actions stood out.
First, look at your search performance data. Which queries return zero results? Which searches lead to poor engagement? Where are buyers using language that does not match your taxonomy? These signals show where your content, product data, or discovery experience is failing.
Second, talk to your service and support teams. They know what customers struggle to find, where confusion happens, and which questions come up repeatedly. That insight is extremely valuable for improving both digital commerce and AI readiness.
Third, start documenting workflows. How do customers buy today? What steps are manual? Where do approvals happen? Which tasks are repeated again and again? Documentation does not need to be perfect to be useful. It creates the foundation for identifying what can be improved, automated, or supported with AI later.
The broader lesson is simple: gather and structure the data now. In five years, the organizations that invested in data readiness, knowledge management, and connected digital experiences will have more options. Those that waited may find themselves starting from scratch while competitors are already scaling.
The Future Of B2B Commerce Starts With the Fundamentals
AI, agentic commerce, and autonomous procurement will continue to evolve. The exact timeline is difficult to predict, but the direction is clear: B2B buying will become more intelligent, more assisted, and more data-driven.
The companies best positioned for that future will not be the ones chasing every new AI feature. They will be the ones that understand their buyers, structure their knowledge, connect their systems, and design digital experiences around real B2B behavior.
That means moving beyond B2C-style assumptions. It means designing for complexity rather than pretending it does not exist. And it means treating data not as a back-office asset, but as the foundation of the customer experience.
To hear the full discussion and learn how B2B leaders are approaching smart commerce, intent-driven discovery, and AI readiness, watch the webinar on-demand.