Five AI trends reshaping B2B commerce in 2026

A guest article by our partner Mirakl.

AI is changing how B2B companies sell, how procurement teams buy and how commerce platforms operate.

Gartner expects AI agents to intermediate more than $15 trillion in B2B purchases by 2028. The change is already underway, but many organizations are not yet prepared for it. Research also shows: Although 64% of B2B leaders expect AI to have a “very significant” impact on digital sales, only 20% say they are ready for what comes next.

This gap creates an opportunity for companies that act early. The following five trends show where AI is changing B2B commerce and what organizations need to address now.

Trend 1: Conversational AI creates new B2B buying channels

B2B buyers are changing how they discover products and place orders. This development is taking place in two areas: new AI-based channels are emerging, while established procurement and sales channels are gaining conversational capabilities.

Procurement professionals already use Large Language Model platforms such as ChatGPT, Gemini and Perplexity for product research. A buyer might ask an AI platform to find a supplier for industrial bearings that offers same-day shipping in the Midwest. LLM platforms are therefore becoming part of the early B2B buying journey.

At the same time, AI agents are changing existing channels. These specialized interfaces can pursue goals, take actions and complete tasks on behalf of users.

E-procurement systems increasingly use agents that understand natural-language requests. Webstores use chatbots to guide buyers through complex product selections. Messaging services are also becoming ordering channels. In some markets, restaurant owners can describe their supply requirements in a WhatsApp voice message, which an AI agent then converts into a structured order.

This development is expected to accelerate. Forrester predicts that procurement teams will deploy agents in 2026 that can scale negotiations across hundreds of suppliers at the same time. Static pricing pages could consequently develop into dynamic negotiation interfaces.

According to Gartner estimates, 80% of B2B sales interactions already take place digitally. Gartner also expects AI agents to handle most routine transactions within the next few years.

LLM platforms are now extending their capabilities beyond discovery and research. Some already support transactions within their chat interfaces. B2B organizations therefore need to consider both emerging AI platforms and AI-enhanced versions of their existing sales channels.

Trend 2: Product data determines whether AI can find an offering

AI agents depend on complete, current and structured information. Missing specifications or outdated prices can make a supplier effectively invisible.

A human buyer might contact a company to clarify incomplete information. An AI agent can instead select another supplier whose data provides a clear answer. This shift is contributing to the rise of Generative Engine Optimization, or GEO: the practice of influencing how LLM platforms retrieve, summarize and present information in response to a query.

According to Deloitte’s Tech Trends 2026 report, AI-generated answers already dominate results across major search engines and have reduced click-through rates to conventional websites by more than a third. AI platforms currently account for 6.5% of organic traffic, with the figure projected to reach 14.5% within a year.

Paid search shaped digital marketing in the 2000s, followed by social media advertising in the 2010s. AI-generated answers are now developing into a major marketing channel for the 2020s.

For B2B wholesalers, product data remains a persistent operational challenge. Supplier networks often produce inconsistent information, including:

• missing fitment data for automotive parts,

• incomplete specifications for industrial components,

• inconsistent naming conventions for food and beverage SKU,

• unclear replenishment cycles for accessories and consumables.

Manual catalog management can take months. The results may already be outdated when new products enter the assortment.

AI-powered catalog platforms can process supplier information from PDFs, spreadsheets and EDI feeds and transform it into agent-ready content in days rather than months.

This becomes particularly relevant for platforms managing thousands of suppliers. More suppliers with clean data create richer transaction patterns from which AI can learn. That foundation supports more relevant recommendations and better buying experiences.

Trend 3: Specialized agents enter B2B procurement

B2B commerce is beginning to use agents designed for individual tasks.

Negotiation agents can work on contract terms. Replenishment agents can trigger orders. Pricing agents can adjust rates, while assortment agents can optimize a supplier’s product mix.

Leading companies are already testing these applications. The agents operate continuously and require immediate, accurate information in four areas:

• pricing,

• promotions,

• inventory availability,

• delivery estimates.

McKinsey’s 2025 research found that 88% of organizations use AI in at least one business function. Scaling the technology remains the greater challenge. Organizations attributing more than 5% of EBIT to AI have redesigned their workflows around real-time data synchronization across these four areas.

For platforms with thousands of suppliers, specialized agents create both an opportunity and an immediate data requirement. They can help buyers find suitable products faster, negotiate better conditions and optimize spending.

Their effectiveness, however, depends on the quality and timeliness of the information sellers provide.
Pricing needs to reflect contracts and current market conditions. Promotional calendars must show active campaigns. Inventory data needs to represent actual availability. Fulfillment information must update as orders move through the supply chain.

Organizations that maintain accurate information across all four areas are better positioned than those relying on periodic updates.

Trend 4: AI takes on more post-purchase processes

The B2B customer journey continues after the order.

AI agents can already respond to order-tracking requests and process returns and refunds autonomously. These capabilities help B2B providers deliver the level of service their buyers increasingly know from consumer commerce.

Research from Boston Consulting Group indicates that companies using AI-driven customer management have achieved increases of up to 50% in customer acquisition and 20% in upselling and cross-selling.

B2B post-purchase processes often involve multiple stakeholders, approval workflows and account-specific contractual terms. Automated tracking updates, proactive delay notifications and immediate status information can reduce manual service work while maintaining consistency across a large number of transactions.

The main requirement is a connected data foundation.

Product attributes, current inventory and pricing, transaction histories and account-specific rules need to be accessible across systems. Once this information is available, AI can analyze patterns across millions of historical transactions and identify relevant products, prices and terms for each buyer’s context.

AI can also take on operational personalization while sales teams concentrate on strategic relationships and complex negotiations. This includes:

• product recommendations based on purchase history,

• dynamic pricing aligned with account relationships,

• industry-specific catalog views,

• contextual offers based on buyer behavior.

Human relationships remain central to B2B sales. AI supports those relationships by handling recurring operational tasks and making relevant information available when it is needed.

Trend 5: Connected buyer data gives sales teams the right context

Buyer information is often distributed across CRM and ERP systems, procurement platforms, support tickets and email conversations. Without a consolidated view, AI agents lack the context required to support sales effectively.

BCG reports that companies embedding AI agents across the customer journey are achieving up to 40% higher lifetime value from their client portfolios.

The underlying difference is a unified buyer profile that can combine:

• negotiated contract prices,

• product preferences,

• affinities between complementary products,

• post-purchase requests,

• seasonal buying patterns,

• delivery requirements.

This consolidated view helps AI identify relevant opportunities and gives sales teams better information for their conversations.

Creating it requires connected systems and centralized context. Organizations need a data strategy that removes silos and establishes a single source of truth for buyer intelligence. Data must be orchestrated across the commerce stack so that AI agents can work with complete and consistent information.

With this foundation, sales professionals can focus more of their time on strategic discussions. AI can provide immediate access to the information relevant to each buyer, supporting faster responses, more useful recommendations and stronger customer relationships.

Data infrastructure connects all five trends

Each of these developments depends on the same foundation.

AI assistants need structured catalogs to identify and recommend products. Real-time synchronization requires orchestration between systems. Post-purchase automation relies on complete order information. Personalization requires transaction histories and customer context.

Companies preparing for agentic commerce are therefore working on more than individual AI tools. They are improving the infrastructure that allows those tools to operate:

• data processes that ingest product information at scale,

• order management systems that synchronize in real time,

• fulfillment infrastructure that supports autonomous transactions,

• payment systems prepared for machine-to-machine negotiation.

This foundational work determines whether a company will be discoverable in an AI-mediated B2B economy valued at $15 trillion or remain outside the results presented to buyers.

More sophisticated AI cannot compensate for fragmented or unreliable data. Organizations need infrastructure that allows AI to access current information and act on it consistently.

B2B leaders that establish this foundation now will be better prepared to shape the transition to AI-powered commerce.

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