Preparing for agentic commerce in B2B: A practical readiness framework
AI agents are projected to intermediate 90% of B2B buying by 2028, pushing $15 trillion through autonomous purchasing flows.
The gap between leaders and laggards is already stark: High-maturity B2B suppliers beat annual sales goals by a 110% greater margin than low-maturity competitors.
This isn't a future scenario. It's a commercial reality unfolding now. The question isn't whether your buyers will use AI agents — it's whether your business will be a preferred supplier when they do.
At B2B Online Chicago, Mirakl and Deloitte shared a practical playbook for readiness. Here's what you need to know.
Why agentic commerce is different than traditional eCommerce
Agentic commerce isn't a new sales channel. It's a shift in how buying happens across all your existing channels, including your website, procurement platforms, even voice-to-shop experiences like WhatsApp ordering.
That changes what readiness means. You're not building a separate agent storefront. You're making your entire commerce infrastructure accessible and understandable to AI systems operating on behalf of buyers.
B2B makes this harder.
Agents need to navigate gated experiences, apply contract pricing, evaluate fitment requirements, access order history and apply customer-specific payment terms. A repeat order for industrial components requires confirming compatibility, applying negotiated pricing, routing to the right fulfillment location and providing accurate delivery estimates, all in one interaction.
Traditional eCommerce optimization won't cut it. You need a different foundation.
The three-layer agentic commerce readiness framework
Most companies want to jump straight to building chatbots or voice ordering. But agentic commerce readiness works in three layers, each building on the one before it.
Layer A: Presence
The first layer is presence. Before you can sell on LLMs and through AI shopping agents, you need to be found. That means making your business discoverable to AI agents by:
Monitoring how agents interact with your content.
Implementing structured product data and schema markup.
Upgrading deep linking in order to direct agents to the right pages.
Creating clear summaries for products, categories and FAQs.
Layer B: Access
Once you’ve got discoverability down, the next thing you need to do is give AI shopping agents and LLMs the ability to transact with you. That comes in the form of APIs.
Model Context Protocol (MCP): Acts as a secure digital handshake, allowing AI agents to query external data sources (like real-time inventory and pricing) directly for faster, more accurate information than web scraping.
Read-only APIs: Lets agents access product info, inventory and pricing
Transactional APIs: Enables agents to build carts and create orders
Post-purchase APIs: Provides visibility into order status, tracking and returns
Without this layer, agents can research and recommend your products, but they can't close the shopping loop.
Layer C: Experience
Every retailer and brand cares deeply about their customer experience. This layer is where you can shape how and where transactions happen. Consider:
In-tool checkout with LLM partners (Microsoft Copilot, Google AI).
First-party AI shopping assistants on your site or app.
Autonomous commerce for subscriptions, pricing negotiation and replenishment.
Agent-led, post-purchase support to resolve customer service queries like “when will I get it,” warranty claims or technical support, improving the end-to-end experience.
The key insight: You can't skip straight to Layer C. The experience only works if Layers A and B are solid.
Making your catalog agent-readable (GEO optimization)
Every early deployment reinforces the same lesson: clean, structured product data is the foundation.
But "clean" means more than accurate SKUs. It means making your catalog easy for large language models (LLMs) to interpret and use — commonly referred to as eCommerce GEO (generative engine optimization).
To get an idea of what eCommerce GEO looks like in practice, consider the following example of traditional vs GEO-optimized product data.
Traditional product data:
Title: "Hand Drill – Power tools – JWTT098801"
Description: "Professional cordless hand drill, 12V-35"
GEO-optimized product data:
Title: "Professional GBXY RE corded rotary drill – 750W motor, 2-speed gearbox, keyless 13mm chuck"
Description: Explains what it's for, who should use it, how it compares
Attributes: Compatibility details (230V corded, 13mm drill bits, metal/wood)
FAQs: Max drilling capacity, speed ranges, intended applications
This level of detail helps agents make accurate recommendations and complete transactions confidently.
If your catalog has gaps or ambiguities, agents can't use it reliably. In a world where agents intermediate buying decisions, incomplete data means lost revenue.
What an agentic commerce experience looks like in practice
A procurement manager needs to reorder components. Today that might look like three emails, a phone call and manual system entry. It’s time consuming, labor intensive and tedious.
With agentic commerce, the buyer's AI assistant:
References order history to confirm part numbers.
Checks real-time inventory across approved distributors.
Applies contract pricing and payment terms.
Calculates delivery estimates.
Routes the order for optimal fulfillment.
The buyer confirms. Done. No emails, no calls, no manual entry. This is the agentic commerce future we are rapidly approaching.
Early pilots show measurable impacts like higher conversions as agents complete transactions without friction, fewer service calls as agents handle routine inquiries, and better order accuracy as agents validate compatibility programmatically.
Lessons from B2B agentic trials
Leading B2B manufacturers and distributors are already rolling out agentic commerce experiences. Here are a few early learnings from these projects:
Start with a commercial hypothesis. Tie readiness to a business problem in order to reduce order cycle time, improve repurchase accuracy, lower cost-to-serve or expand digital reach.
Be problem-first, not tech-first. Identify three high-friction moments in your buyer journey where agents can deliver value. Build narrow solutions rather than trying to solve everything at once.
Treat data as a revenue driver. Agents have no tolerance for ambiguity. Missing attributes and incomplete information don't slow them down — they stop them.
Think MVP. Run fast four-week cycles with clear metrics. Launch one use case, measure, learn and adjust.
Three actions to take now in your B2B agentic commerce journey
Audit your product data. Review your catalog through an agent's lens. Are titles descriptive? Do you have scenario-specific attributes? Is compatibility documented? Prioritize enrichment.
Identify your highest-friction interaction. Is it complex re-ordering? Product discovery? Order tracking? Pick one and use it as your first agentic use case.
Align the right stakeholders. This isn't just about IT or marketing. It requires commerce, product, data and channel leaders working together.
The B2B suppliers pulling ahead aren't predicting the future. They're so they're ready when their buyers are. The question isn't whether you'll get there — it's how much ground you'll cede before you do.
If you're evaluating where to start or want to explore how Mirakl's B2B solutions can support your agentic commerce strategy, to continue the conversation.
