Agentic Commerce Is B2B's Next Battleground—Here's What Most Leaders Are Missing
Token Usage: 34,384 / 272,000 (13%) Uncached Read: 2,075 Cached Read: 32,128 Output: 181 Next create: 50% Next compact: 90% Transcript tokens over time 34,384 tokens · 13% Step token count (relative height) Summary created Compaction activated #1 77% 33,836 tokens $0.0220 #2 94% 34,203 tokens $0.0118 Cached read Uncached Cache write create a sunary of this article 5:41 PM Summary B2B ecommerce is entering a new phase: “agentic commerce,” where autonomous AI agents—not humans—will discover products, compare options, negotiate terms, and place orders. The article argues that most enterprise leaders are underestimating this shift because they still treat ecommerce like a better online catalog, optimized for human browsing. The key point: AI agents don’t shop like people. They make decisions using structured, machine-readable product and commercial data (pricing, inventory, certifications, contract terms, APIs). If your product data and systems aren’t accessible in this way, you risk becoming invisible to AI-driven procurement workflows—even if your website experience is strong. The article highlights early signals (agent-ready payment APIs, commerce tools integrated with AI assistants) and recommends five actions: audit and clean up data architecture, build API-first infrastructure, enrich product data beyond SKUs, implement governance/guardrails for risk and trust, and start with human-in-the-loop pilots (e.g., AI drafts quotes but reps approve). It closes with a warning: competition will increasingly be about whether an AI agent can find you, understand you, and transact with you—before a human buyer ever gets involved. make it 1 paragraph 5:41 PM B2B ecommerce is shifting toward “agentic commerce,” where autonomous AI agents—not humans—will discover suppliers, compare options, negotiate terms, and place orders, meaning most leaders are making a costly mistake by treating digital commerce like a better online catalog; the article’s core argument is that AI agents don’t browse or respond to marketing copy, they decide using structured, machine-readable data (pricing, inventory, certifications, contract terms) accessed through modern APIs, so if your product data and systems aren’t designed for these interfaces you risk becoming invisible; to prepare, it recommends auditing and fixing data foundations, investing in API-first infrastructure, enriching product data beyond SKUs, putting governance and security guardrails in place, and starting with small, human-in-the-loop deployments (like AI drafting quotes for rep approval) before scaling autonomy as trust grows.
Orlando Gadea
Executive Director
Tools
Agentic Commerce Is B2B's Next Battleground—Here's What Most Leaders Are Missing
I've spent years watching enterprises invest in digital commerce platforms. Most treat them like upgraded catalogs. That's about to become a very expensive mistake.
The shift isn't just about AI chatbots or smarter search. It's about autonomous agents that discover, negotiate, and purchase on behalf of your customers—without ever visiting your website.
This changes everything about how B2B companies compete.
Why This Matters Now
The numbers tell the story. The global agentic AI market will jump from about $5 billion in 2024 to nearly $200 billion by 2034, growing at more than 40% each year. Deloitte projects that 25% of enterprises using generative AI will deploy autonomous AI agents in 2025, doubling to 50% by 2027.
This isn't a distant future scenario. Shopify has already integrated its Commerce for Agents toolkit with OpenAI's ChatGPT and Microsoft Copilot. Visa, PayPal, and Mastercard have all launched agent-ready payment APIs. Alibaba.com plans to add agentic AI capabilities to its B2B eCommerce platform in December.
The infrastructure is being built right now. The question is whether your commerce architecture is ready to participate.
The Core Insight
Here's what most B2B leaders miss: AI agents don't shop like humans. They don't browse pretty pages or respond to marketing copy. They make decisions based on structured, machine-readable data.
Traditional ecommerce was built for humans searching Google. Agentic commerce is built for AI agents executing procurement tasks autonomously—comparing pricing, checking inventory, validating certifications, and placing orders in milliseconds.
If your systems and product data aren't accessible to these new interfaces, you'll be invisible.
This is why B2B commerce is particularly suited for this transformation, involving repetitive ordering, complex specifications and multi-stakeholder processes—areas where AI agents excel.
What This Looks Like in Practice
Consider what's already possible:
A business buyer can rely on an agent to rebuild carts from contract catalogs, request quotes, and route them for approval within spend limits. The agent assembles orders directly from customer-specific price lists, applies negotiated terms, and submits for sign-off—all without human intervention.
Agentic AI can serve as an intelligent, always-on sales assistant, guiding customers through product discovery, generating quotes, recommending bundles, and even submitting orders. Unlike traditional automation that requires reps to manually process quote requests, agentic systems can parse unstructured inputs, generate tailored responses, and only escalate to humans when thresholds are exceeded.
Salesforce's Connected Shoppers report finds that 43% of retailers are already piloting autonomous AI and 53% are evaluating its use.
Five Moves to Make Now
First, audit your data architecture. Agentic AI is only as strong as the data you feed it. If your foundation is fragmented or inconsistent, you create risks like wrong price quotes, wrong product recommendations, or poor customer interactions.
Second, invest in API-first infrastructure. Agentic commerce architectures are built on modern, API-first principles. Agents can connect with different business systems and access data across the ecosystem—breaking down the silos common in legacy setups.
Third, enrich your product data beyond SKUs. AI agents rely on context. Implement tools that generate variant-rich content, FAQs, and troubleshooting info, and expose it through accessible endpoints.
Fourth, build governance guardrails. In B2B, deploying autonomous AI agents raises complex security and governance challenges. Risk, trust and oversight are critical, especially in high-stakes areas like finance and procurement.
Fifth, start small but start now. Early deployments should mirror existing workflows: for example, the AI drafts a quote in CRM, but a rep approves it. Expand autonomy as confidence builds.
The Strategic Reality
Shopify executives outlined what they called a shift "from search to conversation"—where AI assistants manage product discovery, checkout and post-purchase interactions on behalf of buyers.
For manufacturers and distributors, this means your competition won't just be other suppliers. It will be whether an AI agent can find you, understand your offerings, and transact with your systems—all before a human buyer ever gets involved.
This isn't just your basic chatbot or recommendation engine. These agents can take action on their own, make decisions within set boundaries, and adapt to situations in real-time.
The companies that prepare their systems and data today will capture the attention of tomorrow's buyers. The ones that don't will wonder why their traffic is declining while their competitors' revenue is growing.
What's your biggest barrier to making your commerce platform agent-ready?
#agenticcommerce #b2becommerce #digitaltransformation #AI #b2b
