Agentic AI in Commerce: The 2026 Guide for B2Bs

Gartner predicts that by 2028, AI agents will handle 90% of all B2B purchases – over $15 trillion in annual spend. The forecast has ignited a race, with vendors repositioning platforms and executives scrambling to deploy agentic commerce before competitors do.

But ask three people what “agentic” means and you’ll get three different answers. The industry can’t agree on basic definitions, and that confusion is expensive. Companies are investing in capabilities they don’t understand, solving problems they don’t have, and building on foundations that won’t support what comes next.

This article defines what agentic AI in commerce requires, separates the real implementations from overpriced automation, and gives B2B companies a framework for evaluating readiness.

Most B2B organizations aren’t ready for autonomous commerce yet, but there’s significant value in the capabilities that come before it.

What Is Agentic AI?

The term agentic commerce has no single industry-standard definition.

McKinsey describes it as “shopping powered by intelligent AI agents capable of anticipating, personalizing, and automating every step of the process.” Gartner defines agentic AI as “autonomous or semiautonomous software entities that use AI techniques to perceive, make decisions, take actions and achieve goals.” 

Google frames it around protocols like the Universal Commerce Protocol and Agent Payments Protocol that enable agent-to-agent transactions.

Agentic AI definition that works for B2B

At OroCommerce, we use IDC analyst Heather Hershey’s framework because it’s the most operationally clear. Instead of focusing on the technology underneath or the protocols enabling it, Hershey defines agentic commerce by three conditions that must be met:

1. Autonomous purchase: The AI decides what to buy and executes the transaction
2. Autonomous payment: The system handles payment authorization without human approval on each order
3. Autonomous fulfillment: Post-purchase logic runs without manual intervention

With this framework, you can look at any system and ask: is a human making the purchasing decision? If yes, it’s not agentic commerce, regardless of how sophisticated the AI appears.

The 5 Levels of AI Autonomy

To truly evaluate a system’s capabilities, you must understand where the technology stands right now. The newly published IDC PlanScape: Agentic Commerce report provides a roadmap by defining the Five Levels of AI Autonomy:

• Level 1: Chatbots – Reactive tools that answer prompts but have no memory or ability to act outside of generating text.
• Level 2: Reasoners / Copilots – These models use chain-of-thought to solve complex problems. However, they still require a human trigger and cannot execute the plans they recommend.
• Level 3: Agents – Systems that can use digital tools to execute multistep plans on their own. This is where true agentic commerce begins.
• Level 4: Innovators – Systems capable of multi-agent collaboration. These can invent new processes and manage other agents. (Theoretical).
• Level 5: AGI / Organizations – Systems that operate entire organizations or complex domains without human intervention. (Theoretical).

Understanding this maturity model makes it painfully obvious when a vendor is overpromising. Walk through any demo of “shopping agents” and watch where human approval is required. If the AI does the research, comparison, and recommendation work, but a human still has to click “buy” – you’re looking at a Level 2 Copilot, not a Level 3 Agent.

What gets mislabeled as agentic AI for commerce

Here’s what’s common but incorrectly labeled as agentic:

• AI-powered search that helps buyers find products faster (product discovery with natural language processing)
• Chatbots that answer questions and add items to a cart for human checkout (conversational commerce)
• Systems that flag when inventory is low and suggest reordering (analytics and recommendations)
• AI that generates purchase orders from email requests and sends them for approval (human verification still required)
• AI-powered product recommendations based on browsing history (personalization, not purchasing)
• Voice assistants that add items to shopping lists (task capture, not transaction execution)

Why Autonomy and Automation Aren’t the Same Thing

Automation follows the rules you set. When inventory of SKU #4242 drops below 100 units, the system orders 500 more from Supplier A at the contract price. The workflow is deterministic – same inputs always produce the same output. When something unexpected happens, the automation stops and waits for human intervention.

Autonomy makes decisions within parameters you define, but determines its own path. An AI agent monitoring that same inventory might order from Supplier B this time because it detected a lead time change, or split the order between two suppliers to optimize delivery timing and cost. Same goal, different approach based on current conditions.

Here’s where companies get into trouble: they buy AI-powered autonomous systems, then constrain them with so many rules that all the autonomy gets programmed out. What’s left is expensive automation – deterministic workflows with a higher cloud bill and more complex failure modes.

AI-Powered Systems in Digital Commerce: Automation vs Autonomy

The cost difference matters too. Automation scales predictably. You know what you’ll spend because the workflows are fixed. Autonomy costs vary with complexity: more decisions, computing power, and unpredictability in both results and bills.

Agentic Checkout vs Agentic Commerce

The terminology continues to get muddled, particularly around “agentic checkout.” Google and OpenAI both use this term to describe checkout flows that happen inside AI platforms like ChatGPT or Google’s AI Mode. A user asks the AI to find a product, the AI presents options, and the user can complete the purchase without leaving the conversation.

This is conversational commerce checkout, not agentic checkout. The AI didn’t decide to make the purchase. A human did. The AI just shifted the buying process into a chat interface, narrowed options based on the prompt, and executed when told to.

True agentic checkout would mean shopping agents initiate and complete the transaction based on standing instructions or inferred need. The human sets parameters (“keep household cleaning supplies stocked”), the AI figures out when, what, and from whom to order.

Almost nothing in production works this way. Even Amazon’s Subscribe & Save, which comes closest, requires humans to set up the subscription and doesn’t dynamically adapt purchasing decisions. It’s sophisticated automation, not autonomy.

Buyer-Side vs Seller-Side Agents

The agentic commerce conversation is also splitting into two paths: AI agents that help buyers and AI agents that help sellers. The distinction matters because they optimize for different outcomes.

Buyer-side procurement agents are designed to reduce cost, ensure compliance, and optimize purchasing decisions for the company buying goods. They might compare suppliers, negotiate pricing, consolidate orders, or enforce procurement policies. The agent works on behalf of the buyer’s interests.

Seller-side sales agents are built to increase revenue, qualify leads faster, and move opportunities through the pipeline. They might identify upsell opportunities, automate quote generation, or prioritize accounts most likely to convert, improving customer engagement and business development efficiency. The agent works on behalf of the seller’s interests.

Right now, most “agents” on either side are actually assistants. They surface information and recommendations, but humans make final decisions. The next phase is when companies deploy their own agents and start transacting with other agents. That’s what protocols like Google’s Agent-to-Agent Protocol are built to enable as agentic commerce evolves.

But agent-to-agent commerce creates a new problem: when both sides are optimizing autonomously, who ensures the transaction is fair, compliant, and aligned with the actual business relationship? The technical infrastructure is forming. The governance frameworks and ethical considerations still lag behind.

AI Agents Face Different Rules in B2B

Most of the agentic commerce conversation is happening in retail. Consumer brands are testing AI-powered shopping assistants. Global retailers are integrating with AI platforms. The headlines focus on people online shopping for running shoes or home goods through conversational AI tools.

There’s a reason B2B digital commerce isn’t getting the same attention. The two business models operate on fundamentally different infrastructure, and trying to apply retail strategies to B2B creates expensive mistakes. 

To understand why those strategies fail, it helps to see where the retail urgency comes from.

Why Retail Is Racing Ahead (And Why B2B Isn’t)

Consumer behavior is shifting alongside consumer expectations. People use AI platforms for product discovery before they buy. Search engines like Google now compete with next-generation conversational interfaces for research traffic. For retail businesses, this creates pressure to be discoverable across the entire shopping journey.

The data shows the shift is real but incomplete. Transactional intent on AI platforms sits around 2% across ChatGPT, Perplexity, and similar tools. Consumers research there. They complete purchases on e Commerce platforms they already trust, like Amazon and brand websites.

This creates the merchant’s dilemma. Expose your catalog to AI systems and you risk losing customer relationships, pricing control, and the data that drives brand loyalty. Don’t participate and you risk becoming invisible as more consumers start their search in AI tools instead of Google or your website.

Industry leaders have mostly chosen a middle path: enable product discovery through AI platforms but redirect transactions to owned channels. They’re treating AI agents as a new top-of-funnel, not a replacement for their eCommerce platforms.

B2B Doesn’t Follow That Playbook

The retail vision of agentic commerce assumes AI agents will discover products across open marketplaces and complete purchases autonomously. B2B purchasing doesn’t work that way.

B2B starts with relationships, contracts, and approved supplier lists. The buyer already knows who they’re ordering from. AI’s job is executing purchases within those existing agreements.

The entire commercial infrastructure is different:

1. Contracts over catalogs. Pricing gets negotiated annually and tied to volume commitments, payment terms, and service-level agreements. AI agents can’t just compare products when each supplier relationship has custom terms.
2. Approval chains over instant checkout. A $50,000 component purchase flows through procurement and finance before execution. Governance requires human approval and audit trails, not autonomous transactions.
3. EDI over shopping carts. B2B already has automation through electronic data interchange. It handles repetitive orders efficiently but requires human intervention when exceptions occur, like supplier stock-outs, price changes, or specification adjustments.
4. Relationships over transactions. B2B decisions depend on trust, history, and strategic value – factors that don’t exist in the transactional data AI agents typically access.

The B2B Paradox

B2B requires more infrastructure than retail, but the decisions themselves are easier to automate. Consumer agents must predict preferences and handle novelty. Will this shopper like these running shoes? Hard problem. B2B agents need to execute known rules within governed systems. Does this order match our contract terms? Is it within budget? Is the supplier approved? Those are data lookups, not predictions.

The challenge is building unified systems where AI can access contracts, pricing, approvals, and inventory data. Most B2B companies don’t have that foundation yet.

Build the Foundation Before Chasing Autonomy

Most B2B companies aren’t ready for autonomous AI agents making purchasing decisions. Gartner predicts 40% of agentic commerce projects will be canceled by 2027. MIT research found 95% of generative AI pilots fail to deliver ROI.

The bottleneck is always the same: 70-85% of failures stem from data architecture problems, and 57% of organizations estimate their data is not AI-ready.

Chasing autonomy before the infrastructure exists burns budget. The companies avoiding these failures follow a different sequence. They fix the fragmented commerce stack and data first. Then they deploy practical automation. Autonomy comes later, once the foundation can support it.

What Works: AI That Assists, Not Decides

The AI capabilities B2B companies are deploying successfully don’t meet our definition of agentic commerce. Humans still make commercial decisions. But these systems require the same foundation autonomous commerce will eventually need.

• Document processing: Extracts purchase orders from PDFs and emails, validates against inventory and pricing, generates draft orders with minimal human input. Processing time drops from 20-30 minutes to under 2.
• Conversational assistants: Answer routine questions in natural language (stock levels, contract pricing, order status) without tying up sales teams.
• Semantic search: Interprets buyer intent across large catalogs, handles typos and variations, reduces abandoned searches and improves customer experience.
• Product recommendations: Analyzes actual buying patterns to suggest complementary items, increases order value without manual intervention.

These capabilities share common requirements: clean, structured product data, documented rules, and centralized business logic. The only difference: who has decision authority.

Infrastructure Requirements for AI-Assisted Automation

Why Building an AI Foundation Now Matters

Companies deploying AI-assisted automation today are creating the prerequisites for autonomous commerce tomorrow.

Your competitive window exists because critical infrastructure is still being built:

• Payment providers and payment systems develop infrastructure for autonomous transactions
• Fraud detection adapts to agent-driven purchasing patterns
• Liability frameworks establish clear responsibility when agents err
• Governance frameworks catch up to technical capability

While those barriers get solved externally, you can build internal readiness: unified data, proven automation, and workflows designed for AI. When the ecosystem is ready, you’ll deploy autonomous agents in months instead of years.

How companies build that foundation matters as much as when. The implementation data shows a clear pattern:

• 67% success rate for vendor-led AI solutions
• 33% success rate for internal builds

Specialized vendors solve defined automation problems on proven architectures. Internal teams often overreach, building toward autonomy on systems that can’t support it.

Next Steps for B2B Companies: The AI Readiness Ladder

Most B2B companies know infrastructure matters. Few know where to start. Here’s the sequence companies use to move from fragmented systems to AI-ready operations:

1. Foundation: Audit your current state

Where does pricing data actually live? Customer records? Product specifications? If the answer involves phrases like “Jim’s spreadsheet” or “the old system,” you have work to do before any AI deployment.

Map your data sources, identify fragmentation points, and document where business rules exist only as tribal knowledge.

2. Infrastructure: Unify commercial data

Consolidate pricing logic, structure product catalogs, and clean customer records. This creates the foundation both current automation and future autonomy require.

3. Automation: Deploy AI-assisted workflows

With clean data in place, start with high-frequency, low-risk processes where AI can deliver measurable ROI: document processing, conversational search, automated status updates. Deploy capabilities that deliver measurable ROI and actionable insights.

4. Watch: Monitor protocol development

Track agent-to-agent standards, payment network capabilities, and governance frameworks. When Universal Commerce Protocol, Model Context Protocol, or other agentic commerce protocols become production-ready, you’ll know whether they solve real problems or create new integration burdens.

5. Experiment: Test autonomous use cases carefully

When your data is unified and protocols mature, start with narrow autonomous deployments: consumable replenishment, routine MRO, standard components with multiple qualified suppliers. Low-risk purchases where optimization beats judgment.

The Discoverability Question

Building internal AI readiness is half the equation. The other half: ensuring your existing buyers can find what they need when they use AI-assisted tools.

The dual strategy:

• Maintain classic SEO for branded searches and specification-driven queries
• Structure your data so technical documentation, product specs, and supplier certifications are machine-readable
• Stay discoverable across search contexts, whether buyers use Google, AI platforms, or procurement system assistants

What this doesn’t mean: rushing to expose your catalog to every AI platform. Make your product information machine-readable, your specifications accessible, and your commercial terms clear enough that modern agents working on behalf of buyers can accurately evaluate and compare suppliers. Companies that stay ahead ensure their data is discoverable without sacrificing control over pricing and customer relationships.

Less Sci-Fi, more ROI: How OroCommerce Is Building for the AI Era in Commerce

OroCommerce was one of the first platforms built from the ground up for B2B back in 2017 and has remained true to this market ever since. 

Following the needs of our customers, we built a unified platform that runs commerce, customer relationships, business workflows, and full quote-to-cash from one system. The architecture was designed for complex B2B relationships from day one, which allows for scalability and straightforward expansion into new segments, markets, and selling models.

Now, with nine years invested into the most robust and secure B2B foundation in the market, we’re confidently building AI infrastructure around it.

Our approach to AI is less sci-fi – more ROI, which means:

• Unified data architecture: AI trained on complete transactional context (orders, quotes, inventory, customer history)
• Embedded assistance: order processing automation, intelligent chatbots, and back-office insights built into existing workflows, not bolt-on agents
• Explainable logic: AI tools backed by business rules teams can audit and trust
• Data sovereignty: your proprietary information never leaves your control or trains external models

AI works when the infrastructure supports it. Companies running on unified B2B commerce platforms can deploy practical automation today and add autonomous capabilities as the technology matures. 

What This Looks Like in Practice

A $6B lumber manufacturer was drowning in manual order entry. Sales reps spent 20+ minutes rekeying each dealer PO into the ERP. They deployed AI document processing:

• Reads incoming PDFs and extracts line items
• Validates SKUs against live inventory
• Creates draft orders for rep approval

Processing time dropped to under 2 minutes. Admin workload fell 30%.

A major HVAC distributor faced fragmentation instead of volume. After years of acquisitions, they were running 12 different ERPs while consolidating to one. Customer-facing teams couldn’t wait. They used AI to normalize order intake:

• Reads orders from any format (email, fax, portal)
• Routes correctly regardless of which legacy ERP handles fulfillment
• Creates unified customer experience despite back-office chaos

Neither company chased autonomous agents. Both deployed practical AI automation with OroCommerce and started generating ROI immediately.

The efficiency gains customers are already seeing with AI tools built into our core platform drive what comes next. Practical, secure AI for B2B commerce is our number one priority for the coming years.

The Choice Ahead

Gartner’s $15 trillion forecast points to an era of agentic commerce where autonomous agents handle B2B purchasing. Most companies read this as a call to deploy AI agents immediately.

But the race to deploy autonomous agents before infrastructure exists leads to predictable outcomes: canceled projects, failed pilots, budget burned on capabilities that don’t deliver. Organizations focused on proven automation first generate ROI while preparing for how agentic commerce will evolve.

This creates a divergence. Some companies chase headlines about agentic capabilities without the data architecture to support them. Others build competitive advantage through clean data, machine-readable systems, and reliable automation.

The choice determines who leads B2B eCommerce in the new era and who plays catch-up.