Key takeaways:
Why agentic commerce matters in B2B
Automation has long been a promise across the B2B sector: Eliminate manual labor, execute repetitive tasks within predefined rules and accelerate sales decisions at scale. Yet many of the most critical B2B processes to commerce — quoting, replenishment, complex ordering — have remained stubbornly resistant to automation, requiring heavy human involvement to avoid costly mistakes.
Agentic commerce changes that equation.
While early agentic AI adoption has been largely consumer-facing, focused on conversational experiences in channels like ChatGPT, its impact in B2B is unfolding as a way to reduce friction in complex workflows, minimize errors and bring agency to processes that were previously considered too nuanced or risky to automate. As a result, B2B enterprises are poised to increase revenue and drive long-term growth.
At its core, agentic commerce in B2B is about delegating intent. Instead of navigating portals, spreadsheets and email threads, buyers and sellers increasingly expect AI agents to execute tasks on their behalf — within pre-defined rules, and with full awareness of contracts, purchase history and operational constraints. When applied correctly, this approach has the potential to rewrite long-standing bottlenecks, from quoting and item resolution to guided ordering across complex catalogs.
Across industries, three broad categories of agentic commerce use cases are taking shape:
Improving the “ease of doing business” for buyers.
Augmenting sales reps in an increasingly hybrid commerce environment.
Boosting operational efficiency across the board.
Let’s take a look in more detail.
Improving the “ease of doing business” for buyers
For many B2B organizations, agentic commerce begins not with radical autonomy, but with something far more fundamental: Fixing the everyday friction that makes buying harder than it needs to be.
In B2B, “ease of doing business” is not a vague promise. It shows up across the purchasing journey, in how quickly a buyer can get a correct quote, how confidently they can reorder the right product and how little effort it takes to complete routine tasks without back-and-forth emails or phone calls. According to Dentsu, B2B enterprises that focus on getting the buyer experience just right close deals 31% faster, highlighting the tangible business impact of digital excellence.
Agentic commerce is uniquely suited to address these moments because they are rules-driven, repetitive and high-impact.
Quoting is one of the most critical — and most painful — processes in B2B commerce. Despite years of digital investment, it is still largely manual, fragmented and slow. Buyers request quotes via email or PDF documents at best, via fax and photos at worst. Sellers respond after reconciling pricing rules, availability, contracts and internal approvals, information which could be spread across many disparate systems like the CRM, ERP, and spreadsheets. Multiple stakeholders are involved, and every handoff introduces a delay.
In practice, quoting often breaks down in predictable ways:
Buyers submit SKU lists using their own internal (ERP) codes, forcing sellers to manually map products.
Inventory visibility is disconnected, so out-of-stock items are only discovered after a quote is requested.
Quotes may exceed budget thresholds with no guidance on viable alternatives.
Sellers must escalate discount decisions through internal approval chains or make them on their own to secure a deal, even when rules already exist.
Agent-led quoting changes the nature of this interaction.
By operating within predefined pricing rules — volume tiers, customer-specific contracts, regional constraints — AI agents can resolve complex pricing logic instantly and generate a price-ready basket in real time. Instead of waiting days for a quote, buyers receive almost immediate, accurate pricing that reflects their actual entitlements and current inventory.
For sellers, this removes the constant need for manual intervention while protecting margins. For buyers, it replaces uncertainty with clarity.
Crucially, this only works when agentic quoting is grounded in deterministic AI:
Strict business logic rather than open-ended generation.
Governed workflows that reflect real approval structures.
Deep integration with ERP, CRM and inventory systems.
When implemented correctly, quoting becomes a seamless step in the buying process.
Few experiences are more frustrating for B2B buyers than trying to identify the correct spare part for a specific piece of equipment, especially in industries where part numbers change over time, vary by region or differ across generations of products.
Agentic systems excel here because they can interpret intent, not just inputs. AI agents can map:
Free-text descriptions (“the gasket for the older Model X compressor”).
Serial numbers or equipment IDs.
Photos taken on a factory floor or in the field.
…to the correct, currently available SKU.
This dramatically reduces misorders, returns and back-and-forth clarification. It also lowers the cognitive burden on buyers, allowing them to focus on keeping operations running rather than decoding product catalogs. The best part? A seller can still be in the loop to confirm and verify that this is indeed the right part.
Today’s buyers expect to resolve routine questions without calling a sales rep. Too often, however, self-service experiences stop at static FAQs or generic chatbots that lack real context.
Agentic self-service moves beyond this by combining conversational access with real operational data. Agents can answer questions such as:
“Where is my order?”
“What did I pay for this last year?”
“Can I reorder this exact configuration?”
On their own, these capabilities already reduce Average Handling Time and support costs. But the real value emerges when self-service becomes personalized and proactive.
This is where features like “Did You Forget?” logic come into play, reminding customers of items they typically purchase but might have missed in a current order. Or it’s an opportunity to surface “Frequently Purchased With” items, increasing basket size and enabling new product discovery among existing customers.
When done well, personalized self-service reduces errors and forgotten items, building trust and retention across the customer base.
Guided ordering agents help buyers navigate complex product catalogs by asking clarifying questions and progressively narrowing the set of valid options. Instead of discovering incompatibilities at checkout — or worse, after delivery — AI agents prevent invalid combinations upfront.
This is especially valuable in industries with:
Configurable industrial equipment.
Technical dependencies between components.
Regulatory or safety constraints.
AI agents can guide buyers through configuration, pricing and quoting in real time, ensuring that every selection is compatible with previous choices.
Building on this logic, B2B enterprises can implement a secure natural language interface integrated with commerce APIs, allowing buyers to create and modify their carts entirely through conversational commands. For example, instead of navigating multiple pages to edit an order, a buyer could simply tell the AI agent, “Increase my weekly produce order to 50 cases, switch to organic tomatoes, and remove the bottled juice,” and the system updates the checkout basket automatically in real time.
The outcome is a smoother ordering process, fewer errors, faster transactions and significantly higher buyer confidence.
Replenishment is one of the most repetitive tasks in B2B commerce — and one of the most overlooked opportunities for improvement. Using historical data, consumption patterns and operational signals to predict needs before the customer explicitly asks, recurring orders can be achieved autonomously.
That means, instead of forcing buyers to remember what to reorder and when, AI agents proactively generate replenishment orders or draft carts for review. Depending on the setup, they can even check out the order with minimal or no human intervention.
Over time, ordering shifts from a conscious task to a background process:
Safety stock levels are monitored continuously.
Patterns are recognized automatically.
Reorders are suggested or initiated according to predefined rules.
A “human in the loop” approach provides another layer of trust for buyers to adopt autonomous agents.
For buyers, this eliminates the mental overhead of routine purchasing. For sellers, it increases retention, basket consistency and predictability.
Augmenting sales reps in an increasingly hybrid commerce environment
The future of agentic AI in B2B is not about replacing people. It’s about removing the operational drag that prevents them from adding value.
By automating the repetitive, rules-based work — the replenishment grind, the back-and-forth of quoting, the manual resolution of items — agentic systems enable sales and service teams to become Super Sellers. They become focused less on processing orders and more on advising customers, solving real problems and strengthening long-term relationships that help lift the bottom line.
Augmented selling provides salespeople with AI-powered intelligence to identify, engage and close deals faster and more effectively. This intelligence layer transcends the multiple platforms B2B organizations rely on — ERP, CRM and even spreadsheets — giving sales reps a 360-degree view of customers instead of a partial, and often outdated, one.
For example, a rep might ask: “Show me which industrial clients have decreased their orders of packaging supplies compared to last quarter.”
In seconds, the agent surfaces order behavior changes, competitor threats and historical patterns — information that would have taken hours to compile manually. By democratizing data access for non-technical staff and reducing administrative friction, every on-site or digital interaction is driven by real-time insights rather than intuition.
Agentic systems also help sales reps automate and personalize engagement cadences across email, chat and social channels, orchestrating timely, relevant and context-aware follow-ups and nurturing campaigns.
A powerful extension of this capability is the seller-influenced customer experience. By embedding the seller’s expertise into the digital buying journey at the right moments, sales reps can influence product recommendations, merchandising and portal content based on their knowledge of customer preferences, purchase history, and operational needs.
For example, they can also gain visibility into active carts — seeing when items are sitting idle or an order is close to completion — and proactively engage the buyer to adjust quantities, resolve questions, or finalize the purchase together. The buyer receives a tailored, seamless experience that combines AI precision with human insight while accelerating the path to close.
In a hybrid commerce model, sales are no longer either/or — human or digital — it’s both, working together. Agentic commerce enables this synergy by allowing AI agents to handle repetitive, rules-based tasks while sales reps focus on strategic and consultative work.
AI agents act as digital co-pilots for sales and purchasing teams. Sales agents autonomously surface leads, nurture opportunities, trigger outreach and analyze customer history, transaction data and market trends to recommend pricing adjustments, bundles or discounts. Autonomous purchasing agents interpret sourcing requests, compare specs, validate availability, negotiate with suppliers and prepare quotes.
Humans can step in only for oversight of exceptions or high-value decisions, such as finalizing a customer’s cart and completing orders when appropriate.
This hybrid model accelerates deal closure, reduces operational friction, and delivers a highly personalized, data-driven experience for buyers — all without increasing headcount.
Boosting operational efficiency across the board
In B2B commerce, operational efficiency is mission-critical. The complexity of large catalogs, high-value transactions, regulatory constraints and multi-step, multi-stakeholder workflows makes errors costly and slowdowns expensive.
Agentic AI can dramatically reduce operational friction by automating repetitive tasks, orchestrating workflows across systems and surfacing actionable insights in real time. From predictive maintenance to inventory management, agentic systems are transforming the back office into a strategic backbone, enabling organizations to scale with confidence while maintaining precision, compliance and agility.
Predictive maintenance is one of the most tangible ways AI agents boost operational efficiency. By analyzing sensor data, usage patterns and historical service records, agents can anticipate equipment failures before they happen.
In practice, this means:
Reducing production downtime for customers, e.g., if a broken part isn’t replaced in a machine that’s critical to the process, then all operations are halted, translating into missed revenue.
Optimizing service schedules and parts inventory.
Lowering unplanned maintenance costs while extending asset lifecycles.
By automating insights and recommendations, predictive maintenance shifts operations from reactive to proactive, giving B2B teams time to focus on higher-value activities.
Complex supply chains and large, multi-location inventories make stock management a persistent challenge. Agentic AI can monitor stock levels, predict replenishment needs and even initiate orders autonomously in accordance with business rules.
Benefits include:
Minimizing stockouts and overstock situations.
Reducing manual inventory reconciliation.
Providing real-time visibility across multiple warehouses or supplier sites.
Effective inventory management infused with AI transforms what was once a reactive, labor-intensive function into a streamlined, predictive process that keeps commerce flowing smoothly while still empowering teams to stay in control.
B2B payments are notoriously complex: High-value transactions, local compliance requirements, varied payment methods, taxes and multi-party approvals create friction that slows cash flow and disrupts operations. Traditional single-provider payment solutions often struggle to handle this complexity efficiently.
AI can act as an orchestrator across the B2B payment ecosystem, streamlining:
Checkout and order approvals.
Invoicing and accounts payable/receivable.
Shared payment workflows.
According to Forrester, one-third of B2B payment workflows will leverage AI agents by the end of 2026, making payments not just a transactional step, but a strategic, automated backbone that supports operational efficiency, compliance and scale in B2B commerce.
As B2B commerce matures, the next frontier is autonomous deal-making. Agent-to-agent negotiation allows buyer and seller systems to interact directly, finalizing deals within predefined rules, optimizing discounts and accelerating transaction speed — all without (or minimal) human intervention.
Key advantages include:
Protecting margins while enabling faster decisions.
Reducing the operational load on human sales teams for routine negotiations.
Ensuring consistency, auditability and compliance.
In the future, agentic systems will execute entire negotiation flows autonomously, turning back-office operations into a highly efficient, margin-protecting engine for B2B commerce.
Preparing for the agentic economy
B2B commerce is already shifting. 89% of B2B buyers use generative AI as a top source of self-guided information, and by 2026, 20% of sellers will engage in agent-led quote negotiations. The message is clear: Agentic workflows and machine-readable product data are urgent.
AI-powered search and agentic interfaces are changing how buyers discover and purchase products. In the era of zero-click commerce, answers, recommendations and even purchase options are delivered directly by AI, making traditional SEO and website traffic insufficient.
Success now depends on Answer Engine Optimization (AEO): Structuring product information, pricing rules, technical documentation and compliance data so AI systems can interpret and trust it. Companies that master this gain preferential placement in AI-assisted procurement cycles.
Even as AI-driven discovery grows, owned experiences — eCommerce portals, mobile apps or procurement interfaces — remain critical. They provide high-value touchpoints for repeat business, upselling, contract renewals and relationship management, while maintaining control over pricing, fulfillment and customer data.
Additional benefits of adopting agentic workflows now include:
Margin protection: AI ensures discounts always comply with rules that benefit all parties.
Faster implementation: AI-ready MVPs that can be deployed in weeks, replacing multi-year replatforming cycles.
Strategic visibility: Being discoverable in AI-assisted buying workflows secures recurring business.
In short, the agentic economy is here. Businesses that prepare now are the ones that will thrive as AI reshapes how B2B buyers make decisions.
With over a decade of experience across product and marketing teams in the tech world, Julia specializes in creating innovative, customer-first strategies and driving cross-functional growth and go-to-market initiatives.
Manuela leads content strategy at commercetools. With over 20 years of experience in B2B SaaS, she writes about all things commerce by day and turns to fiction by night. She loves long walks, traveling, and, unsurprisingly, reading books.
