The age of AI agents in B2B purchasing

The phrase AI agents in B2B purchasing captures a looming shift. Gartner told the world that AI agents are on track to intermediate more than $15 trillion in B2B purchases by 2028. Therefore, businesses that sell to enterprises and those that buy from suppliers must treat this as an urgent strategy question. This piece walks through the evidence, practical use cases, and immediate steps leaders can take. Additionally, it ties together signals from healthcare routing, marketing organization, and retail to show what works — and what risks to avoid.

## Gartner's Wake-Up Call: AI agents in B2B purchasing

Gartner’s projection is a clear market signal: software that can represent a buyer or seller — an AI agent — is not niche. Instead, it is poised to become a primary channel for decision-making and transaction flow in B2B markets. The forecast puts a dollar figure on that change: more than $15 trillion by 2028. That scale means procurement platforms, marketplaces, and ERP systems will need to adapt. Otherwise, they risk becoming invisible to autonomous buyers.

For vendors, the message is twofold. First, digital catalogs and APIs must be designed for machines as well as humans. Second, trust and provenance become table stakes because agentic systems will favor reliable, structured data. Therefore, companies should audit their product metadata, SLAs, and integration points now. Additionally, buyers must rethink how contracts and approvals work when an AI agent can negotiate or recommend purchases on behalf of people.

This is not only a technology change. It is a business model shift. Legacy procurement processes can become bottlenecks if they assume human-only workflows. However, firms that design clear agent interfaces and guardrails will unlock faster, more consistent buying. The immediate outlook is active: in the next 12–36 months, early adopters will test agentic sourcing and see productivity and speed gains. Longer-term, entire B2B ecosystems will rewire around agent-to-agent interactions.

Source: Digital Commerce 360

AI agents in B2B purchasing — a healthcare routing use case

Healthcare gives a vivid, practical example of how AI agents can change workflows. Today, patients contact providers across chat, phone, and portals. However, context often disappears when a case moves channels. Notes don’t carry over. That creates friction and risk. Therefore, routing and orchestration that preserve context are critical. AI agents can sit between the patient and the care team to collect structured information, prioritize cases, and hand off complete context to the right clinician or system.

In practice, an agent could triage a symptom report, check scheduling constraints, and then book a follow-up with a clinician — all while pushing a full, structured summary into the electronic record. This reduces redundant questions, speeds response time, and improves patient experience. Additionally, agents can enforce guardrails: they can escalate red flags to humans and avoid unsafe autonomy.

For enterprise buyers in healthcare, the lesson is clear. AI agents must be integrated into interoperability plans, not bolted on. However, integration is only useful when data flows cleanly and teams agree on ownership. Therefore, clinical leaders must partner with IT and procurement to define what an agent can do. They should also specify failover paths and audit logs for transparency.

Finally, healthcare shows how agentic automation can drive measurable operational gains. Early deployments will focus on routing and triage. However, over time, agentic orchestration can touch scheduling, billing, and supply ordering — creating efficiencies across the system.

Source: CX Today

AI agents in B2B purchasing and the cost of marketing silos

When organizations adopt AI agents in B2B purchasing, internal structure matters. MarketingDive highlights a persistent problem: organizational silos. These silos hinder data sharing, slow decision-making, and reduce the value of automation. Therefore, the arrival of AI agents makes integration and cross-team alignment more urgent than ever.

AI agents require consistent data and clear ownership. For example, if product metadata, pricing rules, and campaign messaging are fragmented across teams, an agent’s recommendation will be inconsistent or incorrect. Additionally, procurement and sales processes must align with marketing signals so agents can respond to promotions and contractual terms accurately. If they do not, automated systems may miss opportunities or create compliance risks.

The practical response is organizational, not only technical. Marketing, sales, product, and operations need shared governance for data and automated behaviors. Therefore, firms should map ownership of key artifacts: master product records, customer contracts, and pricing rules. They should also create cross-functional review cycles that include compliance and procurement. This reduces the chance that an agent acts on stale or partial information.

In short, AI agents magnify the consequences of silos. However, they also create an incentive to fix them. Organizations that integrate data and align teams will get faster, more reliable automation. Conversely, those that cling to old boundaries will find agentic systems amplifying their weakest links.

Source: Marketing Dive

Retail signals from Black Friday: channels, giveaways, and digital strategy

Black Friday 2025 offers timely lessons for digital commerce leaders as agentic systems emerge. According to Marketing Dive coverage, online discounting was flat year over year. However, some retailers succeeded with exclusive giveaways that drove in-store visits. This outcome shows that tactics matter. Therefore, companies should rethink how promotions are structured in a world where AI agents could evaluate offers automatically.

If AI agents act as buying proxies, they will compare not just price but fulfillment, exclusivity, and experience. An exclusive giveaway that requires in-store pickup becomes more than a discount; it is a differentiator. Additionally, agents will value clear, machine-readable signals about inventory and promotion mechanics. Without such signals, an agent might overlook an offer that is compelling to a human customer.

For retail leaders, the takeaway is practical. First, ensure promotional terms and inventory are available in structured formats. Second, design omnichannel experiences that agents can reason about — for example, how a pickup promotion affects total cost and timing. Third, test incentive structures that reward desired behaviors, such as store visits or higher-margin bundles.

Therefore, Black Friday is not dead; it is evolving. Promotions that rely on surprise or human persuasion will be less effective as agents make rational trade-offs. However, creative tactics that change the calculus — like giveaways or bundled experiences — will still win. Retailers who publish clear, consistent signals will benefit when AI agents start evaluating options at scale.

Source: Marketing Dive

Why Black Friday is limited for B2B sellers

Not every trend in B2C translates to B2B. Digital Commerce 360 reports that Black Friday is becoming more visible in B2B ecommerce, but Core dna warns that only a slice of B2B sellers should engage. There are clear constraints in B2B: elevated tariffs, margin pressure, and long buying cycles. Therefore, a one-size-fits-all holiday promotion often misses the mark.

B2B purchases frequently involve contracts, approvals, and inventory planning. A temporary discount can disrupt supply chains or erode negotiated pricing. Additionally, agentic buyers will weigh long-term terms and service commitments, not just a sticker price. Therefore, short-term promotions must be structured to respect commercial realities. For example, targeted incentives on consumables or first-time orders with defined fulfillment terms may work. However, blanket price cuts on large-ticket items can backfire.

Enterprises should also consider how agents will interpret and act on promotions. Agents programmed to minimize total cost of ownership will flag offers that are tempting but costly in the long run. Therefore, B2B sellers must present offers with structured metadata around lead times, warranties, and replenishment schedules. This helps agents evaluate the true value.

In short, Black Friday can be a tool for some B2B sellers, but it is not a universal strategy. Instead, firms should design promotions that align with procurement cycles, margin needs, and agent-friendly data formats. This reduces risk and preserves long-term relationships while still capturing seasonal demand.

Source: Digital Commerce 360

Final Reflection: From agents to channels — a practical roadmap

The five stories together make a clear narrative: AI agents in B2B purchasing are not a distant idea. Gartner’s $15 trillion forecast forces strategic action now. Healthcare routing shows immediate, human-centered wins when agents preserve context and triage sensibly. Marketing silos reveal the organizational work required to make agents reliable. Retail’s Black Friday outcomes show which incentives still matter. Finally, B2B caution on holiday promotions reminds us that not every tactic transfers across markets.

Therefore, leaders should pursue three parallel tracks. First, fix the data: product, pricing, and contract metadata must be structured and governed. Second, redesign processes: define what agents can do and where humans must intervene. Third, realign teams: break down silos so automated systems act on trusted, complete information. Additionally, test early with bounded pilots — for example, routing in healthcare or targeted retail incentives — to learn quickly.

The future will favor firms that treat agents as partners, not threats. They will gain efficiency, consistency, and speed. However, these gains require deliberate design, governance, and cross-functional cooperation. In short, the age of AI agents offers huge opportunity. However, realizing it depends on the hard, often unglamorous work of integration and organizational change.