What is agentic commerce? Agentic commerce is a model where autonomous AI agents — acting on behalf of buyers with delegated authority — independently research vendors, evaluate options, negotiate terms, and execute B2B purchases without continuous human direction. The term was coined by Kearney to describe the shift from human-directed purchasing to AI-mediated supply chains. Unlike AI-assisted research, agentic commerce involves agents that act, not merely inform.
The B2C framing of AI commerce — autonomous shopping agents, ChatGPT recommending products, Perplexity replacing Google for consumer research — has captured the majority of marketing attention over the past eighteen months. It is the wrong frame. The more immediate and structurally consequential disruption is happening in B2B procurement, where deal sizes are larger, buying cycles are longer, and AI agent adoption is advancing faster than most CMOs have recognized.
Gartner made the scale explicit at the IT Symposium/Xpo 2025: by 2028, 90% of B2B purchases will be handled by AI agents, channeling more than $15 trillion in spending through automated exchanges. McKinsey’s parallel projection estimates $3–5 trillion in orchestrated global agentic commerce revenue by 2030. These are not speculative forecasts. Google’s CEO Sundar Pichai said in February 2026 that Google spent 2025 “working with the ecosystem to develop the underlying protocol that’s going to be needed for this agentic world,” and identified 2026 as the first year these capabilities begin appearing in real buying scenarios.
B2B vendors who have not yet structured their content, data, and API infrastructure for agent discovery are already behind the window. The brands claiming citation share in AI-mediated research in 2025 and 2026 are building structural advantages that will compound as autonomous procurement matures.
The Procurement Journey Has Already Changed
Before examining where agentic commerce is going, it is important to understand what has already happened to B2B buying — because the human behavior shift that preceded agents makes the agent transition both more plausible and more urgent.
6sense’s 2025 Buyer Experience Report, based on responses from more than 4,000 buyers across North America, EMEA, and APAC, documented the following state of B2B procurement: 94% of buying groups ranked their preferred vendors before initiating first contact with any sales team. The vendor buyers prefer before engagement still wins 77–80% of deals. The average buying cycle runs 10 months, with buyers controlling 60% of the journey before any vendor interaction. By Day One of a formal evaluation, buyers have already filled 3–4 shortlist positions — and 95% of the time, the winning vendor was already on that list.
This means that for the majority of B2B deals, the sales conversation is a confirmation process, not a persuasion process. The decision is largely made in the research phase that precedes it. And that research phase is now AI-mediated: 94% of B2B buyers used large language models during their purchasing journey, according to both 6sense 2025 and Forrester 2025 research. Among 25–34 year-old buyers — the procurement leads and evaluation committee members making shortlisting decisions today — 85% use AI for supplier research. The dark funnel has expanded from anonymous web browsing into AI platforms that generate zero CRM data, zero attribution, and zero signal for demand generation systems.
Kerry Cunningham, Head of Research at 6sense, framed the urgency directly: “Buyers are choosing a preliminary winner much earlier than they have in the past. That early choice is often driven by the need to evaluate AI capabilities — but it’s happening before vendors even know they’re being considered. The real urgency for revenue teams is to influence those early journeys before buyers reach out.”
From Research Assistant to Autonomous Buyer: The Two-Phase Transition
The transition from AI-assisted research to AI-agent procurement is happening in two phases that are running concurrently, not sequentially.
Phase 1: AI as Research Intermediary (Now)
This phase is already fully deployed. A procurement lead opens ChatGPT and asks: “What are the best demand generation platforms for enterprise B2B?” They receive a shortlist in seconds — no Google search, no clicking through ten results, no form fills on vendor websites. This interaction generates no CRM data for the vendors on the list. It generates no data for vendors absent from the list either — which is the critical asymmetry. Brands that appear in AI-generated research responses are building consideration equity. Brands that don’t exist in those responses don’t exist in that buyer’s evaluation.
The research-phase data is unambiguous: 94% of procurement teams already use generative AI tools at least once per week (6sense 2025; EY Global CPO Survey 2025). AI Overviews appear with increasing frequency in B2B informational searches, meaning buyers encounter AI-generated summaries before they encounter vendor websites. TrustRadius documented a 60% decline in analyst report consultation since 2022 — the trusted third-party validation that used to sit between vendors and buyers is collapsing, and AI search is filling the gap.
Phase 2: AI as Autonomous Procurement Agent (2026–2028)
This phase is in early deployment for specific use cases and will become mainstream by 2028 per Gartner. The operational model: a food service operator’s AI agent monitors inventory levels, identifies when a specific SKU drops below threshold, queries multiple distributors’ APIs for pricing and availability, verifies that documentation meets compliance requirements, and places the order — without a human opening a browser. Forrester predicts buyers’ procurement teams will deploy agents capable of “scaling negotiation across hundreds of suppliers simultaneously,” turning static pricing pages into dynamic negotiation interfaces.
Gartner’s companion prediction contextualizes the scale of internal deployment: by the end of 2026, 40% of enterprise applications will integrate task-specific AI agents, up from less than 5% in 2025. By 2028, AI agents will outnumber human sellers by tenfold. JAGGAER’s “Autonomous Commerce” platform, GEP Smart, SAP Ariba, and Coupa are all embedding agentic AI directly into Source-to-Pay workflows. The infrastructure for autonomous procurement is being deployed inside enterprise buyers today.
Why B2B Is More Exposed Than B2C
Several structural features of B2B commerce make it more immediately vulnerable to agentic disruption than consumer purchasing.
Buying committees, not individuals. The average B2B buying group for deals averaging $250,000 involves 10.1 members (6sense 2025). Forrester’s 2026 State of Business Buying puts the number at 13 internal stakeholders and 9 external ones for enterprise purchases. AI agents are better suited to synthesizing the information requirements of a 10-person committee than a single consumer making a discretionary purchase. They can run parallel research across multiple evaluation criteria simultaneously, normalize data across competing vendors, and produce structured comparison outputs — tasks that take committees days and AI agents minutes.
Structured criteria, not emotion. B2B procurement decisions are governed by pre-defined criteria: compliance certifications, SLA requirements, pricing tiers, integration specifications, delivery windows, and contract terms. These criteria are machine-readable in ways that consumer preference is not. An AI agent evaluating five software vendors against a procurement checklist operates with higher reliability than an agent recommending a restaurant. The more structured and criteria-driven the purchase, the more suitable it is for agent intermediation.
High-volume, repeatable transactions. The most immediate agentic commerce deployments are in tail spend and recurring procurement: MRO supplies, software licenses, raw materials, professional services with standard scope. These are high-volume, criteria-driven purchases where human review adds cost without adding value. ISG’s 2025 State of Enterprise AI Adoption study found that supplier risk assessment and monitoring has a 58% production deployment rate — the highest of any enterprise AI use case. Procurement represents just 6% of current enterprise AI use cases, but the use cases with the highest production rates are procurement-adjacent.
What AI Agents Actually Evaluate When Selecting Vendors
Understanding how procurement AI agents make vendor selections is the critical missing piece for most B2B marketing and content strategies. The criteria are fundamentally different from what influenced a human buyer reading a landing page.
Structured data completeness. Agents evaluating suppliers via API prioritize vendors whose product data is complete, consistent, and machine-readable. A supplier with a well-structured product catalog, real-time inventory APIs, and verified specification sheets is discoverable to an autonomous procurement agent. A supplier whose product information lives in PDFs and requires phone calls for pricing quotes is functionally invisible. Kearney calls this becoming an “agent-preferred supplier” — the standard for data readiness that procurement agents will filter on as they become mainstream.
AI citation authority. Before agents execute transactions, they conduct research — using the same AI search platforms that human buyers now use. The AI platforms cite 3–4 brands per response on average (BrightEdge/Amsive 2025). The top 20 domains capture 66% of all AI citations. Only 11% of B2B brands have the majority of their content in AI-discovery-ready formats, according to 10Fold’s 2025 “AI-First, Buyer-Ready” report of 400 senior marketing executives. Brands absent from AI-generated research responses during the discovery phase simply don’t surface in the agent’s shortlist.
Verifiable provenance and compliance data. Gartner specifically identified “verifiable data feeds and standardized trust frameworks” as the infrastructure that agentic procurement relies on. Agents cannot call a sales rep to verify that a supplier is SOC 2 compliant, ISO certified, or meets supply chain transparency requirements. This information must exist in structured, verifiable, digitized formats that agents can authenticate programmatically. Vendors that cannot prove provenance in machine-readable form will be filtered out before a human ever reviews the shortlist.
Content density and statistical authority. For the research phase, the Princeton GEO study found that content containing specific statistics receives a 27–36% visibility boost in AI-generated summaries. The LEADSCALE analysis confirms: “AI models prefer data-rich, clearly structured content. The brands that provide it get cited. The brands that rely on generic marketing copy do not.” In agentic procurement, the research phase determines which vendors enter the agent’s evaluation set. Generic content — vague claims about “enterprise-grade solutions” and “industry-leading results” — does not pass the citation filter.
The Infrastructure That Determines Agent Visibility
For B2B vendors, agentic commerce readiness requires work across three infrastructure layers simultaneously. These are not sequential — they must be built in parallel because the research phase (Layer 2) and the transaction phase (Layer 3) are arriving on overlapping timelines.
Layer 1: Content and Citation Infrastructure
This is the layer that determines whether your brand appears in AI-generated research during the discovery phase. Requirements: structured content with direct answers in the first 40–60 words of each section; fact density with specific statistics every 150–200 words with citations to primary sources; FAQ schema markup (pages with FAQPage schema are 3.2x more likely to appear in Google AI Overviews); entity-clear brand descriptions that AI models can parse and cite consistently; and regular content updates with visible timestamps (Perplexity weights content updated within 30 days significantly higher than older material).
Original research is the highest-leverage content investment in this layer. AI agents — both research-phase agents used by human buyers and autonomous procurement agents — preferentially cite verifiable, attributable data. Proprietary data published by your brand creates citation anchors that generic content cannot match. A benchmark report, customer data study, or industry survey with your brand’s name attached will generate AI citations that compound over time.
Layer 2: Data and API Readiness
This is the layer that determines whether an autonomous procurement agent can evaluate and transact with you. Requirements: structured, machine-readable product catalogs with complete specifications (not PDFs that require human interpretation); real-time inventory and pricing APIs that respond to programmatic queries; digitized compliance documentation — certifications, SLAs, supply chain provenance — in verifiable formats; and integration capabilities with major procurement platforms (SAP Ariba, Coupa, JAGGAER) that enterprise buyers are deploying as their agent infrastructure.
The bar for “machine-readable” is higher than most B2B vendors currently meet. Modern AI-powered catalog platforms can ingest and transform supplier data from PDFs, spreadsheets, and EDI feeds into agent-ready content — but the underlying data must first exist. Vendors whose pricing requires a quote request, whose certifications are stored in email attachments, and whose specifications live in slideware are structurally invisible to autonomous procurement agents.
Layer 3: Trust and Verification Infrastructure
Gartner’s $15 trillion forecast includes a critical dependency: “standardized trust frameworks that allow agents to negotiate, contract and execute purchases at high frequency.” Autonomous procurement agents cannot execute against suppliers they cannot verify. This layer includes: third-party review presence on platforms agents consult (G2, Capterra, TrustRadius, industry-specific directories); Wikipedia entity coverage for brands that agents use as a baseline trust signal; consistent NAP (name, address, phone) data across all directories that AI models draw from; and press coverage in credible publications that AI systems treat as independent validation.
Only 24% of consumers currently trust AI recommendations for autonomous purchasing (Forrester). The trust gap is wider in B2B, where procurement failure carries higher organizational consequences. Vendors that build the verifiable trust infrastructure now — before autonomous procurement reaches mainstream scale — will have a compounding advantage as agent adoption accelerates.
The Strategic Timeline
Gartner’s 2028 projection is not an endpoint — it is the midpoint of a transition that is already underway. Google indicated 2026 as the year agentic procurement capabilities begin appearing in real buying scenarios. EY’s 2025 Global CPO Survey found 80% of chief procurement officers plan to deploy generative AI within three years, with initial focus on spend analytics and contract management — the stepping-stone functions before full agentic procurement. The Hackett Group’s 2025 Key Issues Study found 64% of procurement leaders expect AI to fundamentally reshape their function within two years.
The strategic implication for B2B vendors is a closing window, not a future planning exercise. Citation patterns in AI search are consolidating: the top 20 domains already capture 66% of AI citations, and early research by ZipTie.dev and others suggests that citation dominance, once established, is difficult to reclaim — AI models reinforce what they have already learned. Building AI citation authority and agent-ready data infrastructure now yields compounding returns. Waiting until agentic procurement reaches 50% adoption means competing for citation share that will already be structurally dominated.
Topic Intelligence™ was built to address exactly this problem — mapping the topic surface that AI systems pull from, tracking which brands earn citation authority in which categories, and surfacing the content and data gaps that determine whether a vendor appears in AI-mediated research. The $15 trillion question for B2B vendors is not whether agentic commerce is coming. It is whether your brand will be in the answer when an agent asks.
Frequently Asked Questions
What is the difference between AI-assisted B2B purchasing and agentic commerce?
AI-assisted purchasing uses AI tools like ChatGPT as research aids — human buyers query them and use the outputs to inform their decisions, but humans retain full control. Agentic commerce involves AI agents that act with delegated authority: they autonomously research vendors, evaluate options against predefined criteria, negotiate terms, and execute purchases without continuous human oversight. Kearney coined the term to describe this shift from AI as tool to AI as buyer. Gartner projects 90% of B2B purchases will be agentic by 2028; the current period represents the transition between these two modes.
Why is B2B more exposed to agentic commerce disruption than B2C?
B2B procurement decisions are governed by structured, verifiable criteria — compliance certifications, SLA requirements, pricing tiers, specification sheets — that AI agents can evaluate programmatically. Consumer preferences involve taste, emotion, and social context that are harder to automate. Additionally, B2B buying groups averaging 10.1 members (6sense 2025) face coordination costs that AI agents reduce significantly by parallelizing research and normalizing comparison data. High-volume, repeatable B2B transactions — MRO supplies, software licenses, recurring services — are the most immediate deployment targets.
What does an AI procurement agent actually evaluate when selecting vendors?
Procurement agents evaluate four primary dimensions: structured data completeness (machine-readable product catalogs, real-time APIs for inventory and pricing), AI citation authority (whether the vendor appears in AI-generated research during the discovery phase), verifiable provenance and compliance documentation (certifications and SLAs in digitized, authenticable formats), and content density (specific statistics and data-rich descriptions that AI systems can cite rather than generic marketing language).
How do I make my B2B business visible to AI procurement agents?
Three infrastructure layers are required simultaneously: content and citation infrastructure (structured content with FAQ schema, original research, specific statistics, regular updates); data and API readiness (machine-readable product catalogs, real-time pricing and inventory APIs, digitized compliance documentation); and trust and verification infrastructure (third-party review presence, Wikipedia entity coverage, consistent directory data, credible press coverage). Brands absent from any layer risk being filtered out before a human ever reviews the shortlist.
When will agentic procurement reach mainstream B2B adoption?
Google identified 2026 as the first year agentic commerce capabilities begin appearing in real B2B buying scenarios. Gartner projects 40% of enterprise applications will integrate task-specific AI agents by end of 2026 (up from less than 5% in 2025), and 90% of B2B purchases will be agent-intermediated by 2028. EY found 80% of CPOs plan to deploy generative AI within three years, with procurement infrastructure spending accelerating. The transition is running on overlapping timelines — research-phase agents are deployed now; transaction-phase agents are in enterprise pilots today and mainstream deployment by 2027–2028.