Agentic Commerce: Gartner's Framework and G2's 2026 Data on Autonomous Procurement
A massive structural shift is underway in B2B procurement as enterprise buyers transition from "assistive AI" (where humans use AI tools to research vendors) to "agentic procurement" (where AI agents act as active intermediaries in the discovery, evaluation, and transaction phases). This evolution is redefining how brands must present their product data to be "read" and recommended by autonomous systems.
Gartner’s Agentic Commerce Imperative
In its January 15, 2026 report, Optimize Product Data for Agentic Commerce, Gartner outlined the scale and urgency of this transition:
- The Transaction Forecast: Gartner predicts that by 2030, 20% of all digital commerce transactions will be executed through AI platforms using on-platform check-out or by AI agents.
- The Traffic Surge: The shift is already accelerating in the consumer and business-to-business landscapes. Digital commerce site traffic originating from AI platforms was up 805% YoY on Black Friday in 2025, indicating that buyers are rapidly routing their intent through AI interfaces rather than traditional search engines.
- The Readiness Gap: Gartner warns that most organizations are unprepared because their product and service data is too incomplete or unstructured for AI platforms to accurately interpret, evaluate, and recommend.
To bridge this gap, Gartner outlines four categories of product data that AI platforms draw from:
- Master Data: Identifiers, dimensions, materials, compliance certifications, and country of origin.
- Non-Master Data: Pricing, inventory, marketing descriptions, return policies, and lead time.
- Semantic and Outcome-Based Data: Use cases, problems solved, benefits, and product ontology. Gartner notes this is where most brands have their biggest gap.
- Organizational Data: Sustainability commitments, mission, and geographic presence. As AI platforms learn preferences and develop memory capabilities, they seek and return this information to match buyer values.
G2’s 2026 Data: How Buyers Deploy AI Agents
While the long-term vision of agentic commerce involves autonomous agent-to-agent negotiation, G2’s July 2026 report, The Evaluation Maze, reveals how B2B software buyers are actually deploying AI agents in real-world procurement today. Buyers are heavily using agents for analytical and research tasks, but are keeping a tight leash on purchasing authority.
- Agent Adoption: 61% of B2B software buyers currently use or plan to use AI agents as part of their software buying process, with an additional 19% open to using them for select use cases.
- Top Agent Use Cases: Buyers are leveraging agents to bypass manual spreadsheets and analysis:
- Evaluating Total Cost of Ownership (TCO): 51%
- Building Shortlists: 51%
- Researching Solutions: 49%
- Evaluating Shortlisted Vendors: 46%
- The Autonomy Guardrail: Enterprise buyers are highly conservative when it comes to financial delegation:
- Research & Recommend Only: 47% of buyers would allow an AI agent to conduct research and make recommendations while humans retain all final decision-making authority.
- Guardrailed Purchasing: Only 9% are comfortable letting AI agents execute purchases within approved guardrails.
- Fully Autonomous Purchasing: Just 2% would allow AI agents to make purchases without pre-approval.
Strategic Takeaway for B2B Founders
For founders selling to enterprises, "agentic readiness" is now a core GTM requirement. Because 51% of buyers are using agents to evaluate TCO and build shortlists, your software’s documentation, pricing models, and case studies must be structured in a way that AI agents can easily parse and synthesize. This requires moving beyond high-level marketing copy and exposing structured, semantic, and outcome-based data (e.g., clear API schemas, explicit pricing tiers, SOC 2 compliance details, and quantified ROI metrics) on your public web channels. If an agent cannot parse your TCO or security posture, your brand will be filtered out of the shortlist before a human ever sees it.