Why AI, Trust, and Buying Networks Demand a New GTM Playbook

Trust, AI, and the Rise of a New GTM Playbook

November 1, 2025

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The rules of B2B buying are being rewritten. Not incrementally — structurally. Buyers have never moved through a linear tidy funnel owned by sales and marketing; they navigate a buying network made up of internal stakeholders, external influencers, independent research, and increasingly, AI agents that summarize, shortlist, and recommend on their behalf.

If your go-to-market (GTM) playbook still targets a single buyer persona or relies on volume outreach, you’re invisible in the very places buyers now start their journeys.

Two big shifts are driving this:

  1. The explosion of generative AI and agentic workflows in buyer research.
  2. The growing prominence of trusted third-party nodes — analysts, peer review sites, and specialist media — that buyers and their AI assistants rely on to validate choices.

Forrester recently argued that providers must move beyond “buying groups” to thinking in terms of buying networks — an expanded ecosystem whose connective tissues are signals and trust.
(Forrester, Buying Networks: Your Buyers’ New Reality, 2025)

AI Is a Gatekeeper

Generative AI is now a primary research mode for many buyers. Forrester’s buyer research (2025) shows that nearly 89% of B2B buyers use generative AI at some point in their evaluation process — discovering vendors, comparing features, and even drafting final recommendations to internal stakeholders.
(Forrester, B2B Buyer Adoption of Generative AI, 2025)

“AI isn’t just helping buyers research — it’s becoming the gatekeeper that decides who gets shortlisted.”

That changes where and how vendors need to show up. It’s not enough to be findable by a single buyer: you must be visible and credible to the AI systems and third-party sites that feed that buyer’s decisions.

At the same time, the agentic AI story has a double edge. Companies are launching AI agent marketplaces to automate procurement and vendor evaluation — Oracle and SAP among them — but early evidence suggests up to 40% of agentic AI projects will be scrapped by 2027 as organizations learn their limits.
(Reuters, “Over 40% of agentic AI projects will be scrapped by 2027,” citing Gartner, 2025)

That mix of rapid adoption and uneven maturity makes trust and verifiable data the core differentiators for vendors that want to survive and win in agent-mediated shortlists.

Trust and Precision Data Beat Volume Outreach

As buyer journeys diffuse across networks, the old MQL-heavy model produces noise and waste. What cuts through is contextual relevance backed by verifiable outcomes — data and signals that an AI agent or an external influencer can trust and cite.

That’s where modern intent data providers and trusted media partners come in: their first-party signals make GTM actions both timely and credible. Forrester’s 2025 Wave™: Intent Data Providers for B2B found that top performers combine signal quality, noise filtering, and buying-group prediction, producing materially better results for clients.
(The Forrester Wave™: Intent Data Providers for B2B, Q1 2025)

“Trust compounds. Visibility in the networks buyers use reduces friction and accelerates deals.”

Put simply: when your content and evidence appear in the places buyers trust (and the agentic systems they use), you get shortlisted more often — and earlier.

Tactical Shifts for GTM Teams

Here are five concrete shifts GTM leaders can make immediately:

  1. Signal-First Targeting
    Move from static account lists to real-time behavioral signals — what topics people are researching, which third-party pages they read, which networks are active. Prioritize outreach where network activity is concentrated rather than blasting generic campaigns.

  2. Evidence-Led Storytelling
    Replace feature claims with outcome proof: measurable ROI, independent validation, and reproducible case data. Provide the exact snippets an AI agent would use to justify a recommendation.

  3. Network Playbooks, Not Personas
    Build engagement mapping for the external nodes that buyers trust — analysts, community leaders, review platforms — and make those nodes your allies through transparency, data sharing, and co-created content.

  4. Agent-Aware Content and APIs
    Make your assets machine-readable (structured data, JSON-LD for product facts, accessible summaries) so buyer agents can parse and cite them accurately. Treat agent interactions as a first-class channel.

  5. New KPIs for Influence
    Track network amplification metrics: third-party citations, analyst mentions, review momentum, and the share of shortlists where you appear via independent sources — not just MQL → SQL ratios.

“Move from persona playbooks to network playbooks — map the external nodes that actually influence buying decisions.”

The Long Game: Compounding Trust

Shortlists and demos are transient — trust compounds. Brands that persistently show up with transparent proof, consistent third-party validation, and machine-friendly evidence build a “visibility moat” inside buying networks.

That’s what shortens sales cycles, increases win rates, and protects margin in a world where product claims are easy to copy and AI makes discovery broader.

If you want to keep winning, stop asking how to interrupt buyers; start asking how to be the trusted signal their agent and network cite.

“Treat buyer AI as a channel: make your evidence machine-readable and independently verifiable.”

References