Best of LinkedIn: Go-to-Market CW 24/ 25

Across the selected LinkedIn activity, GTM shifted from tool-led automation toward a broader redesign of revenue operating models. AI is becoming the infrastructure layer for context, data, workflows and execution, while teams with disciplined GTM foundations are best positioned to turn automation into sustained growth.

Date

June 25, 2026

Go-to-Market

Thomas Allgeyer

Methodology: Every two weeks we collect most relevant posts on LinkedIn for selected topics and create an overall summary only based on these posts. If you´re interested in the single posts behind, you can find them here: https://linktr.ee/thomasallgeyer. Have a great read!

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If you prefer listening, check out our podcast summarizing the most relevant insights from Go-to-Market CW 24/ 25:

AI-Native GTM Systems

GTM shifted from prompt experimentation to repeatable AI systems built around context, skills, orchestration, and workflow integration
Claude was positioned as a multi-layer GTM workspace, covering daily work, repeatable projects, collaboration, and execution workflows
New tools and concepts included Claude LinkedIn GTM Skillset, Claude Code GTM setup, GTM.AI, GTM Agent OS, PMM Sherpa MCP connector, GTM Fields, and AI Readiness Scan
Context engineering emerged as a key differentiator as generic AI-generated outreach and content became easier to detect

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GTM Engineering

GTM Engineering matured into a core revenue capability linking RevOps, data, automation, sales, and customer success
The strongest guidance focused on starting with the business problem before selecting workflows, tools, or automation logic
Required capabilities expanded across Clay, n8n, APIs, CRM logic, enrichment, routing, signal detection, SQL, and Python
The talent market remained immature, with role clarity, technical standards, and end-to-end experience becoming critical hiring filters

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GTM Strategy

GTM strategy centred on stronger foundations before acceleration, especially around ICP clarity, positioning, pricing, workflow fit, and market selection
Poor GTM performance was linked to upstream issues such as vague ICPs, weak beachhead segments, messy data, and broken customer journeys
CEOs and founders were framed as owners of the GTM system, rather than sponsors of disconnected sales, marketing, and customer success activities
Sector-specific GTM gained importance, with MedTech and banking examples highlighting the need for regulatory, reimbursement, workflow, and revenue-model discipline

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Signal-Led Outbound

Outbound effectiveness was framed as an infrastructure challenge, not a copywriting challenge
Deliverability, inbox setup, domain health, data quality, enrichment, verification, intent targeting, and sequencing became core execution topics
Signal-led outbound stood out as a higher-quality alternative to static list-based prospecting
Relevant signals included website visitors, product usage, LinkedIn engagement, CRM activity, call transcripts, champion movement, ad engagement, review sites, job openings, funding, technographics, and firmographics

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Sales Enablement & Launches

Sales enablement moved toward shorter, more usable field assets such as cheat sheets, pitch videos, and battlecards based on real objections
Sales leadership quality was linked to quota performance, with coaching discipline positioned as more important than deal inspection alone
Product launch readiness was framed as a GTM discipline, not an engineering release milestone
Common launch risks included unclear launch tiers, weak resourcing, late stakeholder blockers, sales teams lacking the story, and insufficient readiness planning

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Ecosystem GTM

AWS was positioned as a GTM amplifier for ISVs through co-sell, co-marketing, Marketplace, and AI competencies
GTM.AI connected ZoomInfo intelligence into AI agents and platforms including Claude, ChatGPT, Salesforce Agentforce, HubSpot Breeze, and Microsoft Copilot
PMM Sherpa’s MCP connector added product marketing judgment into Claude projects and AI-enabled launch workflows
Events highlighted the rise of agent-driven GTM, programmatic workflows, context graphs, production AI systems, and agent-to-agent buying motions

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New Offerings

GTM.AI launched as a headless GTM context layer connecting ZoomInfo intelligence to agentic workflows and GTM platforms
GTM Agent OS became open-source, bringing together skills, agents, sales triggers, email templates, CLI tools, and flywheel workflows
ScaleMate introduced GTM Engineer as a Service for Series A and later SaaS companies
GTM Fields launched as an AI-native advisory model for GTM, integrated marketing, and B2AI strategy

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Strategic Takeaways

GTM advantage is shifting from tool ownership to system design
Winning teams combine clean data, clear ICPs, strong context, signal-led workflows, and human judgment
AI creates leverage only when the underlying GTM motion is already defined
The next phase of GTM will be shaped by revenue systems that learn, execute, and improve continuously

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