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!
If you prefer listening, check out our podcast summarizing the most relevant insights from Go-to-Market CW 06/ 07:
GTM Stacks
Lean GTM stacks replace tool sprawl with four core systems that connect data, outreach and CRM workflows
Early-stage operators share transparent stack blueprints that balance affordability with high signal quality in prospecting
Structured stack audits remove redundant tools, improve data quality and speed feedback loops between product and revenue teams
Intentionally designed stacks support localized go to market plays that generate meaningful pipeline without large budgets
AI and Automation
AI copilots move from experiments to embedded workflows that automate enrichment, routing and outreach steps in GTM
Operators mix deterministic rules with AI logic to keep control of sensitive tasks while scaling automation
Cheaper data and curated tutorials lower the barrier for teams to adopt AI tools and iterate on workflows quickly
Domain specific judgment remains critical to steer AI outputs, maintain relevance and avoid amplifying noisy signals
GTM Engineering
GTM engineering emerges as a defined discipline with dedicated schools, communities and visible role models for operators
Teams treat workflows and automations as versioned assets, using repositories and documentation similar to software engineering
Revenue operations broadens from reporting to owning process design, handoffs and predictable revenue structures
Marketplaces and hiring debates show demand for clear role definitions and realistic expectations around GTM engineer impact
Revenue Systems
Go to market strategy shifts from isolated campaigns to full revenue systems with clear ICPs, funnels and shared definitions
Leadership teams confront misalignment between product and GTM as a key drag on AI startup growth and fundraising
Data driven playbooks surface pricing gaps, discounting patterns and conversion levers that can be fixed without new features
Probabilistic planning and faster decision cycles replace static forecasts, helping teams act on signals before competitors
Community and Learning
Curated tutorial lists and stack breakdowns turn fragmented GTM tool knowledge into structured learning paths
Operators publish full engines and weekly drops that show complete systems instead of isolated tactics, accelerating peer learning
Podcasts, newsletters and events become primary channels for sharing GTM experiments, benchmarks and AI workflows in real time
Structured programs for GTM engineers sit alongside community content, reinforcing that continuous education is part of the role
Want to see the posts voices behind this summary?
This week’s roundup (CW 06/ 07) brings you the Best of Go-to-Market:
→ 72 handpicked posts that cut through the noise
→ 34 fresh voices worth following
→ 1 deep dive you don’t want to miss