Best of LinkedIn: Go-to-Market CW 50 - 01

GTM Engineering is positioned as a distinct discipline that designs revenue systems end to end, not just ops support

The role blends automation, data integration, CRM ownership and AI literacy, creating a high pressure but high leverage profile

Clear expectations, documentation and measurable outcomes are needed to protect GTM Engineers from burnout and churn

Career paths evolve toward Revenue Engineer and RevOps leadership, signalling that systems ownership is becoming a senior mandate

AI success in GTM is defined as rapid impact using both external and first party data, not experimental pilots

New assistants and agent-based systems automate prospecting and workflows while keeping messaging consistent and on brand

Teams shift from traditional SaaS structures to AI native GTM engines that optimise acquisition, retention and efficiency

Human AI collaboration remains central, with strategy and communication quality staying firmly in human hands

Strong GTM strategy is highlighted as the main driver of startup survival, often more decisive than product quality

Missteps such as weak conviction, fuzzy ICP and poor segmentation are identified as root causes of stalled growth

Jobs to be Done targeting and Total Relevant Market thinking replace broad persona based and TAM centric approaches

Scorecards, prioritisation matrices and conviction stacks formalise how teams decide where to focus go to market resources

Contributors advocate a move from data driven to data led, using fewer but more meaningful signals in GTM decisions

Modern revenue systems are built around clean CRM cores, integrated tools and simple, trusted dashboards

RevOps is pushed to balance experimentation speed with coherence, governance and predictable pipeline health

Analyses of GTM organisations expose misalignment and wishful math, prompting calls for zero based redesigns of systems and reports

Partner choices are treated as strategic levers that must fit the GTM motion rather than generic channel add ons

Events are reframed as engineered trust accelerators, designed with data, revenue attribution and clear follow up

Simple, human outreach on LinkedIn proves more effective than complex sequences, especially when tightly aligned to audience needs

Trust ecosystems emerge that combine personalised social presence, behavioural email and AI assisted negotiation into one experience

New platforms integrate AI with market and first party data to create a single shared view for GTM teams

Assistants such as Apollo’s AI layer streamline processes without adding complexity to already crowded stacks

Agencies reposition from pure outbound services to full funnel GTM partners built around flywheel and product led motions

Acquisitions in the GTM space signal that engineering the revenue system is becoming a stand alone service category

Sales and marketing leaders are urged to provide orientation, clear strategy and stable models instead of constant tactical shifts

GTM failures are often traced to misalignment and outdated operating assumptions, not to lack of effort from teams

Operators are encouraged to act as entrepreneurial founders inside the company, testing and iterating within defined guardrails

Communities, podcasts and recurring GTM forums become key support structures for leaders navigating AI change and RevOps transformation