Artificial Intelligence Reshapes Go-to-Market Strategy with Engineers Stepping into Leadership Roles
In the ever-evolving landscape of business, a new role is making waves - the GTM (Growth Team Management) engineer. This role, set to fundamentally restructure traditional GTM organisations, is at the forefront of the automation of GTM workflows, thanks to the pioneering work of companies like Cargo.
Current GTM teams often follow an assembly-line structure, with roles such as SDRs (Sales Development Representatives), AEs (Account Executives), AMs (Account Managers), and a reactive ops team. However, the future of GTM teams is set to flatten, with the SDR/AE/AM model giving way to agile pods. In these pods, a GTM engineer owns a segment or product line end-to-end, supported by lightweight human reps and scalable AI agents.
The GTM engineer is not just another cog in the machine. They are the architects of growth workflows, responsible for designing modular, version-controlled, reusable, and testable systems. This engineering approach is crucial, as GTM is now expected to operate as a continuous delivery system, with constant deployment, testing, and iteration.
The GTM model automates data work through three foundational modules: Extraction, Enrichment, and Engagement. The Extraction Module, for instance, ingests every signal and pipes it into a clean, real-time data layer. Similarly, the Enrichment Module augments leads with data from third-party sources and scores them using custom models. The Engagement Module, on the other hand, generates personalized outreach at scale.
But what about the human touch? GTM engineers define how AI agents and humans work together. They determine who drafts, who reviews, and who owns outcomes. An experimentation mindset is essential for GTM engineers, allowing them to test, measure, and optimize like engineers, not marketers.
To achieve this, GTM engineers use a variety of tools. They connect various tools (Slack, HubSpot, Salesforce, enrichment tools, and AI agents) into a single system. Low-code integration is necessary for stitching together these tools and agents into clean, self-healing systems. SQL and data are used to build custom lead scoring, track funnel drop-off, and debug workflows.
The role of a GTM engineer also involves creating dashboards and alerts to track funnel health, agent performance, and campaign ROI. Decision APIs determine how each lead is handled based on defined rules: AI-Only, Human, or Hybrid (AI_Draft + Human_Review). Canary Launches are used to roll out new sequences, qualification models, or AI prompts to a small portion of the funnel before scaling.
Context engineering becomes vital for crafting AI instructions for agents. GTM engineers must ensure that the AI agents understand the context in which they operate, enabling them to make informed decisions and deliver personalised, effective outreach.
The founder and CEO of the company leading this revolution, Cargo, is Aurelien Aubert. Cargo is building AI agents for automating GTM workflows, helping modern revenue teams streamline their operations and focus on what truly matters - growth. With the introduction of the GTM engineer, the future of GTM is looking bright, full of automation, efficiency, and innovation.
