The GTM Jobs Index

The 43 jobs of go-to-market

Just as tractors pulled people out of fields and created new industries, GTM Superintelligence will pull people out of the grind and create room for strategy, creativity, and trust. We won't need human armies dialing lists or stitching spreadsheets. We will need taste and judgment. The human edge that sets intent and guardrails while systems do the rest.

Our job is to build the bridge. The OpenGTM jobs site reflects this future: roles for agents to execute and roles for people to direct, elevate, and create. Less wasted human potential. More GTM Superintelligence.

Methodology

We built the GTM Jobs Index by decomposing go-to-market into its underlying units of work rather than its organizational functions. Starting from OpenGTM's GTM ontology, we analyzed how hundreds of modern GTM teams actually create, enrich, prioritize, route, execute, measure, and optimize revenue work across sales, marketing, customer success, partnerships, operations, and executive leadership. Rather than treating roles like SDR, AE, RevOps, or Demand Generation as the atomic unit, we decomposed every workflow into discrete jobs-to-be-done with a single measurable objective. After normalizing overlapping activities and removing organization-specific implementations, the result was a canonical set of GTM jobs that represent the fundamental work performed across virtually every modern revenue organization.

Each job is defined independently of job titles, software vendors, reporting structures, or departmental boundaries. Organizations assign these jobs differently, but the underlying work remains remarkably consistent. The index therefore measures the work itself, not the people or teams currently responsible for performing it.

To ground the taxonomy in observed AI usage, every GTM job was mapped to the occupational task categories used in the Anthropic Economic Index (AEI). This mapping provides an external calibration point that connects OpenGTM's domain-specific ontology to a large-scale empirical dataset describing how frontier AI systems are already being used across knowledge work. Where multiple AEI task categories contributed to a single GTM job, their observed usage patterns were combined to produce a weighted reference profile. This grounding helps ensure that the GTM Jobs Index reflects both the structure of enterprise go-to-market work and the reality of how AI is being applied in production environments today.

For every GTM job, we evaluated two performers using the same 1–10 capability scale: today's frontier AI agents and an experienced human practitioner. AI performance reflects extensive hands-on evaluation using leading frontier models and agentic workflows completing the underlying tasks required by each job under realistic production conditions, not benchmark prompts or isolated demonstrations. Human performance represents the expected success rate of an experienced operator performing the same work within a modern GTM organization. The same methodology was then applied to estimate expected frontier AI capability in 2027 based on observed model progress, infrastructure improvements, agent architectures, and the increasing availability of structured business context.

To understand the economic impact of AI, not simply its technical capability, we estimated the relative cost of performing each GTM job within a representative enterprise revenue organization. Rather than measuring salaries by department, we modeled the fully loaded human effort consumed by each job regardless of where it sits organizationally. This allows the index to distinguish between jobs that are highly automatable but economically insignificant and jobs that consume substantial GTM investment despite remaining difficult to automate. Cost weights represent modeled averages derived from OpenGTM's GTM ontology, enterprise workflow analysis, organizational teardowns, implementation experience across large revenue organizations, and calibration against the Anthropic Economic Index. The result is a framework that reflects both where GTM organizations invest resources today and where frontier AI is already demonstrating meaningful economic utility.