The proof that GTM engineering became a budgeted function, not an agency fad: 228 operators across 30-plus countries, US median around
The best framework I have read on GTM engineering: not automating existing processes, but rebuilding the motion around signals, agent orchestration and autonomous pods. "Systems beat tactics" — agreed, that is exactly the shift from gut feel to a repeatable system. The part the operating system cannot encode is the trust that carries a tier-1 OEM to signature over months. The system delivers the pipeline; a human wins the deal.
The most concrete enterprise proof yet: monday.com runs three agents for inbound qualifying, trial activation and account planning. Demo response from 24 hours to under 2 minutes, trial conversion 2.5x, account research from one to two weeks down to five minutes. And still: the agents augment, they do not replace — the rep gets qualified meetings with the context prepared and keeps strategy and the relationship. Exactly the model I argue for, proven here at public-company scale.
The numbers behind "necessary, not sufficient": autonomous AI SDRs fall short, hybrid pods make roughly 2.3x the revenue on fewer meetings, and 50 to 70% of AI SDR deployments churn within a year. "Data plumbing beats prompts" — the data foundation decides, not the model. In high-ACV industrial deals the split is settled anyway: AI does volume and research, the human owns judgment. The lesson from 2025’s exposed vendor claims: check post-trial retention, not the demo.
A clean map of what my buyers actually weigh: buying agentic AI is not software procurement — trust and vendor lock-in compound because the model shapes autonomous decisions. Waehner argues for open standards, multi-model and MCP to stay interoperable. For GTM that means: selling into industrial enterprises requires speaking trust and no-lock-in, or the engineered funnel never reaches procurement. It is exactly why I advise vendor-neutral — the edge is sitting on the buyer’s side of the table.