Where AI actually works in go-to-market.

Most "AI in GTM" is a chatbot on a pricing page. The version that moves revenue looks different: agents embedded inside the revenue workflow, doing the repetitive knowledge work that used to eat SDR and ops hours — research, enrichment, qualification, triage, briefing.

The pattern that works: find a step where a human reads something, forms a judgment and writes it down — then make an agent that does exactly that step, and wire its output into the next system. Nothing more. The narrower the job, the better the agent.

Where agents earn their keep

Account research. An agent that reads a company's site, news and hiring signals answers qualification questions no static database can. That's how 60k accounts stay scored on fit and intent — continuously, at a cost no SDR team could match.

Outbound triage. Identifying which website visitors are worth pursuing, finding the right contacts, drafting the first touch — with a human approval gate so quality never slips. The human decides; the agent does the legwork.

Briefing. Before an exec walks into a meeting, an agent has already assembled who they're meeting, why they matter and what to say. Event campaigns run on this.

Where it fails

Agents fail where judgment is the product — negotiation, relationship, strategy. And they fail quietly when nobody owns their output quality. Every agent in production needs an owner, an eval, and a human gate at the decision that matters. That's GTM engineering: the discipline that makes AI dependable inside revenue systems.