The GTM teams getting the most from AI are building engines, not completing tasks
92% of Claude Code users say it saves time. That's the floor. The ceiling is what 67% report. Workflows that were previously impossible.
A survey of 200 GTM operators who use Claude Code or Cowork found that 92% say the product saved them time. That’s the expected result and the wrong thing to optimise for.
Why is time saved the wrong thing to optimise for?
Of the 200 operators surveyed, 67% said they built GTM workflows that were previously impossible, a different category of outcome from the 92% who report time savings. Faster is incremental. Previously impossible is a structural change in what the team can do. The split comes down to what each group is building.
What’s the difference between GTM tasks and GTM engines?
Teams reporting time savings use AI for tasks: faster drafts, quicker research, summarised calls. Valuable, but bounded. The task completes, the time is saved, then the next task starts. Teams reporting previously impossible outcomes build GTM engines: signal-based outbound sequences, tools built for SDRs, messaging adapted dynamically by segment. Engines run continuously.
When asked which use case had the biggest GTM impact, the top answer among Code users was GTM engines and prospecting.
What does signal-based outbound look like as an engine?
The signal fires, the system enriches the account, scores the signal against the ICP, routes it to the right rep with a pre-drafted message and account context, and triggers a parallel ad sequence to the buying committee. The system runs whether the rep checks it or not.
The task-based version of the same motion: a rep gets an alert, reviews it, decides whether to act, drafts a message, and sends it. The AI might help with the draft. The sequence depends on the rep checking the alert.
What does building a GTM engine require?
A GTM engine takes more setup than a task: real context engineering, meaning connected live data sources, workflow logic, and routing rules. Most teams won’t do it. The ones that do get infrastructure that runs without manual input, improves as signals correlate with outcomes, and compounds as the team adds context over time.
The question worth asking when evaluating where to use AI: is the goal to do this task faster, or to make this motion run without someone manually driving it?