Hot take: AI is making most GTM teams faster at the wrong things

Six months of watching how different teams actually use AI in their GTM motion has produced one consistent pattern: the teams getting the worst results are moving the fastest.

They've wired up Claude or a competitor to auto-generate cold emails at scale. They're running 500 sequences a month where they used to do 150. They're producing three times as much content. The output numbers look great in the dashboard.

The conversion rate is down.

The issue is that the bottleneck in most GTM motions was never speed. It was signal quality. Knowing which accounts are actually in-market, which pain point to lead with, which decision-maker has budget authority right now. AI doesn't help with any of that if your underlying ICP definition and intent signals are weak. It just automates the part that wasn't the problem.

The teams I've seen use Claude effectively in GTM are doing something different. They're not using it to produce more — they're using it to research faster and say less. One RevOps lead I know uses Claude to synthesize 10-K filings and recent press releases before any enterprise call. Another uses it to cut a 500-word sales email down to 120 words by identifying which three sentences have actual signal and which are filler.

The output is fewer, better touches. Not more of everything.

AI GTM adoption metrics — emails sent, sequences launched, content pieces published — are largely vanity metrics. The real signal is whether your reply rate, meeting rate, and qualified pipeline per rep are moving. For most teams I've seen adopt AI tools in the last year, those core numbers haven't budged.

Curious what others are seeing: has AI actually moved your core GTM metrics, or mostly just your activity metrics?