It helps most where the task is high volume, low judgment, and already has a clear right answer: transcribing and summarising calls, drafting first pass emails, enriching and deduping records, flagging deals that have gone quiet, and pulling the data a rep would otherwise have asked ops for. Gong's analysis across more than a million opportunities found meaningfully higher win rates when reps actually completed AI suggested next steps and when AI was used to research accounts before a call. Where it is still mostly theatre is fully autonomous outbound at scale and any AI generated forecast you cannot explain to your board line by line. The honest test is whether a human reviews the output before it reaches a customer or a number. If nobody does, you have not automated the work, you have automated the risk.
3 hand-picked resources, 3 link-checked. Pick how you want to dig in.
Why we picked it Measured across more than a million opportunities at 1,418 companies, so it is actual behaviour data rather than a vendor survey. Useful for separating the AI features that move win rates from the ones that just look good in a demo.
From Gong Labs by Dan Morgese 10 min read
Open gong.io →Why we picked it Poyar counted the actual job postings rather than the LinkedIn noise, which is the most useful sanity check available on whether the GTM engineer role is real yet. The free preview alone is worth reading before you open a req.
From Growth Unhinged by Kyle Poyar 12 min read
Open kylepoyar.substack.com →Why we picked it Gives you a scoring framework for deciding which tools survive the next renewal, and argues convincingly that four functional layers is enough. Read it with a spreadsheet of your current contracts open.
From Unify by Austin Hughes 13 min read
Open unifygtm.com →How revops and the gtm stack reads from a different seat.
