What AI genuinely helps with, where it breaks, and what we've stopped shipping because it didn't work in real teams.
Everyone's shipping AI. Not everyone's shipping AI that helps. Here's our honest read.
Where AI genuinely helps
- Summarizing long call recordings. Reps get the key points, objections and next steps without replaying the whole call.
- Drafting replies. The rep reviews and sends — much faster than writing from scratch.
- Language translation. Now good enough to help with multilingual outbound, with a fluent human reviewing anything important.
- Conversation scoring. Not perfect, but good at flagging calls a manager should listen to.
- First-touch AI voice calls. Fast, consistent qualification calls for new inbound leads, with a clean hand-off to a rep.
Where AI breaks
- Autonomous outbound at scale. Buyers can tell, and response rates suffer.
- Auto-classifying leads. Language models can be confidently wrong on messy data. Keep a rep in the loop.
- Long-form content generation. Buyers can tell that too.
What we stopped shipping
- AI-generated meeting notes without transcript review. Too many hallucinated details.
- AI-suggested deal scores. Reps ignored them because reps knew the deals better.
How to evaluate an AI feature for your team
Ask what job it replaces. If the answer is vague, it's probably a demo feature.
Check where a human reviews it. Anything that reaches a customer should have a clear review step.
Measure time saved, not "AI usage." The goal is more selling time, not more AI clicks.
Turn it off if nobody misses it. The best test of a feature is whether people complain when it's gone.
The rule we settled on
AI drafts. Humans decide. Never the reverse.
Try it on your own pipeline
Book a 20-minute walkthrough with our team.
