What AI call summaries are genuinely good at, and what they are not
An honest look at automatic transcription and summarisation after a year of running it across live customer calls.
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Key takeaways
- The clearest win is CRM hygiene, with accurate notes attached automatically within a week.
- Summaries capture what was said reliably but infer how a call went poorly.
- Treat sentiment scores as a prompt to listen to the recording, never as a verdict.
- Feed a glossary of product and account names in to close most accuracy gaps.
Summarised from the article below. Always worth reading the full piece for the caveats.
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GeminiExcellent at the admin nobody enjoys
The clearest win is CRM hygiene. Reps who used to write two-line notes an hour after the call now get an accurate summary attached automatically. The data quality improvement is not subtle, and it shows up within a week.
Good at recall, weaker at judgement
Summaries reliably capture what was said and what was agreed. They are much less reliable at inferring how the call went. Treat sentiment scoring as a prompt to go and listen to the recording, never as a verdict on a rep or a deal.
Accents and jargon still need help
Accuracy varies with audio quality and domain vocabulary far more than vendors like to admit. Feeding a glossary of your product and account names into the model closes most of that gap, and it is worth the afternoon it takes to assemble.
Tell people it is running
Beyond the legal requirement in most jurisdictions, announcing recording and summarisation changes very little about how customers behave and a great deal about how your team feels. Quiet monitoring is the fastest way to lose a floor.
Put this into practice in an afternoon
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Written by
Daniel OkaforHead of Sales Engineering
Daniel helps sales teams get more conversations out of the same list. Before Votelly he led revenue operations at two B2B software companies and spent a lot of time arguing about dialler settings.
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