AI USE CASE
AI Sales Call Coaching from Transcripts
Automatically review recorded sales calls and surface coachable moments for small sales teams.
What it is
This use case transcribes and analyses sales call recordings against a customisable scorecard, checking for discovery questions, objection handling, and confirmed next steps. Each call yields 2–3 prioritised coaching points, letting a sales manager give structured feedback in minutes rather than hours. It replaces ad-hoc, memory-based coaching with a repeatable, data-driven process that scales across a small team without a dedicated enablement function.
Data you need
Recorded sales calls (audio or video) with at least 20–30 calls per month to generate meaningful coaching patterns.
Required systems
- crm
Why it works
- Involve reps in defining the scorecard so they understand and trust the evaluation criteria.
- Block a recurring 30-minute weekly coaching slot per rep where AI-flagged moments are reviewed together.
- Start with a single scorecard dimension (e.g. next-step confirmation) before expanding to avoid overwhelm.
- Tie coaching outcomes to a simple leading metric, such as call-to-meeting conversion, so progress is visible.
How this goes wrong
- Reps feel surveilled rather than supported, causing resistance and low adoption of feedback.
- Scorecard criteria are defined too vaguely, producing generic feedback that managers ignore.
- Call volume is too low (fewer than 10 calls/week) to surface statistically meaningful patterns.
- No dedicated time is carved out for manager-rep coaching sessions, so insights are generated but never acted upon.
When NOT to do this
Don't deploy this if the sales manager has no bandwidth or habit of giving regular feedback, the AI surfaces insights, but a human must still run coaching conversations for any improvement to materialise.
Example vendors
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