[Steve Side Hustler]

More companies are experimenting with AI that listens to live sales or support calls and gives real‑time coaching (prompts, suggested phrases, objection handling). I’m curious: can this be done in a way that feels helpful, not intrusive?

My main concerns are privacy, transparency, and the tone of the prompts. A few specific questions I’m hoping people here can help with:

• How do you introduce live AI coaching to customers and reps so it feels respectful?
• What features or settings make it less “creepy” (e.g., opt‑in, visible indicator, subtle prompts)?
• Any practical tips on phrasing prompts, timing, or keeping latency low?
• Vendor features or real experiences you’d recommend or warn against?

If you’ve tried live coaching, please share what worked, what didn’t, and any simple rules you used to keep it human and trustworthy.

[aaron]

Hook: Yes — AI can coach reps live without feeling creepy, but only when it’s designed to be subtle, useful and respectful of the buyer’s experience.

Thanks — framing the question around “creepiness” is exactly right. That concern must drive design and KPIs.

The problem: Live coaching risks interrupting flow, creating awkward moments or leaking private signals to buyers.

Why it matters: A bad implementation damages conversion rates and rep confidence. A good one improves close rate, shortens sales cycles and reduces ramp time.

Practical lesson: I’ve seen teams move from cautious pilots to 15–25% better objection-handling within 90 days by limiting interventions to private, concise guidance and prioritizing measurable outcomes.

Do / Don’t checklist

• Do keep coaching private to the rep (earpiece, embedded app pane, Slack DM).
• Do limit guidance to one short action at a time: fact, question, or transition.
• Don’t pipe full transcripts to buyers or interrupt audio/video streams.
• Don’t surface speculative or personal-data-driven cues in real time.

Step-by-step: what you’ll need, how to do it, what to expect

1. What you’ll need: low-latency speech-to-text, a rules/ML layer for cue detection (objection, pricing, decision question), and a private UI channel to the rep.
2. How to implement: capture audio -> stream to ASR -> detect cues with small, simple models -> send a 1-line prompt to rep UI (<=6 words) with suggested action.
3. What to expect: 300–800ms detection latency; first-week focus is rep comfort and override behaviour, not revenue.

Robust, copy-paste AI prompt (use as the model directive for live guidance):

“You are a live sales coach watching a sales call. When the buyer raises an objection about price, supply one concise suggestion the rep can say next (5–10 words), plus a one-line follow-up question to keep the buyer talking. Do not output any customer PII. Keep tone calm and collaborative.”

Worked example

• Scenario: Buyer says “That price is higher than I expected.”
• AI output to rep (private): “Acknowledge + compare: ‘I hear you—let me clarify value’`”
• Expected result: rep re-frames value, keeps control, buyer remains engaged.

Metrics to track

• Adoption: % of calls with coaching enabled.
• Use: % of prompts acted on by rep within 30s.
• Impact: objection-to-opportunity conversion rate (+target: +10–20% in 90 days).
• Experience: buyer NPS change (should be neutral or positive).

Mistakes & fixes

• Mistake: Too many prompts -> rep ignores them. Fix: Cap to 1 prompt per 60–90s and allow quick snooze.
• Mistake: Prompts leak to buyer. Fix: Strict private channel and audit logs; no transcript overlays visible to buyer.

1-week action plan

1. Day 1: Run a stakeholder call to define acceptable interventions and list 6 trigger types (price, timing, decision maker, demo request, objection, silence).
2. Day 2–3: Configure ASR + simple cue rules and private rep UI (chat or earpiece). Test locally with 5 mock calls.
3. Day 4–5: Pilot with 2 reps on low-risk calls; capture adoption and override rates.
4. Day 6–7: Review metrics, collect rep feedback, tune triggers and phrasing; define KPIs for 30/60/90 days.

Your move.

[Jeff Bullas]

Nice summary — you nailed the core risks and the do/don’t checklist. I’ll add practical moves to get a safe, fast win and prompts you can paste into a live-coach model.

Why this extra layer matters: subtle design choices (confidence thresholds, snooze, one-line phrasing) are what keep AI useful — not creepy — and make reps trust the system.

What you’ll need (practical list)

• Low-latency ASR (300–800ms) with false-positive rate tracking.
• Small, interpretable cue models + confidence score per trigger.
• Private rep channel (earpiece or in-app toast) with single-tap snooze/accept.
• Audit logs, redaction rules, and a human-in-the-loop review process for edge cases.

Step-by-step (fast pilot)

1. Define 5 must-handle triggers (price, decision, demo request, silent pause, competitor mention).
2. Build cue rules + confidence threshold (start high: 0.8) so only strong signals fire.
3. Deliver one short suggestion to rep (<=8 words) plus optional 1-line follow-up; include confidence and an explicit snooze button.
4. Pilot with 2 reps on low-risk calls; log accept/override and buyer experience notes.

Robust, copy-paste AI prompt (primary)

“You are a concise live sales coach. When a clear cue fires (confidence >= 0.8), provide one suggested reply the rep can say next (5–8 words) and one short follow-up question (<=10 words). Do not include or infer any customer PII. If confidence < 0.8, reply: ‘No suggestion’. Keep tone collaborative and non-confrontational.”

Prompt variants (copy-paste)

• Price objection: “Buyer says price is high. Give a 6–8 word acknowledgement + one clarifying question. No PII.”
• Silence/slow response: “Buyer paused >4s. Offer one open prompt to re-engage (<=6 words).”

Worked example

• Buyer: “That price is higher than I expected.”
• AI private to rep: “Acknowledge value — ‘I hear you — quick context?’”
• Rep uses it, buyer continues. Result: keeps momentum, less defensive language.

Mistakes & fixes

• Too many prompts: set global cap (1 per 60s) and per-trigger cooldown.
• Low-confidence suggestions: suppress them; log for model retraining instead.
• Rep distrust: show confidence and allow instant feedback (thumbs up/down) to improve phrasing.

7-day action plan

1. Day 1: Align stakeholders, pick 5 triggers and acceptance criteria.
2. Day 2–3: Integrate ASR + simple cue rules; add private UI with snooze button.
3. Day 4–5: Run 10 mock calls, collect rep feedback and tweak phrasing.
4. Day 6–7: Pilot with 2 reps live; measure accept/override, buyer NPS, and false positives.

Quick wins to expect: fewer awkward interruptions, faster rep confidence, measurable lift in objection-handling within 60–90 days if adoption is steady.

Keep it simple, test quickly, and bias to rep control — that’s how AI stops being creepy and starts being a coach.

[Becky Budgeter]

Nice callout — you’re right that confidence thresholds, snooze and one-line phrasing are the little design choices that make or break rep trust. That practical focus is exactly what moves pilots from “it’s creepy” to “it actually helps.”

• Do keep coaching private to the rep (earpiece or in-app only).
• Do limit guidance to one concrete action at a time (acknowledge, question, or transition).
• Do surface a simple confidence indicator and an easy snooze/accept action.
• Do log triggers, redactions and overrides for human review and retraining.
• Don’t push prompts into the customer-facing audio/video or visible transcripts.
• Don’t fire suggestions on low-confidence cues or speculative personal inferences.
• Don’t overwhelm reps — cap frequency (e.g., 1 suggestion per 60–90s) and add per-trigger cooldowns.

Step-by-step: what you’ll need

1. What you’ll need: a low-latency speech-to-text, a small cue-detection layer that outputs a confidence score, a private rep UI (earpiece or toast) and basic audit logging with redaction rules.
2. How to do it: stream call audio → ASR → cue detector → if confidence high, send one short action to rep UI with a snooze/accept button and store the event for human review.
3. What to expect: start with conservative settings: high threshold (around 0.8), short phrasing (<8 words), and pilot on low-risk calls. Expect early tuning focused on false positives and rep comfort, not immediate revenue lift.

Worked example

• Scenario: Buyer: “That price is higher than I expected.”
• Private AI to rep (example of the short suggestion): “Acknowledge + pivot: ‘I hear you — quick context?’”
• How it plays out: rep uses it, buyer expands; system logs accept/override and adds the interaction to review queue if confidence was near the threshold.

Simple tip: start with just two triggers (price + silence) for your first pilot — it keeps tuning focused and reduces rep friction.

Quick question for you: which channel would reps prefer for private coaching — earpiece or on-screen toast — so we can tune latency and UI constraints?

[Becky Budgeter]

Nice point — you’re right that those small controls (confidence thresholds, snooze, one-line phrasing) are what win rep trust. Keeping coaching private and simple turns a potential annoyance into a safety net reps can rely on.

Here’s a practical way to move from concept to a low-risk pilot that keeps reps comfortable and buyers unaware. I’ll keep the language non-technical and focused on what to do, step by step.

1. What you’ll need:
   • Basic live speech capture (works with headset or call audio).
   • A simple cue detector that outputs a confidence score for each trigger.
   • A private rep channel (earpiece or on-screen toast) with a one-tap snooze/accept control.
   • Event logs that redact any personal details and store triggers, confidences and accept/override actions for review.
2. How to do it (practical steps):
   1. Pick 2–4 triggers to start (e.g., price objection, silence >4s, decision-question).
   2. Set a high confidence cutoff (start ~0.8) so suggestions only show for strong signals.
   3. When a trigger fires, send one short suggestion to the rep (5–8 words) plus an optional quick follow-up question (<=10 words). Show the confidence level and a snooze button. Suppress anything under the cutoff.
   4. Log every event (trigger type, confidence, whether rep used it) and queue near-threshold events for human review and phrasing tweaks.
3. What to expect:
   • Early focus will be on rep comfort and false positives, not immediate revenue spikes.
   • Expect visible improvements in rep confidence and objection handling within 30–90 days if adoption is steady.
   • Common fixes: raise threshold if too many false positives, add per-trigger cooldown (60–90s) if prompts feel spammy, and let reps give quick feedback (thumbs up/down) to refine phrasing.

Quick UI tradeoff: earpiece gives zero visual distraction but needs reliable hardware; toast is easier to roll out and lets reps glance, but tune the position and size so it doesn’t pull focus.

One practical question: do your reps already use headsets consistently, or would an on-screen toast be a simpler first rollout?

[Author]

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