Jeremy <> Haris: Project Review

matched

Wed, Dec 3, 2025, 12:00 PM • —

Client

Growth Edge

Participants

0 attendees

Action Items

9 pending

AI Meeting Analysis

AI GENERATED

Client Sentiment

[~]MIXED

The tone is solution-oriented and optimistic about automation and AI improving outcomes, but there is clear concern/frustration around missed calls, slow callbacks, and inconsistent rebooking performance that is materially hurting conversion.

Meeting Quality

EFFECTIVENESS82%

EXCELLENT

Relationship Status

stable

Executive Summary

Jeremy and Haris reviewed current lead nurturing and call-center performance across clinics, identifying major revenue leakage from missed inbound calls and slow callbacks. They discussed using automation and AI (call quality + instructor scoring) to improve conversion, retention, and operational accountability, and outlined next steps for reporting, proof-of-concept development, and implementation planning.

Key Topics Discussed

Lead nurturing automation (email/SMS) and conversion impactInbound lead handling: missed calls, callback speed, and recovery workflowsClinic-by-clinic operational performance reporting and accountabilityRebooking/follow-up appointment capture and revenue retentionAI initiatives: call quality analysis and instructor performance scoringData requirements for AI models (audio recordings, rubrics, scoring datasets)User adoption and technical skill gaps affecting rollout success

Decisions Made

✓Prioritize missed-call recovery and lead response time improvements as a primary lever for conversion gains.
✓Proceed with an AI proof-of-concept to score fitness instructors using audio recordings and an initial scoring rubric.
✓Share clinic/provider-level performance reporting (missed calls, callback times, rebooking) to drive operational accountability and improvement.
✓Advance review of the PMP lead nurturing and call center automation proposal with internal stakeholders (Taylor and Judith) for implementation.

Follow-up Items

→Share the Excel spreadsheet with KCC coaching call scoring data.
→Send the video link demonstrating AI-powered golf putting analysis.
→Develop a proof-of-concept AI model to score fitness instructors based on audio recordings using an initial rubric.
→Prepare and send a pricing estimate for the AI instructor scoring proof-of-concept model.
→Share missed call recovery and lead response time reports for PMP clinics and incorporate feedback for automated workflows.
→Provide recorded instructor session audio files required for AI scoring model training.
→Review missed calls and callback performance reports by clinic to identify operational improvements.
→Review PMP lead nurturing and call center automation proposal with Taylor and Judith for implementation.
→Share provider-level performance and rebooking reports with clinics to increase awareness and drive improvements.

Open Questions

?What is the target SLA for callback time by clinic (e.g., <5 minutes, <15 minutes), and how will compliance be enforced?
?Which clinics/providers are in scope first for missed-call recovery workflows and call center automation rollout (pilot vs. full deployment)?
?What specific rubric dimensions will define the AI instructor scoring model (e.g., engagement, clarity, safety cues, personalization), and who signs off on it?
?How much labeled training data (scored calls/sessions) is available, and who will perform initial human scoring for ground truth?
?What systems will the automation integrate with (CRM, scheduling, telephony), and are there technical constraints or vendor limitations?
?How will rebooking performance be measured (definition of rebooking, time window, attribution) and reported consistently across providers?
?What change-management/training plan is needed given the noted technical skill gaps among younger users?

AI Recommendations

★The meeting surfaced clear performance gaps, aligned on priority areas, and produced concrete action items (reports, data sharing, POC build, pricing, stakeholder review). Some key implementation details remain open (SLA targets, scope/pilot plan, rubric ownership, data readiness), preventing a higher score.

Analysis generated: Dec 15, 2025, 7:43 PM

Action Items

Prepare and send pricing estimate for the AI instructor scoring proof-of-concept model

🎤 Haris Karim

pending

Develop a proof-of-concept AI model to score fitness instructors based on audio recordings using an initial rubric

🎤 Haris Karim

pending

Send the video link demonstrating AI-powered golf putting analysis (01:03:58)

🎤 Haris Karim

pending

Provide necessary recorded instructor session audio files for AI scoring model training

🎤 Jeremy Wright

pending

Review missed calls and callback performance reports by clinic to identify operational improvements

🎤 Jeremy Wright

pending

Review PMP lead nurturing and call center automation proposal with Taylor and Judith for implementation

🎤 Jeremy Wright

pending

Share provider-level performance and rebooking reports with clinics to increase awareness and drive improvements

🎤 Jeremy Wright & Haris Karim

pending

Share the Excel spreadsheet with KCC coaching call scoring data (01:03:58)

🎤 Haris Karim

pending

Share missed call recovery and lead response time reports for PMP clinics and incorporate feedback for automated workflows

🎤 Haris Karim

pending