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AI supervisor platform for contact centers
A comprehensive design case study on creating a supervisor interface for contact centers - integrating call deep dives dashboards and insights, agent performance tracking, and AI-assisted training modules to help supervisors manage operations intelligently and efficiently.
My role
Lead designer, product
Team
CONTEXT
Supervisors rely on scattered tools to track performance and call quality, making it difficult to spot issues or coach agents at the right time.
Feedback loops are slow and mostly manual, often leading to delayed or subjective performance reviews.
Listening to real calls revealed behaviour issues like agents staying silent just to stretch call duration and meet targets - problems supervisors currently can’t catch quickly.
PROBLEM STATEMENT
Supervisors in contact centers lack a single, reliable way to monitor team performance, understand call quality, and identify issues as they happen.
Their current workflow depends on multiple disconnected tools, delayed feedback loops, and manual reviews, making it difficult to coach agents effectively or maintain consistent service standards.
We require unified supervisor platform that provides clear insights, highlights priorities, and supports timely, data-driven coaching.
RESEARCH
Tool & workflow audit
We mapped out the tools supervisors currently rely on throughout their day. Most contact centers use a mix of systems that were never designed to work together, creating a fragmented workflow.
QA software
Used for analyzing conversations, scoring agent performance, and marking compliance issues
Takeaways
Used for analyzing conversations, scoring agent performance, and marking compliance issues
Training and LMS Platforms
Store learning modules, scripts, compliance material, and assessments that supervisors recommend to agents.
Spreadsheet Trackers
Used for manual recording of escalations, coaching notes, daily call issues, or team-specific metrics.
Competitor analysis
We examined supervisor dashboards from leading CCaaS (Contact Center as a Service) tools to understand how others design for supervisors in contact-center operations.
Most dashboards focus on metrics, not insights.
Talkdesk and Freshcaller dashboards mainly present KPIs like AHT, call volume, and SLA compliance, but offer little explanation of why performance dropped or what supervisors should investigate.
Takeaway: Supervisors need actionable context, not just numbers. A meaningful dashboard must highlight why something happened, not only what happened.
Few tools connect call quality, sentiment, and agent behavior into one view.
In Observe.AI, sentiment scores, QA metrics, and agent behavior insights exist but are separated across modules, forcing supervisors to switch between screens to understand the full picture.
Takeaway: There is a strong opportunity to unify call quality, sentiment, and agent behavior in one connected view
Training recommendations are almost never integrated into day-to-day tools.
LMS platforms like Lessonly and EdApp operate separately from call monitoring systems, so supervisors must manually connect performance gaps to training content.
Takeaway: Integrating training suggestions directly into the supervisor dashboard can make coaching continuous, contextual, and far more effective.
Opportunities
📊
Build a unified, layered dashboard with both team overview and call-level depth
🔍
Transform raw metrics into meaningful patterns and insights supervisors can act on
⚡
Provide proactive prompts that highlight urgent issues and coaching opportunities
🔄
Reduce system switching by integrating call quality, performance, and training workflows