CCW Executive Exchange: BFSI 2026
Turn Hidden Contact Center Process Gaps Into Scalable AI Agents
Fort Lauderdale, FL
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Featured Session | April 9 | 4:30 PM
Deploying AI Agents: What Your Contact Center Analytics Can't Show You
A Fortune 50 banking and financial services company lost time, money and customer trust in their trading support operations. Traditional analytics could not pinpoint why. This interactive session walks you through how process context exposed what no system of record could, and delivered $14M in annual savings with a 25% lift in agent productivity.
You will walk away with a practical framework to:
- Find hidden cost drains. Identify process gaps and decision bottlenecks that task mining and traditional process mapping miss entirely.
- Replicate top-performer efficiency. Use the efficient work paths your best agents follow to close performance gaps, reduce handle time and lift team-wide productivity.
- Prioritize the right AI bets. Decide where to invest in AI and automation based on actual task suitability and time and effort data, not vendor roadmaps or internal assumptions.
- Make AI agents operation-ready. Give your AI agents the complete workflow blueprints, decision logic and compliance guardrails they need to execute multi-step processes without gaps.
Meet with Skan AI
The Operational Truth Behind Every Customer Interaction
In BFSI, the contact center is where customer trust is earned or lost. But to protect that trust at scale, you need to see how the work actually gets done. Your tools today only track outcomes: handle times, volumes, disposition codes. They never show you the app switching, the undocumented workarounds, or the ten different ways your agents handle the same case. That gap is exactly where cost hides and AI breaks.
Skan AI observes how your agents actually work across every application and screen, with zero integration. You see the real bottlenecks, the hidden decision points, and exactly what top performers do that others do not. That is the operational picture you need to automate with precision and build AI agents that perform with full context and control.
