Global Bank Cuts Contact Center Costs by 35% with AI-Powered Service Automation
The Challenge
A global bank was struggling with rising contact center costs, long average handle times, and inconsistent service quality across 15,000 agents. Customer satisfaction scores had plateaued while volume grew 20% year-over-year. Manual routing and scripted responses were unable to scale.
Our Approach
We designed and deployed an AI-powered customer service platform that combines intelligent routing, conversational AI for common inquiries, and agent-assist tools. The system uses natural language understanding to classify intent, deflect routine requests to self-service, and surface relevant knowledge to agents for complex cases.
Conducted discovery across contact center operations to map inquiry types, volumes, and resolution paths
Designed intent classification and routing models using historical interaction data and domain ontologies
Built conversational AI flows for high-volume, low-complexity inquiries with fallback to human agents
Developed agent-assist tools that surface knowledge articles, policies, and next-best-action recommendations in real time
Implemented guardrails for compliance, consent, and escalation to ensure regulatory and brand standards
Deployed incrementally by journey and geography with continuous measurement and tuning
Key Features
Intent-based routing and deflection to self-service
Conversational AI for common banking inquiries
Real-time agent-assist and knowledge surfacing
Compliance and consent guardrails
Measurement and optimization by journey and segment
Impact & Results
Reduced contact center operating costs by 35% through deflection and handle-time reduction
Decreased average handle time by 28% with AI-assisted resolution and knowledge surfacing
Improved first-contact resolution by 22% and customer satisfaction (NPS) by 18 points
Deflected 40% of routine inquiries to self-service while maintaining quality and compliance
Technologies & Capabilities
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Capabilities
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