AI for Service Is Reshaping Customer Operations at Scale
AI for Service in global banking, powered by platforms like Kore.ai, is increasingly becoming essential for managing complex, high-volume customer environments. As customer expectations rise and interaction channels multiply, financial institutions require platforms capable of handling scale, precision, and multilingual complexity simultaneously.
A global example from Citibank illustrates what is possible when AI is embedded across enterprise customer service operations.
Company Overview: Citibank
Citibank is a leading global financial institution serving individuals, businesses, and governments in over 160 countries.
Founded in 1812 and headquartered in New York City, Citibank provides a wide range of financial services, including:
Retail banking
Credit cards
Wealth management
Commercial banking
With an extensive global network and a strong focus on digital innovation, Citibank is committed to delivering secure, convenient, and customer-focused financial solutions.
The Challenge: Enterprise-Scale Customer Service Complexity
Citibank required a comprehensive customer service platform capable of supporting complex, enterprise-level demands.
Key requirements included:
300+ customer intents across three or more lines of business
Full Contact Centre as a Service (CCaaS) capabilities
Digital and voice self-service
Omni-channel engagement
Multilingual support
Managing this scale while maintaining accuracy and cost efficiency required a coordinated AI-driven approach across channels.
Implementing AI for Service Across Customer Operations
By adopting components of an AI for Service platform, Citibank embedded AI across its customer interaction ecosystem.
This enabled the organisation to manage:
300+ million annual inbound customer interactions
50+ million global consumer banking customers
Up to 60% self-service containment rate
The deployment supported both digital and voice-based customer journeys, while integrating agent-facing AI capabilities.
Measurable Business Impact of AI for Service
The implementation delivered measurable financial and operational outcomes:
$97 million in annual cost savings via digital self-service
$60 million in additional cost savings through Agent AI and voice self-service
95%+ intent accuracy across 300+ intents
Up to 60% self-service containment rate
At this scale, improvements in containment, intent recognition, and automation translate directly into significant financial impact.
AI for Service at Enterprise Scale
The Citibank use case demonstrates that AI for Service is not limited to isolated chatbots or digital pilots. When implemented across digital, voice, and agent-assisted channels, AI becomes a foundational capability for enterprise customer operations.
Key lessons from this implementation include:
AI must support omni-channel and multilingual complexity
Intent accuracy is critical at high volume
Self-service containment directly impacts cost efficiency
Agent AI and voice automation extend savings beyond digital channels
For large-scale service environments, AI for Service becomes a strategic lever for both operational efficiency and customer experience.
