Transforming a global bank's support with 45% L1 deflection
Overview
A global consumer bank operating across 20+ countries and serving 60M+ retail customers was managing 60K+ service desk tickets per month. High volumes of repetitive L1 requests increased service costs, prolonged wait times and created inconsistent support experiences across regions and languages.
HCLTech implemented an AI-led, omnichannel support model that combined conversational self-service, agent assistance and workflow automation. The transformation deflected 45% of L1 contacts to self-service, freeing agents to focus on complex issues while providing employees with consistent 24x7 multilingual support.
The Challenge
Rising support demand straining global service operations
The organization’s service desk relied heavily on agents to resolve routine requests. As ticket volumes increased, this operating model raised costs, consumed specialist capacity and made it difficult to deliver responsive support across a geographically distributed workforce.
Key challenges included:
- High L1 ticket volumes that increased service desk costs and extended employee wait times
- Repetitive password resets, access requests and how-to queries that consumed agent capacity
- Inconsistent support availability across regions and languages, with no uniform 24x7 service model
- Low self-service adoption and weak first-contact resolution
- Limited ability to scale support without a linear increase in headcount
Together, these challenges increased operating pressure, slowed issue resolution and constrained the organization’s ability to deliver consistent employee experience at global scale.
The Objective
Engineering scalable support without scaling headcount
The organization sought to improve service desk performance while making support more accessible to employees across regions, languages and channels.
The organization aimed to:
- Reduce service desk costs and employee wait times
- Shift routine L1 requests to effective self-service channels
- Increase first-contact resolution and reduce average handle time
- Provide consistent 24x7 multilingual support across the global organization
- Scale support capacity without adding headcount, while preserving agent capacity for complex issues
The Solution
Building an AI-led service model for scale
HCLTech redesigned the support journey around an intelligent omnichannel front door. The solution combined conversational AI, real-time agent assistance and automated fulfillment to address routine requests faster, improve service consistency and reduce dependence on manual intervention.
Enabling intelligent omnichannel self-service at scale
- Deployed HCLTech AEX Cognitive virtual agent and conversational AI across chat, Microsoft Teams and voice
- Used the Everyday AI virtual agent to automate password resets, access requests and knowledge-base answers
- Enabled 24x7 multilingual support through real-time translation
Augmenting service desk agents
- Provided real-time recommendations to help agents resolve requests more effectively
- Applied sentiment analysis to improve interaction quality and identify recurring service issues
- Redirected agent capacity from repetitive requests to complex incidents and employee needs
Automating resolution and fulfillment
- Applied agentic automation to common break-fix processes
- Automated standard provisioning workflows to reduce manual handling and operational delays
- Extended automation from initial employee interaction through request completion
The Impact
Improving resolution, experience and support capacity
45% l1 deflection: Unlocking agent capacity and operational efficiency
- 45% of L1 contacts were deflected to self-service, freeing agents to address complex issues
- The organization achieved higher first-contact resolution and lower average handle time
24×7 multilingual support: Delivering a consistent global employee experience
- Consistent 24x7 multilingual support improved service availability across regions
- Real-time translation reduced language barriers and supported a more consistent employee experience
60K+ tickets/month: Scaling support without adding headcount
- The organization scaled support volumes without adding headcount
- The transformed model supported an environment handling 60K+ tickets per month
- Sentiment insights enabled continuous service quality improvements
Conclusion
By combining conversational self-service, AI-assisted agent operations and workflow automation, HCLTech helped the bank move from a high-volume, agent-dependent support model to a consistent 24x7 service. Deflecting 45% of L1 contacts released capacity for complex work, while improved resolution, lower average handle time and the ability to scale without additional headcount positioned the organization to manage future support demand more effectively.
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