Case Study: Modernising Customer Operations for Resilience and Scalability

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Executive Summary

A regional Australian water utilities provider partnered with us to transform a legacy customer operations environment that struggled to scale during service disruption events. The redesigned model now deflects 62% of inbound contacts through AI-powered self-service, provides 24/7 overflow coverage, and has sustained zero critical system failures during high-volume disruption periods.

Customer Context

The client is a regional water and utilities provider servicing households and businesses across multiple states. With rising customer expectations for real-time updates and seamless support during outages and incidents, the organisation required a modern operating model capable of handling unpredictable demand while protecting service continuity. Its contact centre environment, constrained by fragmented systems and limited surge capacity, could not deliver that reliably.

 

Challenge

The contact centre environment was constrained by fragmented systems and limited surge capacity. Key challenges included:

  • Unpredictable spikes in contact volumes during outages and service incidents.
  • Legacy platforms blocking or dropping calls at peak demand.
  • Manual processes and multi-system navigation reducing productivity and increasing error rates.
  • No scalable workforce and technology model to manage surge events.

Solution

Rather than applying incremental fixes, we redesigned processes, systems, and workforce structures as an integrated operating model:

  • Automation and API-Led Integration – Connected core customer service platforms to remove manual handling and multi-system switching.
  • AI-Enabled Self-Service – Deployed conversational bots to manage common enquiries, absorbing demand during high-volume events.
  • Outage System Integration – Linked outage management systems with the contact centre to deliver live updates to customers, including while on hold.
  • Flexible Workforce Model – Blended permanent and contingent capacity to scale support quickly during surge events.
  • Real-Time Dashboards – Provided operational visibility and demand forecasting for data-led staffing and escalation decisions.
  • Data-Led Escalation Frameworks – Prioritised enquiries by urgency and sentiment so the most critical customers were reached first.

Outcome

The redesigned model delivered measurable performance and service improvements:
62% of inbound contacts deflected through AI-powered self-service, reducing agent workload.
Zero critical system failures during high-volume disruption periods.
24/7 support coverage established, enabling overflow protection during peak events.
Automated live outage updates improving transparency and reducing customer frustration.
Improved productivity through reduced manual handling and system switching

Strategic Impact

This transformation repositioned customer operations from a reactive support function into a resilient service ecosystem. By integrating automation, AI, and flexible workforce design into the core operating model, the organisation strengthened service reliability and customer trust during critical events, and built a scalable foundation capable of responding confidently to future disruptions.

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