Conversational AI That Performs in Practice: Transurban Scales Self-Service Across Channels

Transurban improved customer experience and operational efficiency by embedding conversational AI into real support workflows—automating over 50% of FAQs.

At a glance

Success Highlights

Challenge

As customer expectations evolved, Transurban’s existing chatbot could no longer keep pace with demand. The business needed to improve how customers accessed support while maintaining consistency, accuracy, and trust across both web and mobile channels.

The objective was clear: enable conversational AI that could integrate seamlessly into existing systems, leverage internal knowledge effectively, and automate a growing volume of customer interactions, all without compromising experience quality or operational control.

With a target to automate over 50% of FAQs, the challenge was not just deploying new technology, but ensuring it could perform reliably across real customer journeys and reduce pressure on service operations.

Solution

To address this, a joint effort across operational, technology, and data teams focused on embedding conversational AI into customer support workflows—prioritizing scalability, cost efficiency, and real-time performance.

The approach focused on three key areas:

1. Scalable Conversational AI Deployment

  • A natural language processing (NLP) solution was introduced using Amazon Lex and Amazon Bedrock
  • The system was integrated with Transurban’s knowledge base to deliver context-aware, real-time responses

2. Omnichannel Experience Enablement

  • Asynchronous messaging was implemented within the mobile app, extending support beyond traditional webchat
  • Customers could engage naturally across channels, improving accessibility and reducing friction

3. Operational Integration and Optimization

  • The solution was embedded into existing service workflows to ensure consistency and usability at scale
  • Continuous optimization focused on improving containment, refining responses, and expanding coverage across use cases

As a result, conversational AI became a core part of how customer support is delivered, handling a broader range of interactions while reducing reliance on human-assisted channels.

“The AI chatbot will significantly improve Transurban’s customer service capabilities by solely handling over 60% of all inquiries on the Linkt app and website, reducing the need of human-assisted chat interactions by 20%, even with increased chat volume. With 97% of customer interactions already occurring through digital channels, this enhancement significantly improves service accessibility and efficiency.”

Outcomes

Following deployment and optimization, measurable improvements were seen across both customer experience and operational performance:

35% reduction in live chats, reducing service demand.

+60% containment, increasing automation effectiveness.

20% reduction in human-assisted interactions, improving cost efficiency.

For Transurban, conversational AI was about making customer support more efficient, accessible, and scalable. By embedding AI into real workflows and continuously improving performance, the organization has moved closer to delivering consistent service outcomes while reducing operational strain.

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