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TCS Media and Entertainment Australia

Foxtel Group

Customer Service Automation

Foxtel Group needed to improve customer sentiment, first-contact resolution, and cost to serve as streaming competition intensified, but managing millions of interactions across channels required smarter automation and a more connected operating model than its existing support setup could provide.

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20% reduction in interactions per subscriber
4-5% uplift in first-contact resolution
Interactive voice response (IVR) improved from 46% to 53%
chatbot performance lifted from 41% to 57%

Reported impact

Business outcome

Foxtel improved self-service performance, raised first-contact resolution, lowered interactions per subscriber, and created a more proactive AI-led support model that balances better customer experience with lower service costs and higher workforce productivity.

  • Cost Reduction
  • Customer Experience
  • Productivity Improvement

Solution

Foxtel Group and TCS created a next-generation contact-center operating model that combines AI, GenAI, analytics, and augmented reality. TCS introduced a business-as-a-service orchestration layer that aligns IT and operations, then fed sales, support, marketing, and product signals into a connected-intelligence model for end-to-end visibility. Within that flow, GenAI Agent Assist captures customer context, reads Foxtel's knowledge base, analyzes interaction history, suggests real-time responses, and auto-summarizes conversations for agents. Advanced monitoring and analytics track satisfaction, resolution, and system performance, while IVR, chatbots, and AR-based remote assist handle or accelerate customer interactions before they become costly field visits. The result is an AI-led service architecture designed to blend self-service, agent assist, and observability inside one continuously improving CX engine.

Data flow

Signals from sales, support, marketing, and product teams are consolidated into Foxtel's connected-intelligence model, where monitoring and analytics track performance end to end. GenAI Agent Assist uses customer context, interaction history, and the knowledge base to guide live agents, while IVR, chatbots, and AR-based remote assist handle or accelerate self-service and troubleshooting before issues escalate.

Solution architecture

7 components · 4 layers
  1. Storage
    • knowledge base Provides the reusable content that Agent Assist draws on during customer interactions.
  2. Serving
    • GenAI Agent Assist Surfaces customer context, suggested responses, and post-call summaries for live agents.
    • interactive voice response (IVR) Contains self-service interactions before they need an agent touchpoint.
    • chatbot Handles digital self-service conversations and reduces contact-center volume.
    • augmented reality (AR)-based visual assistance Lets agents guide customers through technical fixes remotely during support interactions.
  3. Orchestration
    • business-as-a-service model Orchestrates the integrated contact-center delivery model across business and IT operations.
  4. Governance
    • advanced monitoring and analytics Tracks customer, operational, and system metrics so teams can improve interactions continuously.

Architecture clues

  • AR-based remote assist is embedded into contact-center troubleshooting to avoid truck rolls.
  • Data from sales, support, marketing, and product teams is consolidated for connected intelligence.
  • GenAI Agent Assist captures customer context, analyzes history, and auto-summarizes conversations.
  • Observability spans business operations and IT systems through advanced monitoring and analytics.

Evidence from the source

"Interactive voice response (IVR) improved from 46% to 53%, and chatbot performance lifted from 41% to 57%"
"Key contributors include a 4-5% uplift in first-contact resolution and a 20% reduction in interactions per subscriber"
"TCS deployed GenAI-powered Agent Assist to capture customer context, tap into Foxtel's knowledge base, and analyse history."
"The agent suggests real-time responses and auto-summarises conversations post interactions"
"customer effort score (CES), which has improved by 9% for digital and 3% for voice"
Transparency Evidence & methodology 95/100 reference value · 95% confidence · 1 source

Review how this reference was scored and trace each normalized claim back to its documented source.

01

Scoring methodology

How scores are calculated

Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.

Customer reference value95/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth25/25
    Rewards named components, data flow, integrations, and architecture detail.
  • Outcome specificity20/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact25/25
    5 metrics describe measurable impact.
  • Deployment scale5/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence95%
  • Source authority20/25
    The main evidence is an official vendor-published customer story.
  • Claim traceability25/25
    12 of 12 key fields link to a named source.
  • Direct evidence20/20
    5 direct source quotes retained.
  • Date provenance10/10
    The publication date comes from the source page or publisher.
  • Source transparency10/10
    1 source reference document publisher, URL, authority, retrieval, and supported fields.
  • Implementation specificity10/10
    Rewards source-backed components, data flow, quantified impact, and a specific outcome.
02

Traceability

Sources & provenance

Field coverage shows which facts each source supports. Dates are shown only when documented by the source.

  1. TCS

    Foxtel Partners with TCS to Accelerate AI-led Customer Experience

    First-party
    https://www.tcs.com/what-we-do/services/cognitive-business-operations/case-study/foxtel-accelerate-ai-led-customer-experience
    Source type
    Vendor-published customer story
    Published
    Jan 1, 2026
    Supports
    • Customer
    • Industry
    • Region
    • Company size
    • Business problem
    • Solution
    • Products
    • Solution components
    • Data flow
    • Integrations
    • technical area
    • use case category
    • Business outcome
    • outcome category
    • Metrics
    • Architecture
    • evidence quotes
    • Publication date

Date evidence

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