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Infosys Media and Entertainment Global

Financial Times

Customer Service Automation

Financial Times needed to support a growing subscriber base and broader product portfolio without overwhelming its service teams, while more than half of incoming cases could potentially be handled through self-service. Manual workflows across email, phone, and web chat, fragmented tooling, inconsistent experiences across desktop, mobile, and app channels, and the need to scale support globally were limiting efficiency and service quality.

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17.4% cancellation-save rate, up from 14% in 2024 as a result of agents having more time to focus on value-added cases
30% (50k) reduction in deflectable case volume (on track to deliver FY target 50%).
37% reduction in inbound email cases with a shift to web, Live Chat and ChatBot channels
52% reduction in spam emails received

Reported impact

Business outcome

The pilot reduced deflectable case volume, shifted more work into digital channels, saved agent time through automated classification and generative reply recommendations, shortened resolution times for online submissions, improved cancellation saves, reduced ghost chats and spam, and helped FT deliver record customer-satisfaction scores while scaling support more efficiently.

  • Cost Reduction
  • Customer Experience
  • Productivity Improvement
  • Scalability

Solution

Infosys and its subsidiary Fluido redesigned Financial Times' customer-care operation around AI-assisted intake and case handling inside a consolidated service stack. Subscriber requests arriving through web, email, phone, and chat were redirected where possible to an AI-powered ChatBot with guided self-serve flows so common questions could be answered without agent intervention. For issues that still required human support, Web-to-Case shifted intake into a web-based workflow that reduced spam and standardized case submission, while Einstein for Service automated case classification to improve routing speed and consistency. LiveChat (MIAW) added asynchronous digital conversations across channels, and Twilio plus internal tools were unified with the Salesforce Einstein environment to simplify operations. The rollout also introduced user-verification steps for online submissions and generative reply recommendations, giving FT a more scalable, globally supportable service model.

Data flow

Subscriber issues enter from web, email, phone, and chat channels. The AI-powered ChatBot handles common requests through guided self-service, Web-to-Case captures remaining digital submissions, Einstein for Service classifies the cases, and LiveChat (MIAW), Twilio, and internal tools support asynchronous conversations and agent workflows until cases are resolved.

Solution architecture

6 components · 4 layers
  1. Ingest
    • Web-to-Case Moves support intake into a web-based case-management flow to reduce spam and standardize submissions.
  2. Compute
    • Einstein for Service Automates case classification so support work can be routed and resolved more consistently.
  3. Serving
    • AI-powered ChatBot with Guided Self-Serve Handles common subscriber questions in real time and steers customers toward self-service resolution.
    • LiveChat (MIAW) Supports asynchronous customer conversations across digital channels with lower wait times.
    • Twilio Provides messaging connectivity within the consolidated customer-care stack.
  4. Orchestration
    • internal tools Remain connected to the Salesforce-based support environment so agents can work cases from a simpler operating stack.

Architecture clues

  • Generative reply recommendations were introduced alongside automated case classification.
  • Infosys and Fluido consolidated Salesforce Einstein, Twilio, and internal tools into a simpler operating stack.
  • The rollout added user verification for online case submissions in addition to AI classification and chatbot flows.
  • The support model spans web, email, phone, and chat channels and was designed to scale globally.

Evidence from the source

65 hours/month in agent time saved by automating case classifications and generative reply recommendations
AI-powered ChatBot with Guided Self-Serve: Personalized real-time responses to common queries, enhancing customer satisfaction.
Advanced Case Classification with Einstein for Service: Automated case classification to speed up resolutions and increase consistency.
Average case resolution time reduced from 7 days to 6 hours for online case submissions via implementation of user verification
Consolidated Technology Stack: Unified Salesforce Einstein, Twilio, and internal tools to reduce operational costs and improve system efficiency.
FT partnered with Infosys to deploy cutting-edge AI-enhanced solutions, aiming to revolutionize their customer care operations.
Transparency Evidence & methodology 100/100 reference value · 85% confidence · 2 sources

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 value100/100
  • 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
    9 metrics describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence85%
  • 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
    6 direct source quotes retained.
  • Date provenance0/10
    The publication date has no documented provenance.
  • Source transparency10/10
    2 source references 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. Infosys

    Driving Innovation and Customer Success Through an AI-enhanced Partnership

    First-party
    https://www.infosys.com/industries/publishing/case-studies/driving-innovation.html
    Source type
    Vendor-published customer story
    Supports
    • Customer
    • Industry
    • Region
    • Business problem
    • Solution
    • Products
    • Solution components
    • Data flow
    • Integrations
    • Business outcome
    • Metrics
    • Architecture
    • evidence quotes
  2. Financial Times

    FT – About Us

    First-party
    https://aboutus.ft.com/

    The official About Us page states: The FT Group employs more than 2,900 people worldwide, including 700 journalists in 40 countries.

    Source type
    Official company website
    Supports
    • Company size

Date evidence

  • First archived Aug 4, 2025 · Internet Archive · Independent First Wayback Machine capture; confirms the page existed by this date, not when it was published.
    Open date source

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