Customer Service Automation · IT Operations Automation
WINDTRE needed to automate and accelerate claims handling for telecom service issues because high report volumes, stateful operational complexity, and repetitive service-desk tasks were slowing response times and consuming staff effort.
Reported impact
WINDTRE automated a large portion of complaints handling, dramatically improved response time, reduced repetitive desk work, and created a foundation for expanding AI and analytics into infrastructure-incident management.
WINDTRE and IBM built an “Intelligent Automation” platform on IBM Cloud to analyze, enrich, route, and resolve service complaints with minimal human effort. IBM Consulting designed and manages the solution, using watsonx Assistant to classify incoming reports, Watson Knowledge Studio to build domain-specific annotators, and Watson Natural Language Understanding to extract relevant facts from unstructured ticket text. Once the AI layer completes the analysis, the ticket is enriched with information pulled from WINDTRE’s internal systems and passed to robotic process automation components—a dispatcher that queues work and performer robots that execute the resolution actions. Because the RPA runs on cloud virtual machines, WINDTRE can scale multiple robots in parallel while dashboards continuously track volume, performance, and benefits. The result is a claims-management workflow that blends NLP, AI classification, ticket enrichment, and RPA so the service desk can focus on higher-value problems while routine issues are resolved or routed automatically.
Customer reports are classified by watsonx Assistant, enriched with information from WINDTRE systems using Knowledge Studio and NLU outputs, then passed to dispatcher and performer robots on IBM Cloud so issues are resolved, partially resolved, or routed to the right team automatically.
“The agreement between the two companies ... is based on the combination of AI services available on IBM Cloud and automation systems.”
“The response time is 10 times faster than before”
“The watsonx Assistant product classifies reports”
“Watson Knowledge Studio creates machine-learning and rules-based annotators”
“over 10,000 reports per month ... are handled”
Review how this reference was scored and trace each normalized claim back to its documented source.
Scoring methodology
Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.
Traceability
Field coverage shows which facts each source supports. Dates are shown only when documented by the source.
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