Customer Service Automation · IT Operations Automation
IBM Software Support needed to keep thousands of globally distributed engineers focused on high-value technical issues while still handling hundreds of thousands of cases, log analyses, and repetitive follow-up tasks across a large software portfolio.
Reported impact
IBM Software Support reduced repetitive effort, accelerated diagnostic work, and created large projected time savings across routine, complex, and follow-up support activities, helping engineers deliver faster and more consistent support experiences.
IBM Software Support built a layered support-automation architecture on watsonx technologies to deflect routine work, accelerate diagnostics, and orchestrate follow-up tasks. Ask SW Support uses retrieval-augmented generation with watsonx.ai and large language models to answer low-complexity cases from IBM documentation and technotes after a single client interaction. Analyze SW Support combines hybrid classification and LLM reasoning to inspect log-heavy cases and create anomalous windows that help engineers troubleshoot faster across the company’s most active products. Automate SW Support extends the workflow by using watsonx Orchestrate and watsonx.ai to handle repetitive steps such as case follow-ups, updates, closures, and cross-team collaboration. Together these components turn IBM’s support knowledge base and case telemetry into a digital labor system that routes simple issues to AI, compresses investigative work on complex cases, and frees specialists to focus on the problems where human expertise adds the most value.
A client issue is matched against IBM documentation and technotes through Ask SW Support, tougher cases flow through Analyze SW Support for anomaly detection on logs and datasets, and Automate SW Support uses watsonx Orchestrate to push updates, follow-ups, and closures back through the support workflow so engineers focus on exceptions.
“Analyze SW Support brings the power of anomaly detection to the diagnostic process.”
“Ask SW Support is a RAG solution (Retrieval Augmented Generation) that leverages the power of Large Language Models (LLMs) and watsonx.ai.”
“Automate SW Support, built on watsonx Orchestrate and watsonx.ai, can help streamline repetitive tasks such as case follow-ups, updates, closures and cross-team collaboration.”
“Now deployed to 2,800 support engineers worldwide, we project a potential monthly savings of 56,000 hours”
“With an estimated 370,000 low-complexity cases handled annually ... approximately 17,000 hours”
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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