February 14, 2025

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Technology Lifecycle Services: Envisioning the next generation of support with AI

Technology Lifecycle Services: Envisioning the next generation of support with AI

IBM TLS serves our clients in several ways. We provide integrated product support for the data center across IBM infrastructure and select partner products that incorporate AI for self-service and delivery. We also provide AI-infused insights on supported assets, risks, and cases. We help our clients accelerate the delivery of AI solutions to their stakeholders with offerings and services designed to assess, deploy and decommission infrastructure.

Based on our experience and expertise with clients, when it comes to product support, these are the key client priorities:

  • Responsive client experience. Enable available personalized self-service access, deep insights, and proactive automated notifications that make our client’s SREs more effective.
  • High-quality service and support. Harness insights from multiple cases with personalized context, enabling our support engineers to deliver improved quality of service and maximize the availability of your systems.
  • Efficient service and support. Continually evaluate and improve the efficiency of our back-end processes to speed up responses and remove bottlenecks.

AI and automation (in all forms, including the latest generative models based on the IBM watsonx platform) are critical to delivering these capabilities. But there are several challenges to implementing AI including

  • Managing complexity from the diversity of infrastructure, product versions, and implementation-specific customization and integration.
  • Accessing data sources while maintaining multi-lingual, privacy, and compliance considerations.
  • Considering the human element when dealing with mission-critical systems with low tolerance for downtime and potential for large financial and regulatory impacts.

IBM TLS is currently working closely with the IBM CIO, software, and research teams to address these challenges. We are implementing novel and scalable approaches to vectorizing, ranking, and summarizing large product documentation. Our goal is to provide the foundation for implementing Retrieval Augmented Generation (RAG) approaches to assist our clients over self-service channels and enable our engineers to respond to cases based on similar historical cases.

We are also implementing consistent testing frameworks, effectiveness and accuracy metrics for the underlying models, as well as client, engineer, and LLM-based feedback loops for continuous improvement. We adopted a platform approach that leverages common code across multiple projects, along with inner-source and open-source consumption and contributions.

At IBM TLS, our objective is to leverage learnings across IBM and contribute to other client-facing teams to deliver best practices and implementation insights to customers on their AI journeys.

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