As companies integrate AI to enhance customer experiences and optimize business processes, AI is becoming ingrained in their operating models. This has created a need to effectively design, deploy, and support the underlying infrastructure for smooth operations of AI-enhanced mission-critical applications.
IBM Technology Lifecycle Support (TLS) provides a wide range of integrated data center services and support designed to help accelerate our clients’ transformation to hybrid cloud and AI. IBM TLS delivers support services for IBM infrastructure products and products from leading third-party systems, as well as support services for software and enterprise networking.
Our AI-infused technology foundation enables us to offer support when problems arise, in addition to helping companies proactively and preemptively avoid them. This allows us to help our clients maintain and deliver high levels of availability and resiliency.
Our journey of infusing AI into support
To address the scale, complexity, and criticality of the infrastructure that IBM TLS supports, we are early adopters of AI and automation technology. From rule-based systems to advanced machine learning models to generative AI, we continually adopt new technology to deliver leading-edge support at scale and across a broad product portfolio.
We leverage the innovation of IBM software and infrastructure products, and we work in collaboration with IBM Research®. As Client Zero for watsonx™ use cases, IBM also uses AI support to improve the client and employee experience.
The IT support experience holds immense potential for leveraging AI across its many facets, making it a prime area for innovation. The next generation of support will increasingly use AI to deliver value to clients, engineers, and the partner ecosystem.
Our solutions are designed for clients to derive increased value in terms of the reliability, availability, and resilience of the systems they implement from IBM and our partners. This can help deliver value to their internal IT staff and, consequently, their customers.
Business leaders expect customer service results—and we deliver
AI-based assistants help our support engineers gain deeper and proactive insights, actionable and personalized solutions to problems, and automation of repetitive processes and tasks. IBM Institute for Business Value (IBV) surveyed nearly 1,500 customer service managers, directors, and executives from organizations that have used conversational AI for at least 12 months across 34 countries and all major industries.
According to the IBV report “Customer service and the generative AI advantage,” nearly two-thirds of business leaders surveyed expect generative AI to increase customer satisfaction, and more than half anticipate higher human agent satisfaction, revenue growth, and customer retention.
IBM TLS provides our customers with tools and offerings that are embedded with AI to help them achieve their IT goals and deepen insights into their environments (including proactive views).
For example, our Support Insights offering allows clients to derive deeper insights about their assets, infrastructure security risks, and case analytics. Our broad portfolio of services addresses the full product lifecycle. It includes design, build, deployment, support, refresh, and decommissioning of core mission-critical systems and new systems for AI. They require end-to-end expertise spanning compute, network, storage, performance, and scale.
Leveraging AI and automation capabilities enables us to provide support services through various channels, including chat, email and phone. This empowers our support agents to offer more informed assistance and improves the overall customer experience.
Using watsonx and a data-driven approach to deepen support services
By using the IBM watsonx portfolio of products for data, AI, and governance, TLS creates new innovative offerings to help our clients optimize their infrastructure investment. We also enable operations for our many thousands of customers with a support experience that is insightful, personalized, and proactive and access feedback from our implementation about the functionality of IBM software and infrastructure products.
We adopted a data-driven approach to measure the improvements driven by AI to our support and delivery processes. We capture business and technical metrics to derive key performance indicators (KPIs) that drive a continuous improvement methodology. These KPIs include improvements to our customer Net Promoter Score (NPS), the effectiveness of our self-service virtual assistants, and the number of issues that were proactively detected and remediated. Additionally, we captured metrics for the time to resolve customer incidents and related process inefficiencies.
Addressing challenges to meet client expectations
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.
Watch the webinar “IBM TLS + AI – Envisioning the next generation of support”
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