AI for Service: 7 Proven Use Cases That Drive ROI

Artificial intelligence is now the top ranked investment area for service leaders, beating out technologies such as business intelligence and field service management, according to the Service Council’s new report, 2025 State of Artificial Intelligence & Service Technology.

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AI’s power lies in its ability to process vast amounts of data, spot patterns, predict outcomes, and continuously improve. And there are a multitude of AI use cases that are helping field service technicians, technical support teams, customer service agents, and customers themselves resolve service issues faster than ever before.

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But where should service leaders start? Analysts recommend focusing on use cases that drive fast, measurable ROI with AI, and developing a system of KPIs to measure leading and lagging success.

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With that in mind, here are seven high-impact use cases for AI in services.  

Intelligent Search

When searching for a resolution to a service issue, teams often struggle to find the right answer. The best resolution may lie in a manual, knowledge base article, service portal, CRM system, or some other data source. Searching through disparate systems and complex documents can be time consuming and frustrating—especially when all that effort doesn’t deliver a good answer.

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That’s why intelligent search is one of the top use cases for AI. Organizations are investing in tools that analyze knowledge articles, manuals, policies, case notes, and more to understand how issues are solved in their specific environment. With domain-specific AI for service, teams are more likely to get a fast, accurate answer, improving productivity and customer satisfaction.

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Ciena, a $3.6 billion telecommunications company, uses Neuron7 Intelligent Search to help agents find one right answer to resolve any issue from a knowledge base containing thousands of articles and past use cases, rather than having to sort through multiple potential solutions.

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After validating the accuracy of recommended solutions with agents, Ciena extended Intelligent Search directly to customers, enabling automatic issue resolution through chat or the service portal.

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“The most important results are to improve the customer experience so that they can engage with us—and when I say ‘us’ I mean that intelligent AI layer,” says Chandan Banerjee, Director Global Services Digital Innovation at Ciena. That intelligent AI is getting smarter all the time, he says, continuously learning about Ciena’s products and its customers, as is now being used across various points of customer interactions. 

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Since deploying AI for its service organization, Ciena has increased customer satisfaction by 14 percentage points, sped up resolutions by 46%, and increased call deflection by 50%.

Knowledge Capture

As veteran technicians approach retirement, the risk of losing critical institutional knowledge is a growing concern for service organizations. Knowledge capture ensures that valuable insights, troubleshooting techniques, and product-specific expertise don’t disappear when technicians leave the workforce. As noted in this Service Council article, “Baby Boomers make up between 15-20% of the current workforce, possess institutional knowledge, are dedicated...and are leaving the workforce at the greatest rate.”

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AI can play a pivotal role in capturing and retaining expert knowledge, transforming it into structured, accessible data that can be shared with support agents, technicians and engineers. Using AI-driven knowledge management, service organizations can create digital repositories, automate workflows, and ensure that insights are readily available for the next generation. In addition to retaining expertise, this also helps to accelerate onboarding, improve troubleshooting, and maintain high service standards.

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This strategy is already paying off: “Increased ability to capture expert knowledge” ranked fourth among the greatest impact of AI investments, only after improved customer service and improved experience for field service engineers and remote agents, according to the Service Council’s 2025 report.

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Investing in AI for knowledge capture isn’t just a precaution—it’s a strategic move that secures the long-term success and sustainability of service teams.

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“By providing real-time, interactive guidance, our teams can have significant improvements in resolution times and customer satisfaction."
-Sarah Rose, Vice President of Global Services, Daktronics

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Faster Onboarding

Onboarding new service team members can be time consuming and resource intensive, especially when it comes to equipping them with the knowledge and expertise to handle complex service resolutions. Traditional training methods often involve long learning curves, with new hires relying on a mix of documentation, mentorship, and hands-on experience.

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AI-driven solutions can transform onboarding by accelerating knowledge transfer and ensuring that new technicians have immediate access to the information they need. Organizations can provide new hires with real-time access to knowledge—from manuals to troubleshooting guides—allowing them to resolve issues as quickly as experienced technicians.

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Service leaders recognize this benefit: In the Service Council’s 2025 report, one in four survey respondents said “Improving frontline employees onboarding and skills” is an urgent challenge to address through their AI and service technology efforts. 

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NCR Atleos is a $4.1 billion company that helps banks and retailers deliver self-service banking for consumers. NCR Atleos implemented AI-powered Intelligent Search to streamline information access for their field service technicians, enabling them to find precise answers from various data sources and reducing dependency on multiple knowledge platforms. That strategy, along with AI-powered guided resolution, “levels the playing field for newly onboarded technicians to be just as successful as a 10-year,” says Bill Girzone, senior vice president of global field services at NCR Atleos.

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TK Elevator is also benefiting from Intelligent Search to provide real-time answers and reduce escalations. This advancement not only improved efficiency but also simplified information retrieval for technicians, enhancing their onboarding experience.

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Thomas Shanks, Director of Operations at TK Elevator, observes that technicians appreciated having information readily available on their mobile devices, too. “That’s been the sentiment from the field, ‘you’ve made my life easier, so thank you’,” says Shanks.

Self Service

AI is at the core of innovative self-service capabilities, making it increasingly possible for customers, agents, technical support, and field service technicians to resolve issues on their own. Customer self-service is a particularly interesting use case, as many service organizations are looking for ways to “shift left” and empower customers to solve issues independently. In addition to enhancing customer satisfaction, organizations are reducing field service costs and experiencing faster resolutions. 

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Terumo Blood and Cell Technologies is a global medical technology company. Its customer base includes blood centers, hospitals, therapeutic apheresis clinics, cell collection and processing organizations, researchers, and private medical practices—many of whom perform self-service. By using AI-powered solutions to deflect calls and reduce escalations, and service team hours saved from those deflections, Terumo BCT determined it could save approximately $500,000 in the first year of AI deployment. That rapid ROI set the foundation to build a 10-year business case, says Rachael Castroverde, vice president of global services at Terumo BCT.

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As mentioned earlier in this article, improved customer self-service was a primary purpose for Ciena’s deployment of AI-powered solutions. The end goal of the use case was to provide customers with a single article about the product that would provide them with the most relevant information, based on common issues and sources of friction. 

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“We focused on the use case that would bring us the quickest and the biggest ROI, which is our biggest product with the biggest population of issues."
Chandan Banerjee, Director of Digital Innovation, Ciena

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Ciena began by setting an accuracy threshold and then working with its AI solutions vendor to ensure that threshold was achieved, and the AI was maturing accordingly, before rolling it out to customers. The solution article quickly rose through the ranks, becoming the most suggested in Ciena’s knowledge base of 15,000 articles. 

Parts Reduction

Managing service parts efficiently is a constant challenge for service organizations. Unnecessary part replacements, inaccurate diagnostics, and inefficient inventory management can drive up costs and lead to wasted resources. However, AI-powered solutions can transform how service teams handle parts management—helping them reduce unnecessary orders, optimize inventory, and ensure the right part is used at the right time.

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By leveraging AI-driven diagnostics, predictive maintenance, and Intelligent Search, service organizations can minimize excess parts usage, improve first-time fix rates, and cut down on costly returns. AI helps technicians pinpoint the exact issue before ordering a replacement, recommends the correct parts based on historical data, and even enables self-service options that eliminate the need for parts in certain cases. The result? Lower costs, higher efficiency, and a more sustainable approach to service operations.

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NCR Atleos identified inefficiencies related to a confusing parts ordering system, which negatively impacted first-time fix rates. Through the use of AI, technicians gained streamlined access to critical information, potentially reducing errors in parts ordering and improving service efficiency.

Preventative Maintenance

For service organizations, preventative maintenance is essential to keeping equipment running smoothly, minimizing unexpected failures, and reducing costly downtime. Traditional maintenance approaches often rely on scheduled servicing or reactive repairs—both of which can lead to inefficiencies and unnecessary part replacements. AI-powered solutions can transform preventative maintenance by making it smarter, data-driven, and proactive.

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By leveraging AI-driven diagnostics, real-time data analysis, and predictive insights, service teams can anticipate failures before they happen, ensuring that equipment receives maintenance at the right time—not too soon and not too late. AI can analyze vast amounts of service data, detect patterns, and recommend actions that extend equipment life, reduce service costs, and improve operational efficiency.

Next Steps

As the companies cited in this article found, it’s not just one but often multiple use cases that demonstrate how AI improves efficiencies, speeds resolutions, saves money, boosts productivity, and leads to greater customer and service agent satisfaction.

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And with any new innovations and technology investments, identifying use cases is just part of the groundwork—a compelling business case comes next. That shouldn’t be a problem considering AI’s growing impact on the services function.  

“Most organizations have messy data, multiple legacy systems, competing initiatives and their fair share of skeptics. However, ignoring AI is not an option for any business that wants to remain competitive,” according to the “Analyst’s Take” section of this research report.

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The right vendor and partner will help service leaders build a business case for AI based on powerful use cases, including demonstrating value through a pilot and delivering relevant customer stories with proven results.

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If you found this article interesting, keep an eye out for additional posts in the coming weeks as part of our How to Build an AI Strategy blog article series. We’ll cover everything from how to build a business case through to deploying AI at scale, and examine how service leaders can excel in their careers in the era of AI.

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