4 Impactful Use Cases for AI in Product and Device Support


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Products and devices can be complex to troubleshoot, making customer support a challenge. Companies typically offer a wide range of products and devices, each with its own support requirements, which compounds the complexity of diagnosing and troubleshooting problems remotely. Providing on-site support isn’t necessarily any easier and is very expensive, as issues can still be difficult to diagnose correctly and technicians don’t always have parts on hand until after the initial in-person diagnosis.

The high level of detailed expertise required to solve support issues makes support automation a difficult task, with only 9% of customers able to solve their own support issues using self-service solutions, according to Gartner. Simple chatbots are capable of handling easy and repeatable support questions, but most complex questions continue to be escalated to live agents, which comes at a price: Gartner estimates that live support channels (including phone, live chat and email) cost companies an average of $8.01 per contact, whereas automated self-service channels (including company-run websites and mobile apps) cost an average of $0.10 per contact.

As AI becomes increasingly mainstream, more companies are leveraging AI-powered product assistants. Unlike simple chatbots, these virtual assistants are capable of understanding context, remembering previous customer interactions, and providing personalized support. They also offer the benefit of being able to interact through multiple channels, including voice and text. However, when it comes to providing customer support for products and devices, open source large language models (LLMs) like GPT-4 are not always reliable as they haven’t been specifically trained on troubleshooting data from the products in question.

To overcome today’s product and device support challenges, companies should incorporate advances in AI to enhance service quality, deliver improved on-site support, and increase operational efficiency. Below are four use cases for the technology that can serve as an impactful starting point.

Personalized self service:

Intelligent support automation built with AI can extend a high level of expert support to self-service. Until recently, online self-support mainly consisted of static help center articles and manuals, or third party support from unauthorized forums or online videos. AI has ushered in a new era of self-support, making content interactive, and understanding customer intent in order to deliver the most relevant troubleshooting steps that increase self-service resolution rates and customer happiness. For product companies, these self-service capabilities allow them to both meet customer expectations, and to realize business efficiencies.

Delivering efficient live support:

AI can assist live agents in analyzing symptoms to significantly reduce their time spent identifying problems, and the appropriate solutions. With annual support agent turnover as high as 45%, and training and onboarding costs around $10,000 per agent, providing live support can be challenging on many fronts, and AI can help. When implemented in conjunction with effective self-service support options, AI-assisted live support equips agents with the tools to easily access product expertise and get answers quickly, which can reduce the costs of onboarding and the time needed to train new agents. This improves service quality, dramatically speeds up the resolution process to allow agents to resolve more support cases, and increases customer satisfaction, preventing costly onsite visits.

Maximizing on-site support visits:

On-site service visits can be expensive, averaging around $100 in addition to the cost of parts, and 20-40% of those visits are determined to be unnecessary with issues that could have been solved with self service. Pre-visit AI diagnostics allow companies to analyze a customer’s problem description, historical data, and real-time hardware data. This analysis can diagnose an issue accurately and determine if an on-site visit is necessary, saving money and allowing the technician to adequately prepare for the visit so they won’t need to return. Additionally, AI-powered troubleshooting guides can provide field agents with real-time, step-by-step guidance and suggest solutions based on similar past issues. Tools like these can empower agents to solve even the most complex and rare issues in just one visit, saving valuable time and resources for both their company and customers.

Generating and managing support content:

LLMs can help companies manage, improve and structure large amounts of support documentation at scale, and generative AI can help companies create and maintain this content. By leveraging existing support documentation (such as product manuals or support articles), generative AI can surface relevant support content and create virtual troubleshooting flows to improve efficiency and relevancy, saving time for support teams. It’s vital to remember, however, that some generative AI models can introduce uncertainty and inaccuracies, so look for closed-source models that are specifically trained to handle complex, product-specific troubleshooting scenarios.

Beyond the immediate scope of customer support, companies can also use AI to uncover new revenue opportunities and to assist research and development (R&D) teams. By analyzing factors such as customer behavior and purchase history, and pairing that data with predictive maintenance signals, AI can provide relevant, personalized recommendations for purchasing spare parts, new products or additional services, opening up infinite opportunities for upselling and cross-selling. Tapping into the most common support issues, AI can also allow companies to provide recommendations to R&D teams on how to improve existing products, or develop new products to address prevalent support issues.

By enabling more personalized support, driving operational efficiency, and unlocking new opportunities for revenue and product development, AI has the potential to fundamentally transform customer support. This improved remote customer support helps companies meet customer expectations and increase brand loyalty. To remain relevant in the years ahead, companies need to establish an intelligent, AI-powered support strategy now. In doing so, companies and their support teams can gain an invaluable competitive advantage by boosting internal efficiency and productivity, and fostering more sustainable customer satisfaction.

Galina Ryzhenko
Galina Ryzhenko is the Vice President of Product at Mavenoid. She has over 10 years of experience building efficient and user-friendly AI products in various industries - from Telecom to Health&Fitness, Energy and now - Customer Support. She is passionate about building products that millions of people love to use daily.


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