MAPConnected Q/A: Leveraging AI in service and warranty

Last month, warranty leaders and industry innovators gathered in Detroit for the Vehicle Service & Warranty Lifecycle Summit by Mapconnected. During the summit, "Leveraging AI to Optimize Critical Decisions in Service and Warranty," the panel explored the practical applications of AI-powered tools in warranty processes and covered questions and key concerns about AI.

Panelists from Circuitry.ai, Xalt, and Mentor shared their insights and addressed audience questions.

Here are our answers to key questions about AI's impact on warranty processes.

Leveraging AI to Optimize Critical Decisions in Service and Warranty Q/A

1. How can AI help with warranty and service Management? Are there any examples of where and how AI can help?   

AI can transform warranty and service management by enhancing and automating key processes throughout the warranty and service lifecycle.

Digitization helped to capture, store, and access the data better. AI makes use of that data to augment and automate the decisions. With AI, you can boost productivity, reduce warranty costs, and improve the efficiency of warranty operations.

Let's take a common scenario and see how AI can help:

Imagine a car breaking down. Our AI can instantly determine if the repair is covered by warranty, predict the necessary parts and labor, and pre-approve the repair.

During the repair, our AI Advisor guides the technician step-by-step, ensuring it's done correctly the first time. Afterward, our AI agent automatically generates and approves a claim from the work order, saving valuable time. Meanwhile, our AI Analysts update repair profiles and identify emerging issues, preventing small problems from turning into bigger ones.

The result? Companies using our Decision Intelligence platform see a 35% increase in productivity, a reduction in warranty costs, and a 20% decrease in issue resolution times.

2. How does AI differ from current systems using business rules, business intelligence, and automation?  

AI is different from traditional systems that use fixed business rules, BI, and automation. In older systems, programmers had to set up specific rules and workflows to automate tasks. With AI, models learn from the knowledge and historical data to give guidance, make predictions, and automate processes without fixed rules. Unlike business rules and BI, which stay the same, AI gets better over time as it learns from the new data. 

3. How can companies realize the ROI from AI in warranty or service? What are the advantages? 

AI can deliver value and ROI for companies by improving team productivity, warranty outcomes, and efficiency of warranty operations. AI improves productivity by 35% by automating tasks and simplifying processes.

It uses predictive analytics to improve product quality and reduce the time it takes to fix issues by 20%. AI also helps technicians, service advisors, and claims processors work more effectively, making every team member perform like an expert.

4. How can AI help with diagnostics or enable technicians?  

An AI Advisor trained on service knowledge can leverage that knowledge to answer questions, guide technicians through step-by-step diagnostics and repair procedures, and recommend the right parts for repairs.

Technicians can save time by getting precise and contextual answers rather than searching through multiple sources of content or contacting technical support centers.

With conversational AI, technicians can access accurate answers globally on any channel, in any language, and at any time. AI can capture feedback and learn from expert answers to help technicians increase first-time fix rates. 

5. How does AI help improve the quality of products?  

 AI helps improve product quality by quickly detecting issues, patterns, and trends that might go unnoticed. Instead of analysts spending hours reviewing reports, AI can spot problems and alert teams immediately. This speeds up issue detection and helps identify root causes, leading to faster fixes and better overall quality.

6. How can AI help reduce, identify, or manage recalls?

AI can help manage recalls by identifying potential issues early and spotting patterns of failure that could lead to a recall. It can also track ongoing recall activities, ensuring that the process is monitored and managed effectively. This early detection and tracking improve recall response and help prevent issues from spreading further. 

7. How can AI be used to offer preventive, predictive, or proactive services to customers? 

AI enables preventive, predictive, and proactive services by analyzing data from connected cars. Unlike the traditional break-fix model, where a technician is called after a car fails, AI can use IoT data to detect issues early, allowing for repairs before a breakdown occurs—something difficult for humans to do manually.

8. What role does connected vehicle data (IoT/Telematics) play in AI-powered decision-making? 

Connected vehicle data from IoT and telematics is essential for AI-powered decision-making in automotive services. By analyzing real-time data, AI can improve decisions, like reading diagnostic codes and suggesting repair steps and parts for technicians. This data also supports predictive maintenance by spotting issues early, helping prevent breakdowns.

While similar methods have been used in industries like aviation and medical devices, the lower costs of data collection and analysis now make these AI-powered capabilities practical for the automotive industry.

9. How can AI help with claim process automation? How will it help with warranty cost reduction? 

AI can streamline the claims process and reduce warranty costs by automating key tasks. It can create repair profiles to validate and speed up claims processing while reducing excess parts and labor expenses. AI also helps prevent fraud by spotting suspicious claims using claim risk scoring and converting text into structured data on failures. AI can even analyze pictures attached to claims for added accuracy. 

10. What is the best way to implement AI? Does it supplement or replace current systems?

The best way to implement AI is to use it to supplement or augment your current systems. AI can add intelligence at key decision points, helping improve outcomes without fully replacing existing systems. Many companies are adopting an AI as a Service(AIaaS) model, similar to how SaaS and cloud systems have been used over the past 20 years. This model doesn’t need a huge upfront investment from companies to realize the benefits from ROI while lowering the risk.

11. How does AI help transform my warranty or Service department? What is the impact on processes?  

AI can transform your warranty or service department by automating routine tasks, allowing your team to focus on more important decisions. AI helps prevent non-warranty claims, guides technicians, and even generates claims, often eliminating the need for manual entry. It gathers relevant information from multiple systems, so manual lookups become unnecessary.

This enables you to process more claims efficiently while improving the quality, consistency, and speed of decision-making. 

12. What service decisions can AI not help with? What are the limitations of AI? 

AI works well for decisions and predictions based on knowledge and historical data, but it does have limitations. AI can help with decisions about diagnostics, repair procedures, parts usage, claim adjudication, return authorization, supplier recovery, quality issue detection, recalls, and more.

AI isn't capable of understanding, reasoning, or creating original solutions, and it's not a form of general intelligence. AI also depends heavily on the data it's trained on, which means it can reflect any biases present in that data. Additionally, it lacks emotions and empathy, making it unsuitable for decisions that require a personal touch. 

13. How does the quality of current data affect AI decisions? 

The quality of relevant data directly impacts AI decisions. Companies don't need to invest in having huge data lakes or warehouses with all sorts of data. Instead, they can focus on the data relevant to support the decisions. This decision-centric approach will shorten the time to value and reduce the cost of data infrastructure.

Poor data quality can lead to inaccurate decisions, biased outcomes, and limited decision-making if data outside the training set is needed. Low-quality data can also cause overfitting, making AI perform poorly on new information, and it reduces learning efficiency, increasing the need for human input to guide the AI. 

14. What are the challenges and risks of implementing AI in the warranty process? 

Companies with complex products and claims may doubt that AI can match the decision-making abilities of experienced staff. To address this, it’s important to have AI/ML models that specialize in models and AI algorithms tailored to the manufacturing industry.

Another challenge is the availability and quality of historical service and claims data. In some cases, key knowledge may not have been documented for AI training. Companies can start by using the data they have and gradually increase automation as AI improves with more input.

15. How do you address concerns about AI going out of control in making claims or warranty decisions?

Companies can start with decision augmentation before moving to fully autonomous decision making and have human-in-the-loop for decision monitoring.   Having the right decision models, governance, and AI/ML Ops will reduce the risk of inaccurate decisions. 

16. People are concerned about their jobs with AI. How can the change and concerns be managed?  

AI can automate many of the routine decisions and tasks to free up people to focus on tasks that require creativity, intelligence, and specialized knowledge. AI can also enable people to do more tasks and make them productive in some existing tasks. While this may redefine job functions, it creates opportunities for people to add value in new ways.

AI can help address the challenges with the skilled worker shortage in functions like service technicians, warranty claims adjusters, product quality, and engineering teams. AI can also help with onboarding and upskilling by helping employees learn faster and perform better.

17. How do you address concerns about data privacy and governance?  

To address data privacy and governance concerns, it's important to choose appropriate models and enforce strict data and privacy controls. When needed, data can be anonymized to protect individual identities while still allowing for analysis and insights.

It’s also crucial to work with enterprise AI partners who have strong controls and data governance measures. These partners should ensure that data is secure, not shared with public models, and never exposed to third parties.

Circuitry.ai's Enterprise AIaaS model completely segregates and protects each customer's data and only uses it to train the AI models for that customer, preventing any risk of data leakage.

18. Is AI another fad or a foregone conclusion?   

AI isn't just a passing fad but a transformative technology that’s here to stay. Just as companies have adapted to the shift toward web and mobile platforms, AI now represents a new frontier for driving productivity and efficiency gains. It is already influencing our daily lives in countless ways, with its use cases projected to generate trillions of dollars.

While certain aspects of AI may be overhyped, its potential to deliver tangible value is real when applied to the right use cases tailored to specific industries and processes. For organizations aiming to thrive and grow, adopting AI is becoming a necessity rather than an option.

Why Circuitry.ai?

Circuitry.ai combines decades of experience in developing leading warranty software with cutting-edge AI technologies to deliver practical and innovative solutions to optimize warranty outcomes. Missed us at the Service & Warranty Lifecycle Summit? Schedule a personalized demo and see Service Advisor in action.