Have you ever wondered how automotive technicians diagnose and determine root causes when processing warranty claims?
Traditionally, warranty claims analysis requires highly skilled and experienced technicians to diagnose and determine root causes. We at Humaxa wanted to figure out how to make diagnoses faster, more accurate, and more efficient. Could AI help simplify root cause analysis?
- Is it possible to verify and categorize the customer complaints through AI?
We know that AI chatbots can interact with customers to gather detailed information about the issue before the vehicle reaches the technician. What if the AI was able to ask follow-up questions based on customer responses. Could this provide technicians with a structured, pre-diagnosed report? We at Humaxa think the answer is a resounding “yes.” It’s also possible for AI to analyze free-text descriptions from customers to identify keywords, symptoms, and themes related to common issues. Then, technicians can focus on those priorities.
- Can AI perform some basic, preliminary diagnostics?
If AI models can be trained on historical data of similar issues, it might be able to predict probable causes based on specific vehicle models, mileage, and conditions. This could allow technicians to prioritize high-probability issues, reducing diagnostic time. For example, machine learning models might be able to analyze combinations of error codes in real-time, helping technicians interpret which codes are the likely primary causes versus secondary symptoms. AI-based diagnostic software might be able to also cross-reference these codes with past claims data for faster insights.
- What if AI could apply diagnostic procedures and help with testing?
If people created guided diagnostics decision trees, AI-powered software could probably provide step-by-step diagnostic guides based on symptom patterns, error codes, and vehicle data. These guides could adjust in real-time as technicians input test results, optimizing the diagnostic process. It’s also possible to simulate different scenarios, helping technicians test hypotheses about a vehicle’s malfunction without needing extensive disassembly. These types of simulations (“digital twins”) can replicate real-world conditions to uncover hidden issues, without the complications and expense of actual simulations.
- Would automated generation of diagnostic reports help?
It’s possible for AI to automatically document each diagnostic step, noting error codes, test results, and even generate a failure description based on diagnostic data and photos. Natural Language Processing (NLP) can streamline this process by translating technical results into standard language for warranty claims. It’s also possible to use voice-enabled AI to capture technician notes and automatically convert them into a structured report, saving time and reducing potential documentation errors.
- Could AI help determine warranty eligibility?
It’s likely that AI could cross-reference diagnoses with the terms of the vehicle’s warranty coverage, quickly determining if a repair qualifies. This could reduce errors in warranty approvals by checking against exclusions or customer misuse issues. It could also be possible for an AI to analyze the costs and typical labor hours associated with the specific repair and recommend whether the repair is likely to be authorized under warranty. This could streamline the authorization process for the technician.
There are assuredly many more use cases for AI to help reduce warranty processing costs; the above are just a few examples.
Have you tried using AI to speed up the warranty process? How has it worked for you? Would you like to collaborate with us at Humaxa to try this out? Let me know and thanks for reading.
Carolyn Peer
CEO/Co-founder, Humaxa
