Your Service Data Already Powers AI (Though You May Not Know It)

Your vehicle service records could be powering data-driven AI already!  Vehicle owners, service locations and manufacturers are all finding use for AI that’s based on their data:  To diagnose & anticipate repairs, fix-right-first-time, track trends & safety, retain customers, maintain better records, and more.

Read on…

Consider a wiring harness repair, and let’s say its write-up mentions water leakage. The labor operation may specify the repair, but will not indicate water leakage. But all such detail, if extracted from the data, helps predict future repairs, facilitates diagnoses, and suggests items to check in similar vehicles.

Along with structured data fields (e.g., parts, labor ops, other vehicle-specifics), service advisor and technician write-ups are very useful. Often write-ups provide context and other details not seen in structured data. AI techniques of Natural Language Processing (NLP) use statistics & grammar systems to extract details from write-ups.

Data from many service visits creates large datasets (i.e., “big data”) to train AI models. Aggregated data statistics may help overcome inaccuracies in individual records.

AI with Big Data

AI trained on big data aims to capture the knowledge across many individuals, locations, and times.

Writing of Service Records is helped with currently popular large language models (such as ChatGPT). Using prompts from inspection & diagnostic AI, write-ups become clean, clear, complete yet succinct. This improves record-keeping, and related work, such as warranty booking & administration.

Data-driven analytical monitoring & reporting, or Business Intelligence (BI), tracks trends, issues, and opportunities. NLP provides detailed data input for BI, significantly improving plain old BI.

AI suggested targeted Inspections needed for each vehicle, rather than generic multi-point inspections. The AI learns from services done for similar vehicles, and identifies needs for a particular vehicle, which is better than finding overall needs for general classes of vehicles.

Service data enables Diagnostics, to find likely causes given the symptoms, and for best repair suggestions. AI also prioritizes repairs to help Fix-Right-First-Time by automatically learning how various repairs fare in fixing similar issues.

Overview diagnostics, with directions to nearby service locations, helps generate new Service Leads. Based on service data and owner demographics, such leads are accurate, relevant, and more often successful.

Service data helps Customer Relationship Management (CRM), such as with AI-generated vehicle health reports sent to owners. This supports better communication before, during, and after service visits, and builds greater trust.

Data-driven Service Price Guidance uses statistical queries helped by AI, providing service prices and their ranges. In fact, they provide price ranges by geographic regions, vehicle type, and other available service data fields.

AI without Big Data

Contrary to folklore, even smaller datasets may suffice when NLP extracts details from data:  Higher quality of data reduces need for larger quantity of data.

Service records are near-automatically assigned specific codes for Warranty Booking & Administration.  This helps with accuracy & rejections, increasing overall warranty reimbursements significantly.

Timely reminders sent to vehicle owners who decline or defer recommended services are likely to be heeded. AI for Declined Service Identification compares service needs to the services performed, and finds essentially all relevant cases. Cases with high-cost recommendations are also targets for vehicle sales (to replace the vehicles with the high-cost cases).

For more information, or to discuss data-driven AI strategies for your business, contact us at https://a2d2a2.com