Setting Clear Goals when Starting with AI

Last week I wrote about experimenting with AI, and how many people don’t want to experiment while going it alone.

Taking a deeper dive into the steps to get started, let’s focus on Setting Clear Goals. What, exactly, do you want to accomplish? Below are a few ideas:

Predict Maintenance Timelines – If your goal is to do a better job predicting when maintenance should occur and preventing issues before they happen, look at the data you can collect (or has already been collected. For example, you can collect data on vehicle performance, past maintenance records, sensor readings, mileage, driving patterns, environmental conditions, and many other data points. You will need to make sure the data you have is clean. This means that it’s free from errors, missing values, and inconsistencies. In the software you’re having built, you can create proactive alerts based on predictive influences like engine temperature, oil levels, and tire pressure.

Improve Quality Control – If your goal is to achieve higher product quality, reduce defects, and attain more efficient manufacturing processes, you might want to focus on AI for improving quality control. You could collect data from the sensors on the production line, quality inspection records, defect reports, environmental conditions, and machine performance logs. As with any AI project, make sure your data is clean with an absolute minimum of errors, and includes data from across various systems for a holistic view of the production process. You’ll also want to pinpoint predictors of quality like temperature, humidity, machine settings, operator actions, and material properties.

Enhance Safety – When your goal is to enhance vehicle safety through data, there are several systems you’ll want to examine. For starters, Advanced Driver Assistance Systems (ADAS) generate a ton of data through computer vision and sensors. How well did the ADAS detect obstacles, pedestrians, traffic signs, and lane markings? Can you access this data? Based on the data, AI can recommend improvements to the automatic emergency braking, lane-keeping assistance, and adaptive cruise control, for example. If possible, collect data from data from multiple sources (e.g., radar, LIDAR, and cameras) which can be synthesized by AI to create a comprehensive feedback loop for improved protection. It may also be possible to analyze collision avoidance systems by analyzing real-time data from vehicle sensors to ascertain how well collision avoidance systems (like automatic breaking or steering adjustments) are working. If you have access to V2X (Vehicle-to-Everything) communication data, it may be possible to ask the AI to make recommendations related to drivers’ situational awareness.

Automate Regulatory Compliance – By using AI to automate regulatory and standards compliance, you can streamline processes, reduce the risk of non-compliance, and ensure that your vehicles consistently meet the highest standards. To start, you’ll want to build a knowledge base that continuously updates and interprets regulatory requirements from various global bodies (e.g., NHTSA, Euro NCAP, ISO). This knowledge base can then be used to train your AI. You’ll also want to ensure you have a proactive alert system setup so that your compliance team knows about changes or updates to regulations and standards, even before they become mandated. (Note: It can take a considerable amount of time to load all global regulations and standards into a knowledge base and also keep it up to date – Humaxa does this for clients specifically because it takes so much time to do and keep current.) You can gather data from various sources (e.g., testing results, manufacturing processes, supply chain data) for comprehensive analysis. This analysis can catch potential compliance issues before they arise, therefore enabling proactive management. Testing and validation can be automated as well. This can be accomplished by having the AI process test results and compare those test results with regulatory requirements, highlighting any deviations that need attention.

Personalize Customer Service – Driving brand loyalty and growth can be made easier by using AI to personalize customer service. If this is a top goal, you might be able to derive customer insights by training the AI on purchase history, service records, and interaction data, to gain deep insights into customer preferences and behaviors. You can also segment customers based on criteria such as buying behavior, service usage, and preferences to tailor your services using AI. You can implement AI-powered chatbots that provide personalized responses based on individual customers and previous interactions, send reminders for service appointments, personalized offers, or new vehicle features that a customer cares about, or send out dynamic offers and discounts based on customer activity and loyalty status. All of this can make past, current, and future customers feel valued and appreciated. Note also that Humaxa provides customized chatbots for automotive clients; we are always happy to help.

 

I hope that seeing a few examples of how setting specific goals around implementing AI in the Automotive Industry can help you move forward in a specific, intelligent way.

If you ever need help or want to chat about goal setting, please let me know. Thank you!

Carolyn Peer

CEO/Co-founder, Humaxa

carolyn.peer@humaxa.com

www.humaxa.com

Recent stories

Thinking of Experimenting with AI? Document your current processes.

When experimenting with AI, how one might want to go about documenting current processes

“I’ve been tasked with figuring out how my organization can and should experiment with AI. Great! Now what?”

How to choose a pilot project to experiment with AI in your organization

"I’m thinking about experimenting with AI. What do I need to know about involving stakeholders?"

What to consider when starting an AI pilot project in the Automotive Industry, specifically around stakeholder involvement