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

First, congratulations! If you’ve been tasked with figuring out how your organization could run some experiments and start using AI, you’ve been bestowed with a great future and a great responsibility.

Next, your top priority will be to figure out WHAT problems would be the best to try solving with AI. It’s also important to distinguish between “traditional” AI (i.e., data science, machine learning) and LLMs (large language models.) Please see my previous article on 11Steps to Build an AI Roadmap for Automotive Manufacturers and Suppliers(humaxa.com) It’s likely that you’ll want to solve different problems using each or use them in concert (like we doat Humaxa) to solve complex problems.

Let’s jump in.

What are your organization’s goals for the year? That’s a great place to start. If your company is like many others, these corporate goals might include:

1.     Electrification

2.     Sustainability

3.     Vehicle Connectivity

4.     Improving Customer Service

5.     Enhancing Service Models

6.     Reduce Manufacturing Waste

7.     Use technology in production lines to become more efficient

8.     Build more resilient and flexible supply chains to mitigate disruptions

9.     Expand into new markets

10. Establish local production facilities to reduce logistics cost

11. Ensure compliance with regional environmental laws

12. Enhance vehicle safety features to comply with evolving safety regulations

13. Reduce costs through efficiencies and supply chain optimization

14. Outpace the competition through better R&D investment decisions

Of course, these are just a few examples of over-arching objectives for an Automotive Supplier or Manufacturer. Do they look familiar?

Now, the big question is: Of all the organizational goals for the year, which could AI impact the most?

Improving customer service: This could be a great choice for an AI pilot project. Why? Because AI chatbots can make the customer service experience highly personal. For example, you might be able to implement a recommender system for customers who are looking for help solving a vehicle warranty issue.

Reducing costs through efficiency and supply chain optimization: There is a lot of waste generated through the manufacturing process. It’s possible for AI to optimize manufacturing processes through predictive analytics, quality control, and process automation. Selecting just one of these could be a good way to get an AI pilot off the ground.

Use technology in production lines to become more efficient: It might be possible to analyze data from production lines sensors to predict equipment failures and improve overall efficiency. By predicting equipment failures before they happen, an organization can reduce downtime and reduce maintenance costs. It’s also simple to pilot such an AI project by focusing on one production line for some early wins.

Ensure compliance with regional environmental laws: By using an AI platform like Humaxa’s that’s already trained and kept up to date on global and regional environmental laws, it’s possible to enhance workers whose job it is to ensure compliance with environmental laws. This not only results in cost avoidance (i.e., non-compliance fines) but it also speeds up the process to confirm compliance.

These are just a few ideas of how to choose small, impactful pilot projects to demonstrate the impact of experimenting with AI. We at Humaxa specialize in helping organizations navigate these waters, especially in the automotive industry. Please let us know if you’d like to learn more.


Carolyn Peer



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