The problem with experimenting with AI is, it’s new! It’s only been around for a short time and in its short tenure, it’s received both rave reviews and demonstrative insults. However, most people can agree that it’s changing our world forever – perhaps similar to the way the Internet did back in the 1990s. Whether we embrace or abhor change, experimentation is on many people’s minds. But when few organizations have a defined budget for “new AI efficiency tools,” how do you carve out your spot in the AI sun?
Let’s break it down to see what makes sense. Initially, it’s important to decide whether you want to go it alone, or work with a partner.
If you are going it alone, you will need to budget for:
Hardware and Infrastructure: AI solutions often require significant computational power, including servers, cloud services, GPUs, or specialized hardware like TPUs.
Software and Tools: Depending on your AI strategy, you may need to invest in AI development platforms, machine learning frameworks, data processing tools, and security solutions.
Talent and Expertise: Acquiring skilled AI professionals like LLM developers, Prompt Engineers, or AI consultants can be a substantial cost. Of course, you can also upskill your existing workforce, and you may want to do that anyway – but that will likely delay experimentation for months, or even years.
Data: As I’m sure you know, AI models need high-quality data for training. Collecting, cleaning, and storing large datasets can be costly. There are ways to automate much of this, but not always.
It will also be important to think about operational costs such as Cloud costs (or server costs, if you’re going to self-host), maintenance and monitoring, compliance, and data security.
What about the costs to scale your AI solution?
Model Iterations and Retraining: AI models often need constant retraining with updated data or to improve accuracy. For example, at Humaxa, we are constantly monitoring new legislation, proposed compliance laws, and upcoming changes with regard to regulations important to the automotive industry. (We do that for our clients, so they don’t have to spend time doing it themselves, while cashing in on economies of scale.)
Deployment and Integration: Do you want your AI solution to integrate with existing systems, like your ERP, CRM, DOORS, or other manufacturing systems? If so, this can involve custom development and testing.
Pilot to Production Transition: Moving from a pilot AI project to a production-ready solution can bring additional costs in terms of infrastructure upgrades, scalability improvements, and more robust testing.
Ok, ok, ok – I get it. All of this costs money. Maybe it’s better to just wait it out, right?
Let’s think about it. There is a myriad of reasons to carve out a budget for an experimental AI project, but let’s focus on one big one: Competitive advantages.
AI is becoming a key differentiator in the automotive industry. Companies leveraging AI are developing next-gen vehicles, achieving manufacturing efficiencies, and offering enhanced services. AI can be considered essential for staying competitive.
AI is transforming the automotive landscape and those who don’t consider how to experiment with AI will simply fall behind. From autonomous vehicles to connected car services and smart factories, competitors are making significant AI investments. Failing to adopt AI could leave your company lagging behind in innovation and market share. And nobody wants that.
AI is coming at us at a race car pace and that makes it difficult to navigate. However, getting organized around a specific pilot effort and learning to budget and speak the right language could be the defining factor to stand out in the automotive industry.
It might just be worth it!
Carolyn Peer
CEO/Co-founder, Humaxa
