Everything is AI – How many do you want?

Everything is AI – How many do you want?

The rise of artificial intelligence (AI) has led to a lot of hype, and it seems that everything is being labeled as “AI-powered” to promote products. However, the reality is that we are still far from achieving true AI, and many organizations are pushing AI for the sake of appearing up-to-date. As customers, we should question the use of AI and refrain from paying a premium for products with an AI label as most of them do not implement machine learning.

Before considering machine learning or AI, it’s important to understand the difference between the two. Machine learning is a type of AI that involves the ability of computers to learn without being explicitly programmed. Understanding this difference is crucial to appreciate the benefits of using AI and machine learning in the right context.

Building meaningful solutions that incorporate machine learning requires a significant investment of time and money. This steep learning curve often necessitates a transformation of an organization’s products and services, as well as its internal processes. This transformation is a fundamental aspect of digital transformation, which involves transforming the organization to apply technology effectively in everything it does. Therefore, there must be a strong business rationale for investing in machine learning or AI.

Examples of meaningful applications of AI and machine learning include digital learning, preventive maintenance, and IoT. For example, machine learning can provide customized training, help leaders understand how to help their staff’s professional development, and move from traditional to data-driven business and service models in industries. These examples demonstrate how AI and machine learning can provide significant business value, but they require more than just technology.

The technology itself is not the issue. It’s the specific expertise required to determine which technology to apply and how to apply it. Finding real experts in the field can be challenging but not impossible.

The most challenging aspect of machine learning is the data. It dictates what is possible and not possible in many cases, and poor solutions that are taken to market too early can have an adverse effect on a company’s reputation and customer confidence. Therefore, understanding the importance of data and how it affects machine learning is crucial.

Finally, starting small and experimenting with AI and machine learning can lead to valuable feedback from customers. There is no shame in starting small and testing the market to determine what customers want. This approach can lead to a positive effect on a company’s reputation even if the experiment fails.

In conclusion, the use of AI and machine learning in business is not about how many AI’s a company has. Instead, it’s about how these technologies can improve and extend the company’s offering and make it more meaningful. To achieve this, businesses must have a strong business case and customer need and understand the new technologies. So, it’s not about whether you want a green or red AI; it’s about what AI can do for your business.