What are the barriers that prevent the smooth progress of AI projects? (Part 3)

What Are The Barriers That Prevent The Smooth Progress Of AI Projects? (Part 3)

Hello everyone, My name is Kenshin Fujiwara and I am the CEO and founder of HACARUS Inc. 

Through this series of blogs, I will discuss a wide range of topics of AI, from the history of AI to practical tips for successful application of AI projects. I hope my blog posts will help you gain a better understanding of AI and solve your business issues.

In the last two blogs, we studied four barriers that prevent the smooth progress of AI projects in the business field. As the last part, today’s blog will explain the human resource barrier, which I noticed through my working experiences. In addition, I would like to propose a new option for solving these problems.

The Human Resource Barrier

In addition to the four barriers mentioned in the previous blogs, I would like to explain one more barrier that I have noticed through my years of experience with client projects. This fifth barrier is human resources, which are used to support everything. When it comes to AI, no matter how much data you have, how many tools you accumulate, or how much industry knowledge you have, a project will always be filled with uncertainties. Implementing AI also requires a team of people who deeply understand how AI works and how to apply it to a business. 

Speaking from experience, a decade ago, there were only a few data utilization projects so we managed to get around with limited human resources. However, as times are changing, the need for AI personnel is rapidly increasing due to the explosion of AI development. 

Although the number of AI experts is beginning to increase, there is still a large shortage of human resources. Even in this state, I don’t think that this should be an issue for most companies. Most companies shouldn’t need a large team of AI specialists who can write code and perform advanced data analysis from the beginning. 

Of course, it is always advantageous to have such personnel. But in most cases, people who have intricate industry knowledge and project management skills are more useful to a business. The best case is if they also have a good understanding of AI and data analysis. In cases where this isn’t possible, technical areas, such as data analysis and AI construction, can be covered by external parties. Therefore, it is crucial to develop employees who understand the business and can communicate appropriately with external technical partners. 

Challenges of Deep Learning

Previously we discussed deep learning, which is now considered mainstream, and how it can be used as a powerful tool in business. However, it also comes with several challenges. In some cases, these hurdles have led to the failure of AI implementation projects.

AI technology is a great tool, and in fact, machine learning and deep learning are only a small part of the AI technologies. Thus, I encourage you to check out AI research and other AI developments because many Al models and methods have been developed and explored. Among them, I would like to propose sparse modeling as a potential solution to the challenges facing current deep learning methods based on the following characteristics.

Characteristics of Sparse Modeling

  1. Sparse modeling does not require a large amount of information and can be analyzed using small amounts of data.
  2. High interpretability eliminates the issue of black box.
  3. Sparse modeling does not require expensive GPUs, which have high levels of energy consumption. This keeps hardware, AI implementation, and operating costs relatively low.

Additionally, selecting the right AI technology that matches your business and operations can lower the hurdles of AI implementation. In some cases, this can lead to the resolution of business issues that were not possible before. While promising, the introduction of sparse modeling into a business will not necessarily lead to this outcome. Moving forward, I think it is a good idea to reiterate how to work with various objectives and circumstances. 

In the next blog, I will explain the difference between Digital Transformation(DX) and Digitization. For your updated knowledge and insight about AI technology, subscribe to our newsletter or visit HACARUS website https://hacarus.com.

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