Is AI important for DX? What are the steps to introduce AI to accelerate corporate DX?

Table of contents of this article

  1. Initiatives to realize DX and the importance of AI
  2. Steps to introduce AI to accelerate corporate DX
  3. The key to utilizing AI lies in the level of expertise and differences in management methods.

For companies aiming to realize DX, AI is a technology that can be a trump card. Many people may be considering introducing AI to promote their company’s DX. However, in order to expect high effectiveness, a basic understanding of AI itself is essential.

Therefore, in this article, we will explain the importance and relationship of AI in promoting DX, and the steps to introduce AI for companies who want to accelerate DX.

Initiatives to realize DX and the importance of AI

It is often said that AI is important for promoting DX, but why is that? First, I will explain the relationship between AI and DX and the characteristics of AI from the following three perspectives.

– The relationship between AI and DX is “means” and “purpose”
– Information and communication technologies such as IoT and 5G are supporting the business utilization of AI
– Some companies are starting to promote DX in earnest by using AI to solve business issues

The relationship between AI and DX is “means” and “purpose”

AI and DX can be said to be similar in that they both involve “using data to do new things.” For example, it may be easier to understand if you explain it as follows.

– AI: “Digital technology” that utilizes “data” to achieve things that were difficult in the past
– DX: A series of initiatives that utilize “data” and “digital technology” to transform companies

From the perspective of DX, AI is included as a candidate for digital technology that can be utilized. In other words, if DX is the “purpose,” AI is the “means” to that end. Therefore, AI is not essential for DX. However, they are often used because of their high affinity for using data together.

There is a page that explains the relationship between DX and AI in more detail, so please refer to it as well.

Information and communication technologies such as IoT and 5G support the business use of AI

What can we do with AI?

AI has the characteristic of making new “inferences” from the results of “learning” based on data. By using this mechanism, it becomes possible to use AI to accomplish things that could only be done by humans in the past. For example, AI can accomplish the following:

– Image and audio recognition
– Natural language interpretation
– Business automation

However, obtaining sufficient accuracy requires the use of large amounts of high-quality data. On the other hand, with the practical application of IoT and 5G, we have entered an era in which vast amounts of information are exchanged. If we can secure the amount of data, we can improve the accuracy of AI. It can be said that the environment is now in place to build AI that is sufficiently accurate and can be applied to business.

There is a page that explains in more detail what is possible with AI, so please refer to it as well.

Some companies are starting to move forward with DX promotion, starting with solving business issues using AI.

AI technology can be used to develop new businesses and improve existing services. In addition, AI is a technology that can help improve business operations. For example, you can expect the following effects.

– Reduce costs by improving work efficiency
– Reduce human errors and improve quality through automation
– Reduce unproductive work by streamlining business processes

You can see that the scope of application of AI is extremely wide. Therefore, there are many cases where the effects of introducing AI (=means) become a stimulus and develop into full-fledged DX (=purpose).

There is a page that explains the benefits of using AI in more detail, so please refer to it as well.

Steps to introduce AI to accelerate corporate DX

Steps to introduce AI to accelerate corporate DX

What steps should be taken to introduce AI? From here, we will explain the procedure for introducing AI that is suitable for companies aiming to quickly realize DX, divided into the following five steps.

– Step 1: Clarify the purpose of AI utilization
– Step 2: Secure and develop AI human resources
– Step 3: Collect and format learning data
– Step 4: Develop an AI model and incorporate it into the IT system
– Step 5: Continually improve accuracy

Step 1: Clarify the purpose of AI utilization

The first step is to clarify what you will use AI for.

This is an important step to avoid the use of AI becoming an end in itself. One of the typical failure patterns in DX is the tendency for organizations to focus so much on introducing AI that they stray far from what they should be as an organization. Avoid mistakes like this by remembering that AI is just one of the tools for DX.

The important thing is to clarify from a DX perspective what you want to achieve as a company and what kind of organization you want to become. The effects of AI implementation can only be expected if it is used in a way that matches the company’s mission and vision.

Step 2: Secure and develop AI human resources

Once the purpose of utilizing AI has been clarified, the next step is to secure human resources who can carry out the implementation of AI.

However, many companies will be facing a shortage of engineers at this stage. This is because AI is a highly specialized technology. For example, in terms of knowledge, in addition to the theory related to AI itself, it is also necessary to have a grasp of the basics of mathematics and programming. In terms of skills, it is necessary to be able to use specialized tools and actually operate AI on IT systems.

This shortage of “advanced IT human resources” has become a social issue, and it is not easy to secure new human resources from outside. Therefore, a solid method is to develop AI human resources in-house. The real challenge in introducing AI is “how to set up an AI education system.”

Step 3: Collect and format learning data

AI requires large amounts of data as a source of learning. In this step, we will collect and format the data for AI training.

Specifically, it will be necessary to collect data scattered within the company so that it can be managed centrally, and to create and procure new data. In addition, we will also perform “preprocessing” to format the data so that it can be read by AI. Preprocessing is an essential step to increase the accuracy of AI learning.

However, when handling data linked to “people” such as customer behavior history, it is necessary to consider how to handle personal information. For example, there are methods such as processing data so that individuals cannot be identified.

Step 4: Develop an AI model and incorporate it into your IT system

Develop AI models and incorporate them into IT systems

The next step is to develop an AI model and incorporate it into the IT system.

AI becomes a practical model by training it with an appropriate method. There are many AI learning algorithms, so you have to carefully decide which one to choose. In this case, specialized knowledge of data science and machine learning is required.

Once the AI ​​model is created, we will incorporate it into the IT system so that it can be applied to actual business operations. However, it is not guaranteed that a model with enough accuracy for practical use will be completed with just one learning session. Implementation will require planning, such as incorporating it into your business in stages.

Step 5: Continually improve accuracy

The final step is to improve the accuracy of the AI ​​model. To do this, it is necessary to repeat the process from step 3 onwards. This means revising the model itself through repeated experiments and verification.

In this iterative process, the challenge is how to control uncertainty and move closer to completion. An agile approach that starts small and makes improvements little by little is appropriate. It can be said that a management approach that is different from traditional IT system development, which is based on waterfall, is required.

The key to utilizing AI lies in the level of expertise and differences in management methods.

AI is a technology that has the potential to become a trump card for DX, depending on how it is utilized. In order to achieve the desired effects, highly specialized engineers and managers who can appropriately handle AI projects are required.

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