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 own DX. However, in order to expect high effects, 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 that want to accelerate DX.
Efforts to realize DX and the importance of AI
It is often said that AI is important for promoting DX, but why is that? First, we 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 a “means” and a “purpose”
– Information and communication technologies such as IoT and 5G support the use of AI in
business
The relationship between AI and DX is “means” and “purpose”
AI and DX are similar in that they both use data to do new things. For example, it may be easier to understand if explained 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 one candidate for digital technology that can be utilized. In other words, if DX is the “purpose”, AI is the “means” for that purpose. So AI is not a must for DX. However, due to the high affinity of using data together, it is actually often used.
Regarding the relationship between DX and AI, there is a page that explains in more detail, so please refer to it as well.
Information and communication technologies such as IoT and 5G are boosting the use of AI in business
What can we do with AI?
AI has the characteristic of making new “inferences” from the results of “learning” based on data. Using this mechanism will expand the possibilities of using AI to achieve things that could only be done by humans in the past. For example, AI can do the following:
– Image and voice recognition
– Natural language interpretation
– Business automation
However, to obtain sufficient accuracy, a large amount of high-quality data must be used. On the other hand, against the backdrop of the practical application of IoT and 5G, we are now in an era in which a huge amount of information is exchanged. If we can secure the amount of data, we can improve the accuracy of AI. It can be said that the environment for building AI with sufficient accuracy and a level that can be applied to business has been put in place.
There is a page that explains in more detail about what is possible with AI, so please refer to it as well.
Some companies have started full-scale promotion of DX from solving business problems with AI
Using AI technology will allow us to develop new businesses and improve existing services. In addition to that, AI is a technology that is useful for business improvement. For example, the following effects can be expected.
– Reduce costs by improving work efficiency
– Reduce human errors and homogenize quality through automation
– Reduce unproductive work by streamlining business processes
As you can see, the scope of application of AI is very wide. Therefore, there are many cases where the effect of introducing AI (= means) is stimulating and develops into full-fledged DX (= purpose).
Steps to introduce AI to accelerate corporate DX
What is the best way to introduce AI? From here, we will explain the AI introduction procedure that fits companies aiming for early realization of DX, divided into the following five steps.
– Step 1: Clarifying the purpose of AI utilization
– Step 2: Securing and training AI personnel
– Step 3: Collecting and shaping learning data
– Step 4: Developing an AI model and incorporating it into the IT system
– Step 5: Continuously improve accuracy
Step 1: Clarify the purpose of AI utilization
The first step is to clarify what the AI will be used for.
This is an important step in order to avoid using AI itself becoming an end in itself. One of the typical failure patterns in DX is the tendency to focus too much on the introduction of AI and deviate from what the organization should be aiming for. Let’s avoid this kind of failure by reminding ourselves that AI is just one of the means for DX.
The important thing is to clarify what you want to achieve as a company and what kind of organization you want to be from the perspective of DX. You can expect the effects of introducing AI only when you use it in a way that matches your company’s mission and vision.
Step 2: Secure and develop AI human resources
Once the purpose of using AI is clarified, the next step is to secure human resources who can implement the introduction of AI.
However, many companies will face a shortage of engineers at this stage. AI is one of the most highly specialized technologies. For example, in terms of knowledge, in addition to the theory of AI itself, it is necessary to grasp the basics of mathematics and programming. In terms of skills, it is necessary to master specialized tools and have the ability to actually operate AI on IT systems.
Such a shortage of “advanced IT human resources” has become a social issue, and it is not easy to secure new human resources from outside. Therefore, it is a solid method to develop AI human resources in-house. It can be said that “how to prepare an AI education system” is the real issue in introducing AI.
Step 3: Collect and format training data
AI requires large amounts of data as a source of learning. In this step, we will collect and format AI learning data.
Specifically, it will be necessary to collect data that is scattered throughout the company so that it can be centrally managed, and to create and procure new data. At the same time, we will also perform “preprocessing” to format the data so that it can be read by AI. Preprocessing is an essential process for improving the accuracy of AI learning.
However, when dealing with 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
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 appropriate methods. There are many AI learning algorithms, so we have to carefully decide which one to choose. At that time, 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 operations. However, it is not always possible to complete a model with accuracy that can withstand practical use with just one learning. The introduction will also require planning, such as gradually incorporating it into the business.
Step 5: Continuously Improve Accuracy
The final step is to improve the accuracy of the AI model. To do so, it is necessary to repeat the process from step 3 onwards. It means that the model itself is reworked while repeating experiments and verifications.
In such an iterative process, the challenge is how to control uncertainty and get closer to perfection. It can be said that the agile approach , which starts small and accumulates improvements little by little, is suitable. It can be said that management with a different conception from conventional IT system development based on waterfall is required.
The key to utilizing AI lies in the high 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 used. To achieve the desired effect, engineers with a high degree of expertise and managers who can handle AI projects appropriately are required.
Our company provides training services for this purpose, “AI Engineering Course” and “AI Management Course”. It is a service that can be used to solve problems related to human resource development for AI projects, so please make use of it.
