What is the background of the current demand for AI Implementation?

What Is The Background Of The Current Demand For AI Implementation?

Hello everyone, My name is Kenshin Fujiwara and I am the CEO and founder of HACARUS Inc. At HACARUS, I have been working with companies around the world to solve their problems for over 8 years by utilizing AI. From my experience, I believe that by having the right knowledge and industry know-how, the chances for success in implementing AI drastically improve. 

From today, I will start posting a series of blogs about a wide range of topics of AI, including an introduction to AI, practical tips to conduct AI projects successfully, how to set up an internal AI team, and how to select appropriate AI models. I hope my blog posts will help you to gain a better understanding of how to solve your company’s problems by using AI and what is possible. 

Al, Innovative business tools for enterprise 

AI has recently become a hot topic in the business field. However, when I talk to business executives who have applied AI into their businesses, I realized that its performance often does not meet their expectations. Moreover many of the entrepreneurs could not understand the effective way to implement AI technology in their business.

In that sense, I would like to share my opinion about why AI technologies do not work in many business fields. Before jumping into the topic, Let’s take a look at the background of AI applications in business on today’s blog.

What is digital transformation?

As AI has become a bit of a buzzword, we also often hear about digital transformation

(DX), which is an important concept when it comes to utilizing AI. DX, or digital transformation, is the use of data and digital technology to transform products, services, and business models to establish a competitive advantage, while making the organization more resilient to changes in the business environment. In other words, DX transforms a company by digitizing its business, and data plays a major role in  the progress of digital transformation.  

To explain this point, AI commonly uses data to perform “prediction,” “judgment,” and “simulation” tasks, which are conventionally performed by humans. For example, AI can judge the data captured by a high-definition camera, allowing it to visually sort out good and defective productions in the production process. This makes it possible to perform the process automatically and more accurately than humans. 

During this process, the condition of the product is digitized by the camera and the AI reads the video data. For this to work, the data can only be generated by incorporating digital tools into the existing operations. Looking at this from another perspective, without the data, AI cannot play an active role. Therefore, I believe that the combination of digitization, data, and AI is essential in promoting DX. 

Difference between digital transformation (DX) and digitization/IT

Although it varies by company, many organizations now have digital tools and systems across all of their operations. As such, these tools generate a large amount of data in their day-to-day operations. The current trend is to take that data and feed it to AI which in turn makes high-level decisions and creates new business for the company. This data is also used as a driving force for DX promotion. 

BI(Business Intelligence) and RPA(Robotic Process Automation) systems also play a key role in promoting DX, but there is actually a big difference between them and AI. Essentially, each of these tools is only a replacement for tasks that are performed by humans. This means that they only perform pre-programmed actions and cannot be expected to produce any additional outputs. For example, BI tools visualize information that is hidden behind the data, but human operators still need to look at this information and make decisions based on their judgment. 

On the opposite side of the spectrum, AI reads the generated data from these systems and produces sophisticated outputs such as decisions, predictions, and simulations. At the same time, AI continuously learns as it accumulates more and more data. This allows AI to become smarter, faster, and more precise to provide more accurate results. 

Autonomous learning of AI Characteristics

When I explain it like this, AI might seem like an all-powerful machine, which can be misleading. At the end of the day, AI is just a software, or in other words, just a collection of powerful programs. However, AI differentiates itself from other tools by learning autonomously to improve its performance over time. For example, let’s look at Microsoft Word, a well-known tool for digitizing text. While it is extremely useful, its performance does not improve no matter how much text is created. This is the major difference between AI and digitization. The ability of autonomous learning has led many individuals to place high expectations on AI with regard to problem-solving. 

In the next blog, I will look at how AI technology has advanced over the past 50 years, and in particular, I will introduce what role machine learning has played in AI technology progress. For your undated knowledge and insight about AI technology, subscribe to our newsletter or visit HACARUS website https://hacarus.com.

 

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