What Sets Successful AI-Adopting Companies Apart? A Deep Dive into Three Common Traits

[With Case Studies] What is the Difference Between Companies that Have Successfully Implemented AI?

The introduction of AI (artificial intelligence) has become an important element in corporate growth strategies. The benefits of AI, such as improved business efficiency, cost reduction, and the creation of new business opportunities, are immeasurable. However, the introduction of AI does not always lead to success. What do companies that have successfully introduced AI have in common?

In this article, we will take a deeper look at three commonalities between companies that have successfully implemented AI, and explain them with concrete examples. In addition, we will introduce the steps to successfully implement AI, common examples of failure and how to deal with them, post-implementation issues and solutions, and the latest trends in AI implementation.

Three commonalities between companies that have successfully implemented AI and a deeper look

Companies that have successfully implemented AI have three things in common: Understanding these things and applying them to your own AI implementation strategy will increase your chances of success.

1. A clear sense of purpose and strategy
2. Data-driven approach
3. Organizational culture and human resource development

Commonality 1: Clear sense of purpose and strategy

In order to successfully introduce AI, it is important to first clarify the purpose of introducing AI. Simply having a vague idea of “wanting to introduce AI” will likely lead to the project going off track and ending in failure.

The purposes of introducing AI are diverse, including solving specific business problems , creating new businesses , improving customer experience , etc. Analyze your company’s issues and goals and clearly define what results you expect from introducing AI.

Once the purpose is clear, the next step is to set goals and KPIs (key performance indicators) for AI implementation . KPIs are indicators for measuring the effectiveness of AI implementation, and it is important to set both quantitative goals (sales increase rate, cost reduction rate, etc.) and qualitative goals (improved customer satisfaction, improved employee engagement, etc.).

to create a roadmap for your AI implementation project . The roadmap should include short-term and long-term goals, a feasible schedule and budget, etc. Creating a roadmap makes it easier to manage the progress of the project and clarifies the path to success.

For example, one manufacturing company clearly defined the purpose of introducing AI as “reducing costs and improving quality by automating product quality inspection.” They then set KPIs of “reducing inspection time by 30%” and “improving defective product detection rate by 50%,” and created a roadmap with specific schedules and budgets. This clear sense of purpose and strategy contributed greatly to the success of the AI introduction project.

Commonality 2: Data-driven approach

A data-driven approach is essential for successful implementation of AI. Data-driven means making decisions and taking actions based on data. AI can learn from large amounts of data, capture patterns and characteristics, and make predictions and judgments.

When introducing AI, it is important to first identify the necessary data and establish a system for collecting and storing it . The type of data will vary depending on the purpose of introducing AI, but various types of data can be used, such as customer data, sales data, product data, and sensor data.

At the same time as collecting data, it is also important to manage the quality of the data . Incorrect or incomplete data can reduce the accuracy of the AI model. In addition, it is necessary to comply with laws such as the Personal Information Protection Act and to give due consideration to protecting the privacy of data .

The collected data is analyzed using data analysis tools to extract useful information. Data analysis can reveal customer needs and behavioral patterns, product quality issues, bottlenecks in business processes, and more. This information is useful for building AI models and formulating business strategies.

Once an AI model is built, its accuracy is verified and adjustments are made as necessary. Also, building an AI model is not the end of the process. By continuously analyzing data and improving the model , more accurate predictions and judgments can be made.

For example, one retailer has built a demand forecasting system that uses AI by collecting POS data, customer attribute data, weather data, etc. This system optimizes inventory by analyzing past sales data and external factors and predicting product demand. This has reduced costs and opportunity losses due to excess inventory, and contributed to increased sales.

Commonality 3: Organizational culture and human resource development

Organizational culture and human resource development are also important factors for the successful introduction of AI. AI is merely a tool, and it is humans who utilize it. Without human resources who can use AI effectively, the benefits of introducing AI cannot be maximized.

First of all, it is essential to improve AI literacy . Everyone from management to field personnel must acquire basic knowledge about AI and understand its advantages and disadvantages, ethical issues, etc. Having correct knowledge about AI can eliminate misunderstandings and anxieties about AI and reduce resistance to its introduction.

Next, we need to develop AI human resources . We need to develop not only specialized human resources such as AI engineers and data scientists, but also human resources with the skills to use AI in their work. We should provide opportunities for people to acquire knowledge and skills about AI through in-house training and external seminars.

also important to raise awareness of AI use throughout the organization . Foster a culture of using AI throughout the organization by promoting AI implementation projects throughout the company and introducing incentive systems to promote AI use. Communication to help employees understand the changes that will come with the introduction of AI is also important.

For example, when one financial institution launched an AI implementation project, it conducted AI training for all employees. This improved employees’ AI literacy and gained their understanding and cooperation in the implementation of AI. In addition, the institution also took steps to raise employees’ awareness of the use of AI, such as holding a contest to propose new services that utilize AI.

These three commonalities are important factors for successful AI adoption. By taking these commonalities into account and developing and implementing an AI adoption strategy that suits your company’s situation, you will be able to lead the way to success in AI adoption.

Common mistakes in introducing AI and how to deal with them

The introduction of AI is an attractive option for many companies, but it does not always succeed. Here we explain common failures in the introduction of AI and how to deal with them. By understanding these failures and taking measures in advance, you can lead your AI introduction project to success.

Failure Example 1: Unclear purpose

If you proceed with a project without clarifying the purpose of introducing AI, the direction of development will not be clear, and you may not achieve the expected results.

Solution: Define the purpose of introducing AI specifically and clarify the goals you want to achieve. It is also important to set KPIs (key performance indicators) to achieve the goals so that you can quantitatively evaluate the progress of the project.

Mistake #2: Lack of data

AI improves its accuracy by learning from large amounts of data. If there is insufficient data, the AI model may not perform to its full potential and may not achieve the expected results.

Countermeasures: Before introducing AI, establish a system for collecting and accumulating the necessary data. It is important to make a thorough plan in advance regarding the data collection method, data format, data quality control, etc. Also, consider using external data sources.

Failure example 3: Lack of human resources

The introduction of AI requires human resources with specialized knowledge and skills in AI. A shortage of AI human resources can make it difficult to develop and operate AI systems, which can lead to project delays or failure.

Countermeasure: Be proactive in developing AI human resources. It is important to improve employees’ AI literacy through in-house training and external seminars. It is also effective to enlist the help of external experts such as AI vendors and consulting companies.

Mistake #4: Excessive expectations

AI is not omnipotent. If you have excessive expectations of AI, you may be disappointed by the gap between reality and your expectations, and may abandon your AI implementation project.

Countermeasures: Understand what AI is good at and what it is not good at, and clarify what AI can and cannot do. It is important to recognize that AI is merely a tool to support human activities.

Failure example 5: Lack of cost awareness

Introducing AI requires not only initial costs, but also operation and maintenance costs. Without cost awareness, the benefits of introducing AI may not be realized, and the project may be stalled.

Solution: Before introducing AI, thoroughly calculate the cost-effectiveness. Not only the initial cost, but also operation and maintenance costs and personnel training costs must be taken into account. It is also important to quantify the benefits of introducing AI and determine whether the return on investment is commensurate with the cost.

Issues and solutions after introducing AI

Introducing AI is not the goal, but the starting point. Even after introducing AI, you may face various challenges. Here, we will explain the typical challenges that may arise after introducing AI and how to solve them.

Challenge 1: Operational costs

Operating an AI system requires various costs, including hardware, software, and human resources. In particular, when introducing a large-scale AI system, the operating costs can become a significant burden.

solution:

• Utilizing cloud services: By using cloud services, you can reduce initial investment and build a scalable environment. In addition, you can leave operation and maintenance to the cloud vendor, reducing your company’s burden.
• Improve operational efficiency: Identify the personnel and skills required to operate and maintain the AI system and build an efficient operational system. It is also effective to utilize tools and services that automate AI operations.
• Verify cost-saving effects: Regularly verify the cost-saving effects achieved by introducing AI, and review and improve the system as necessary.

Challenge 2: Security

Security measures are important because AI systems handle large amounts of data. If confidential information such as personal information or trade secrets is leaked, not only will it damage the company’s credibility, but it could also be held legally liable.

solution:

• Strengthen security measures: Strengthen the security measures of AI systems. It is important to take multi-layered security measures, such as access control, encryption, and vulnerability diagnosis.
• Compliance with AI ethics: Care must be taken to avoid ethical issues in the development and use of AI. It is important to comply with AI ethics guidelines and build transparent AI systems.
• Security education: Educate your employees about the security of AI systems and raise their security awareness.

Challenge 3: The black box problem

AI models such as deep learning have complex structures, making it difficult for humans to understand why they output certain results. This “black box problem” is an important issue because it raises questions about the reliability and accountability of AI.

solution:

• Introducing Explainable AI (XAI): XAI is a technology that explains the reasons for AI decisions in a way that humans can understand. By introducing XAI, we can increase the transparency of AI and improve its reliability.
• Visualizing AI models: Using tools that visualize the internal structure and learning process of an AI model makes it easier to understand how the AI behaves.
• Final decision by humans: Do not blindly accept the decisions made by AI, but make sure that the final decision is made by a human. In particular, when making important decisions, it is important to thoroughly check the basis for the AI’s decisions.

Challenge 4: Data bias

If the training data for an AI model is biased, it may output results that reflect that bias. For example, an AI trained with data that is biased toward a particular gender or race may make discriminatory decisions.

solution:

• Use diverse datasets: It is important to use unbiased and diverse datasets to train AI models.
• Debias Data: Apply techniques to debias data during preprocessing.
• Continuously monitor your AI models: Continuously monitor the output of your AI models to check for bias.

It is important to predict issues that may arise after introducing AI and take measures in advance. In addition, since AI technology is evolving every day, it is also important to always keep an eye on the latest information and update and improve the system as necessary.

Steps to successful AI adoption

Taking the right steps can increase the chances of success when implementing AI. Here are three steps to ensure your AI implementation project is successful.

Step 1: Prepare for AI

When preparing to introduce AI, keep the following three points in mind:

1. Current situation analysis and problem identification: First, analyze your company’s current situation and specifically identify the problems you want to solve by introducing AI. Understand the current situation from various perspectives, such as business processes, data, and human resources, and clarify the purpose of introducing AI.
2. Setting objectives and goals for AI implementation: Set specific objectives and KPIs to achieve the objectives of AI implementation. KPIs are indicators for measuring the effectiveness of AI implementation, and it is important to set both quantitative objectives (sales increase rate, cost reduction rate, etc.) and qualitative objectives (improved customer satisfaction, improved employee engagement, etc.).
3. AI vendor selection: Select a vendor that can help you introduce AI. Compare the vendors’ technical capabilities, track records, support systems, etc., and choose a vendor that best suits your needs. It is also effective to consult with companies that provide consulting services for introducing AI.

point:

• It is important to form an AI implementation project team and include representatives from each department and AI experts as members.
• Communicate closely with internal stakeholders (management, field staff, etc.) and strive to gain their understanding and cooperation regarding the introduction of AI.
• It is also important to collect information about AI implementation and keep up with the latest technological trends and case studies.

Step 2: Implementing AI

When implementing AI, keep the following four points in mind:

1. Data collection and preprocessing: Collect and preprocess the data required for training the AI model. It is important to plan in advance how to collect data, what format it will be in, and how to manage the quality of the data.
2. Building and training an AI model: Build and train an AI model based on the collected data. Building an AI model requires specialized knowledge, so we recommend getting support from experts such as AI vendors or consulting companies.
3. System integration and testing: The constructed AI model is integrated with the existing system and tested. During testing, the accuracy and performance of the AI model are verified, and any problems are corrected.
4. Pilot operation: The AI system is operated on a trial basis in some departments and business operations to verify its effectiveness and identify any issues. Based on the findings from the pilot operation, the AI system is improved and preparations are made for full-scale implementation.

point:

• It is important to regularly check the progress of your AI implementation project and address any issues early on.
• Training an AI model can take time, so be sure to plan your schedule well in advance.
• During the pilot operation, actively incorporate the opinions of field staff and make improvements to improve the usability and effectiveness of the AI system.

Step 3: Post-AI operation

Even after introducing AI, keep the following three points in mind to make continuous improvements.

1. Effect measurement and improvement: Measure the effect of AI implementation and evaluate the degree of goal achievement. Measure the effect based on KPIs, and if there are areas for improvement, modify the AI model or system.
2. Continuous training and updates: AI models can be trained on new data to maintain and improve their accuracy. Periodically retrain your models to keep them up to date with the latest data.
3. Establishment in the organization: To establish an AI system in the organization, continuous support is required, such as employee training and manual preparation. It is also important to inform employees of the benefits of introducing AI and take measures to promote the use of AI.

point:

• Introducing AI is not something that is completed once it is introduced. Continuous operation and improvement are important.
• To maximize the benefits of introducing AI, it is necessary to transform not only the AI system but also business processes and organizational culture.
• The introduction of AI should be viewed as a company-wide initiative, and an environment should be created in which everyone, from management to field staff, can actively participate in the use of AI.

Following these steps will ensure your AI implementation project is successful and will maximize the power of AI.

Latest trends in AI adoption

AI technology is evolving every day, and new trends are emerging in the adoption of AI in companies. Here are three of the latest AI adoption trends. Understanding these trends will help you make your company’s AI adoption strategy more effective.

No-code AI development platform

Traditionally, the development of AI systems required specialized knowledge and skills, but in recent years, “no-code AI development platforms” have appeared and are attracting attention. These platforms allow you to build AI models based on a GUI (Graphical User Interface), making it possible to develop AI systems without any programming knowledge.

The benefits of a no-code AI development platform include:

• Shortened development time: AI models can be built without the need for programming, significantly shortening development time.
• Cost reduction: Development costs can be reduced because there is no need to hire specialized personnel such as AI engineers.
• Flexibility: Models can be easily modified and improved using a GUI, allowing for flexible response to changes in the business environment.

No-code AI development platforms have the potential to lower the barrier to AI adoption and enable more companies to utilize AI.

Utilizing large-scale language models such as GPT-4

GPT-4, developed by OpenAI, is a large-scale language model (LLM) released in 2023, and its advanced natural language processing capabilities have attracted attention. GPT-4 has demonstrated high performance in a variety of tasks, including sentence generation, translation, summarization, and question answering, and has the potential to accelerate the adoption of AI in companies.

Examples of how GPT-4 can be used include:

• Customer Support: Developing chatbots to automatically answer customer inquiries
• Content Creation: Auto-generation of blog posts, news articles, ad copy, etc.
• Translation: Multilingual website and app development
• Data analysis: Extracting useful information from large amounts of text data

GPT-4 is provided as an API, so it can be incorporated into various systems and services. By utilizing GPT-4, companies will be able to develop more advanced AI solutions and grow their business.

The spread of edge AI

Edge AI is a technology that performs AI processing on the device side (edge) rather than in the cloud. The advantages of Edge AI include the following:

• Low latency: No communication with the cloud is required, enabling real-time processing.
• Security: Data never leaves the device, reducing security risks.
• Cost savings: Reduce your cloud bills.

Edge AI is expected to be installed in various devices such as IoT devices, smartphones, and drones, and to make our lives more convenient. For example, Edge AI is being used in a variety of fields, such as detecting defective products on factory production lines and translating in real time using smartphone cameras.

Summary: The key to successful AI implementation is a clear sense of purpose, a data-driven approach, and organizational culture and human resource development.

The adoption of AI is a powerful tool that can accelerate business growth, but three elements are essential for its success: a clear sense of purpose, a data-driven approach, and organizational culture and human resource development.

a clear sense of purpose and strategy will be the compass to lead your AI implementation project to success. By clarifying why you are introducing AI and what results you expect, and setting specific goals and KPIs, you can manage the progress of the project and make effective decisions.

A data-driven approach is the key to maximizing the capabilities of AI. Collecting and storing high-quality data and using analytical tools to extract useful information from the data can lead to improved accuracy of AI models and optimization of business strategies.

Organizational culture and human resource development are the foundations for successful AI adoption. By developing human resources with knowledge and skills in AI and fostering a culture of using AI throughout the organization, the effectiveness of AI adoption can be maximized.

As can be seen from the success stories of AI implementation, a well-balanced combination of these three elements is the key to successful AI implementation. Companies considering introducing AI should refer to these common points when building their own AI implementation strategy.

AI technology is evolving every day, and new trends are expected to continue to emerge. It is important to understand the latest trends, such as no-code AI development platforms, large-scale language models such as GPT-4, and edge AI, and consider how you can use them in your business.

Introducing AI is no easy task, but with a clear sense of purpose, a thorough data-driven approach, and a strong focus on organizational culture and human resource development, you can successfully introduce AI and make a significant contribution to your company’s growth.

I hope this article will be of some help to companies considering introducing AI.