Hello everyone, I'm Tak@! My hobby is developing web services utilizing generative AI, and I also work as a system integrator holding AWS and Azure AI certifications.

This time, I'd like to focus on a topic many companies are currently facing: "Introducing Generative AI into Organizations." I'll clearly explain the barriers that stand in the way and concrete measures to overcome them.

Let's explore tips for maximizing AI's power and helping businesses grow.

Why Is Generative AI Essential for Business Efficiency Now?

Challenges in the Modern Business Environment and the Potential of AI

The modern business environment is challenging for many companies. Particularly in Japan, the declining birthrate and aging population have led to a severe labor shortage, and there's a growing demand for shorter working hours and more flexible work styles.

In this situation, companies face the difficult task of "creating more value with fewer people." AI (Artificial Intelligence) is gaining attention as a powerful means to solve this challenge.

Introducing AI isn't just about digitizing operations; it accelerates digital transformation (DX) and enables companies to adapt to new work styles, including the widespread adoption of remote work.

By entrusting repetitive tasks to AI, people can focus on more creative and uniquely human value-generating activities. This is a crucial mindset for businesses to maintain and enhance their competitiveness.

Three Ways AI Contributes to Business Efficiency

AI primarily contributes to business efficiency in three ways:

1. Automation: AI performs routine tasks, reducing the time people spend on them. Examples include mass data entry and repetitive document creation.
2. Augmentation: AI supports human judgment, improving and speeding up decision-making. This includes making future predictions based on data analysis or analyzing customer behavior to make appropriate suggestions.
3. Analytics: AI helps find useful patterns within vast amounts of data, assisting in strategy development, problem identification, and resolution. Specific examples include identifying wasteful advertising, detecting unauthorized access, and predicting customer purchases.

The main goals for companies adopting AI can be categorized into three areas: "reducing costs," "improving operational quality," and "creating new value." For Japanese companies in particular, automating tasks to compensate for labor shortages and transferring the knowledge and skills of experienced employees to AI are crucial objectives.

"Invisible Walls" in Generative AI Adoption and Their Reasons

While many companies have high hopes for generative AI adoption, they often encounter various obstacles during implementation. In some cases, they don't achieve the expected results or even fail. It's like buying a car but having no one who knows how to drive it.

1. The Wall of Unclear Objectives

A common reason for unsuccessful AI implementation is that "adopting AI itself becomes the goal." Projects often proceed with vague objectives about why AI is being used.

This is often due to insufficient in-depth analysis of current operations before implementation.

For example, when a retail company tried to introduce generative AI to streamline LP (landing page) creation, even though some efficiency had already been achieved through template usage, the specific scope of what AI would handle wasn't clear.

As a result, AI implementation sometimes complicated operations, even increasing the need for human involvement. Without clear objectives, it's impossible to establish criteria for measuring post-implementation effects or evaluate whether the investment of effort yielded worthwhile results.

2. The Wall of Misalignment and Resistance from the Front Lines

Even if management is eager to adopt AI, frontline employees sometimes react coolly, thinking, "Oh, another new tool…"

This "temperature gap" is a significant barrier.

Employees' resistance to new technology isn't simply because they dislike change. In many cases, it's due to anxieties such as "Will my work be disrupted?" or "Will AI take my job?"

If the purpose of implementation and how employees' roles will change aren't adequately explained, and they are simply told, "Just try using it," employees may become passive and uncooperative.

Furthermore, if operational rules like "who, when, how, and under what rules AI will be used" aren't properly defined, employees won't feel secure using AI due to concerns about information leakage or misuse.

3. The Wall of Data and Security

AI learns using large amounts of data, so if the data is insufficient or of poor quality, it won't achieve the expected results. Moreover, AI use introduces new security issues and the risk of personal information leaks for companies.

Despite many companies feeling that AI security is a risk, the current situation is that regulations and systems for AI usage have not kept pace.

It's also common for decisions to be left to individual employees.

For example, there have been reported cases where confidential company information, such as source code, was input into generative AI, which then transmitted it to the AI vendor's server, leading to information leakage. Additionally, information generated by AI may contain errors or be biased, which can damage a company's reputation.

4. The Wall of Cost and Talent Development

AI implementation involves initial costs and ongoing operational expenses. In some cases, the expected benefits aren't realized, and the return on investment (ROI) doesn't justify the expenditure.

Furthermore, a shortage of personnel with specialized knowledge to effectively utilize AI is a significant deterrent to adoption.

Developing AI talent takes time and money, but without proper training systems, even well-intentioned AI tools may not be fully utilized and could become a waste.

Small and medium-sized enterprises (SMEs) often express concerns like, "We don't have AI specialists," or "We don't know what to use AI services for."

Practical Measures to Overcome the "Invisible Walls"

To overcome these barriers and successfully implement generative AI, meticulous planning and a "human-centered" approach are crucial.

1. Clarify Objectives and Start Small

Successful AI adoption begins by clearly defining the purpose of introducing AI.

Concrete measures:

• Quantify current challenges: For example, set specific goals (KGIs and KPIs) such as "reduce X hours of work per month" or "decrease customer inquiry response time by X%." This makes it easier to measure post-implementation effects and gain approval from upper management.
• Visualize operations and prioritize: Interview department heads about operational challenges and diagram workflows to understand the overall picture. Then, prioritize repetitive tasks where AI is expected to have a significant impact. Start with a small-scale proof-of-concept (PoC) to test it in practice. This reduces risks associated with implementation, allows you to confirm actual effects, and build success stories.

2. Consider Human-Centric Operations and Gradual Introduction

It's important to recognize that AI is merely a "tool" to assist people's work. Aim for an "optimal division of labor" where AI and people collaborate, leveraging their respective strengths.

Concrete measures:

• Provide thorough explanations and create forums for discussion: Clearly explain the purpose of AI adoption, its positive impacts, and how employees' roles will change in advance to alleviate anxieties on the ground. It's crucial to listen to employee feedback and involve them in the implementation process to ensure they accept it with understanding.
• Create clear operational rules and guidelines: Define basic rules such as "who, when, how, and for which tasks AI will be used," along with specific guidelines for handling company secrets and personal information. Be sure to establish mechanisms to prevent the use of incorrect information or copyright infringement. This will enable employees to use AI with confidence.
• Implement incrementally: Instead of automating all operations at once, a "lean approach" is recommended, starting with a portion of tasks or departments and gradually expanding the scope.

3. Promote Internal Education and "Prompt Engineering"

To maximize the results of AI implementation, it's essential for every employee to acquire the skills to use AI effectively.

Concrete measures:

• Conduct training to deepen AI knowledge: Provide phased training based on employees' understanding levels, covering fundamental AI concepts, what AI can and cannot do, practical use cases, and security knowledge.
• Educate on "Prompt Engineering": To get the best results from generative AI, the skill of providing appropriate instructions (prompts) is required. Incorporating prompt engineering training to cultivate the ability to give clear and specific instructions significantly enhances the quality and results of AI utilization. For example, JAL Card achieved significant results by thoroughly analyzing past inquiry data and establishing a system to continuously improve chatbot response accuracy.
• Create a knowledge-sharing mechanism: Utilize internal portals or wikis to share successful AI use cases and best practices, preventing knowledge from being concentrated among a few individuals.

4. Visualize Costs and Effects, and Continuously Improve

It's important to clarify the value generated by AI investment (ROI) and establish a cycle of continuous evaluation and improvement.

Concrete measures:

• Calculate ROI and uncover "hidden profits": ROI can be calculated as "(profit gained from AI implementation ÷ cost of AI implementation) × 100." Beyond reducing labor costs, quantify "hidden profits" such as increased sales, improved product quality leading to reduced waste, or enhanced customer satisfaction. For example, implementing a chatbot for customer support is estimated to save tens of millions of yen annually in personnel costs.
• Measure effects and adjust continuously: Regularly check ROI after implementation, retrain AI models, and make improvements based on user feedback. This allows for sustained improvement in AI accuracy and effectiveness.

The Future of Work Unlocked by Generative AI

Streamlining operations with generative AI isn't just about saving effort. It holds the potential to significantly transform the way we work.

By entrusting many simple and repetitive tasks to AI, we can focus more on activities that require critical thinking and creativity.

For instance, at a major financial institution, AI now handles about 80% of contract review tasks, allowing legal department staff to dedicate their time to more crucial duties like "risk prediction" and "exploring new business models."

In manufacturing, the implementation of an automated inspection system using image recognition AI enabled quality control personnel to develop a "quality prediction model," reducing defect rates by 50%.

AI technology is advancing daily. In the future, "collaborative AI models," where AI creates drafts that humans then refine, will become commonplace.

Furthermore, "self-evolving AI systems" that continuously learn from daily operational data, and "AI orchestration" that links multiple AI tools to automate entire work processes, are also expected to progress.

Conclusion: Organizations Growing with AI

Introducing generative AI means more than just adopting a new tool. It's a challenge that can significantly change a company's culture and how its employees work.

To succeed in this challenge, it's crucial to first clarify the purpose of using AI and have a perspective on measuring the effectiveness relative to the investment.

And above all, it's about building a collaborative relationship where people and AI leverage their respective strengths.

Instead of starting big, a good approach to minimize risk and maximize impact is to start small, try it out, and then gradually expand successful results.

Furthermore, providing robust training to enable employees to effectively use AI and establishing mechanisms to share acquired knowledge and success stories are essential for embedding AI within the company.

AI is a reliable partner that can free us from repetitive tasks and guide us toward more creative endeavors.

Instead of fearing this new era, let's embrace it with anticipation, growing with AI and co-creating new value that no one has seen before.

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