At the end of 2022, the emergence of ChatGPT brought generative AI into the center of public attention. This technology began to draw interest as a tool capable of independently creating various types of content—such as images, sentences, code, and music—either complementing or replacing human creativity and imagination.

Companies responded quickly to meet these expectations. They introduced generative AI across various areas of their organizations: automatically generating marketing content, handling customer service through AI, and summarizing meeting notes in real time. The fantasy that “everyone would have their own assistant, planner, or even designer” began to feel like reality.

However, this so-called “generative AI fantasy” has encountered many limitations when faced with real-world work environments. While the technology is ready, people and organizations are not. Organizational cultures are still slow to adapt to change, and day-to-day work habits involving new tools don’t shift easily. There is a clear gap between expectation and implementation, and attempts to bridge this gap—along with the resulting trial and error—are being repeated in many places.

In 2023, Microsoft fully integrated a generative AI-based tool called Copilot into its Office suite. Users could generate draft documents simply by giving commands such as “Summarize the key points of this meeting” or “Write a report based on last quarter’s sales data.”

Initial user responses were positive. Productivity improvements were especially noticeable in repetitive and formal tasks like drafting reports, and tasks such as summarizing meeting notes or writing emails also became faster.

However, as time went on, user experiences became increasingly polarized. Some employees were satisfied with the speed and efficiency of AI, but many began to question the *quality* of the results. Often, the documents failed to reflect the organization’s context or came across as awkward in tone.

One user said, “I ended up spending more time revising the report that Copilot made.” Generative AI was good at providing formal sentences, but it struggled to convey key messages or reflect the organization’s unique communication style.

A major insurance company in the United States piloted Copilot for report-writing tasks, but after three months, the program was put on hold. Many employees reported that they spent more time editing the drafts produced by Copilot than they would have spent writing the reports themselves from scratch. The company realized that without proper training and clear usage guidelines, the tool didn’t deliver the expected benefits.

Another major issue was trust. Some employees doubted whether the AI was using the correct or most relevant information. Others felt uncomfortable relying on a tool they didn’t fully understand. Even though the AI could quickly generate drafts, employees still had to fact-check the content and rewrite many parts, which reduced the overall efficiency.

Moreover, many organizations lacked clear internal policies or rules regarding the use of generative AI. Employees were unsure when it was appropriate to use such tools or how much they could rely on them. This lack of guidance led to hesitation and slowed down adoption across teams.

These experiences reveal an important truth: while the technology itself has plenty of potential, it cannot seamlessly integrate into the way we work unless there is a simultaneous shift in people’s mindset, in the way decisions are made within organizations, and in the trust systems that shape how work is done.

Generative AI theoretically holds the potential to "automate everything," but in practice, its implementation and impact vary by industry and job function.

In the design and marketing industries, the use of generative AI has been adopted relatively quickly. Since 2023, global advertising agency WPP has introduced generative AI into certain stages of campaign planning, using it to produce draft slogans and banner designs aligned with brand images. In this process, human designers shifted their roles to reviewing ideas and refining the details. As a result, working time was reduced by about 30%, and the fatigue caused by repetitive tasks significantly decreased.

In addition, Uniqlo improved its content production process by having generative AI create drafts of product descriptions, which marketers then polished. Since large-scale online shopping platforms constantly require diverse content tailored to different seasons and age groups, AI effectively replaced these repetitive language production tasks. This allowed marketers to focus more on creative planning and significantly accelerated the overall campaign timeline.

On the other hand, in high-trust industries such as healthcare and law, the adoption of generative AI is much more cautious. For example, a global law firm introduced a system where AI drafts contracts and lawyers review them. However, because important legal terms or case references could be missed, some voiced concerns about fully trusting AI’s automation capabilities. In such cases, AI serves as an assistant, while final review and decisions still rely on human experts.

As such, the application of generative AI in the field varies depending on the characteristics of the industry and the nature of the work. In fields where creative tasks are common, AI can serve as a useful support tool, but in areas where trust and accuracy are critical, the human role remains essential.

While the adoption of new technologies is progressing rapidly, the pace of organizational cultural change is much slower. Although AI is designed as a tool to amplify creativity, many people perceive it as a means of surveillance or replacement. This difference in perception becomes a significant barrier to the adoption and utilization of AI. In particular, organizations with strong cultural resistance to new technologies often experience delays in AI implementation.

For example, many small and medium-sized enterprises are reluctant to adopt AI. Employees question AI’s efficiency or express growing concerns that their jobs may be at risk. Such anxiety is especially evident in work environments where autonomy is highly valued. Some employees fear not only that AI may replace their jobs, but also that they may lose control over their own tasks.

Furthermore, an organizational culture that does not tolerate failure also obstructs the effective use of AI. AI is not perfect. It can make errors in unpredictable situations. However, in cultures where failure is unacceptable, it becomes difficult to embrace the potential mistakes that may occur after AI is introduced. This eventually leads to hesitation in adopting AI and discourages experimental approaches within the organization.

No matter how capable AI becomes, if people do not accept it, the technology will go nowhere. While technology is advancing quickly, for it to be effectively utilized in organizations, people’s attitudes must change first.

In conclusion, there is no doubt that generative AI opens up new possibilities in the workplace. However, it is by no means easy for technological advancements to bring about changes in people’s work habits and organizational culture. Resistance to change and issues of trust remain major obstacles to technology adoption. To overcome these challenges, education during the implementation process and cultural change within the organization must occur together.

A fast pace of technological adoption does not automatically translate into meaningful change. Moving the hearts of the people who use the technology, and fostering a culture that is not afraid of failure, will be the key to effectively leveraging generative AI.