Publishing the Guide to 'Unlearning and Relearning' Necessary in the AI Era (Organizational Behavior Science®)
Request Co., Ltd. published a report detailing the necessity of 'Unlearning and Relearning' to develop human judgment in the AI era, based on analysis of 338,000 individuals.
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- 📰 Published: March 31, 2026 at 21:00
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- 🤖 AI Analyzed: April 22, 2026 at 02:36 (492h 57m after Collected)
Request Co., Ltd. (Headquarters: Shinjuku-ku, Tokyo; CEO: Tomoyasu Kohata), which provides Organizational Behavior Science®, has published a report titled "How to Proceed with 'Unlearning and Relearning' Necessary in the AI Era."
Based on the premise that what will become relatively more important for companies in the AI era is not the sheer amount of knowledge itself but "judgment," this report outlines the structural changes making it harder to develop that judgment. It also details specific implementation steps for transitioning from work that relies on precedents to work that requires making judgments by observing differences.
Furthermore, it concretizes the process in a practical manner, detailing which tasks to tackle first, what to visualize initially, how to segment and design them, how managers should change their involvement, and how to alter reflection processes so that criteria for judgment remain within the organization.
Report Download
With the spread of generative AI, tasks within corporate work such as looking up knowledge, organizing information, referencing existing cases, and processing via predefined procedures will increasingly be handled by AI.
On the other hand, what remains on the front lines of companies is the work of deciding what to confirm, what to prioritize, how much to rely on precedents, and where to change the approach, all while taking into account differences between customers, constraints of each project, varying conditions on-site, and differing priorities among stakeholders.
This report clarifies that in the AI era, what grows relatively more important in companies is "judgment," rather than just the volume of knowledge.
While judgment becomes crucial, experiences that foster it are decreasing in corporate work.
Based on an analysis of 338,000 people and 980 companies, the report shows that judgment experiences decreased in 82% of companies, the frequency of supervisor confirmation increased in 58%, and reliance on precedents rose in 64%.
What is happening here is not simply a lack of ability. Due to task standardization, manualization, IT implementation, and work style reforms, there is a demand to proceed according to precedents quickly and accurately. This is a structural change where experiences such as hesitating, comparing, thinking about reasons, and reflecting to update judgment criteria are becoming less likely to remain in daily work.
What this report addresses is the Unlearning and Relearning required in these circumstances.
However, the unlearning and relearning referred to here does not merely mean discarding old methods to relearn new knowledge.
Unlearning means recognizing that the judgment patterns acquired in situations where following precedents was rational cannot be directly applied to tasks with significant conditional differences.
Relearning means reconstructing within actual work the process of observing differences, confirming facts, considering the reasons for discrepancies, comparing multiple options, deciding what to prioritize, verbalizing the reasons for judgments, and updating the next criteria based on the results.
Based on the premise that what will become relatively more important for companies in the AI era is not the sheer amount of knowledge itself but "judgment," this report outlines the structural changes making it harder to develop that judgment. It also details specific implementation steps for transitioning from work that relies on precedents to work that requires making judgments by observing differences.
Furthermore, it concretizes the process in a practical manner, detailing which tasks to tackle first, what to visualize initially, how to segment and design them, how managers should change their involvement, and how to alter reflection processes so that criteria for judgment remain within the organization.
Report Download
With the spread of generative AI, tasks within corporate work such as looking up knowledge, organizing information, referencing existing cases, and processing via predefined procedures will increasingly be handled by AI.
On the other hand, what remains on the front lines of companies is the work of deciding what to confirm, what to prioritize, how much to rely on precedents, and where to change the approach, all while taking into account differences between customers, constraints of each project, varying conditions on-site, and differing priorities among stakeholders.
This report clarifies that in the AI era, what grows relatively more important in companies is "judgment," rather than just the volume of knowledge.
While judgment becomes crucial, experiences that foster it are decreasing in corporate work.
Based on an analysis of 338,000 people and 980 companies, the report shows that judgment experiences decreased in 82% of companies, the frequency of supervisor confirmation increased in 58%, and reliance on precedents rose in 64%.
What is happening here is not simply a lack of ability. Due to task standardization, manualization, IT implementation, and work style reforms, there is a demand to proceed according to precedents quickly and accurately. This is a structural change where experiences such as hesitating, comparing, thinking about reasons, and reflecting to update judgment criteria are becoming less likely to remain in daily work.
What this report addresses is the Unlearning and Relearning required in these circumstances.
However, the unlearning and relearning referred to here does not merely mean discarding old methods to relearn new knowledge.
Unlearning means recognizing that the judgment patterns acquired in situations where following precedents was rational cannot be directly applied to tasks with significant conditional differences.
Relearning means reconstructing within actual work the process of observing differences, confirming facts, considering the reasons for discrepancies, comparing multiple options, deciding what to prioritize, verbalizing the reasons for judgments, and updating the next criteria based on the results.