AI時代に成果を出す人は、「成功事例」より「失敗事例」を分析する

11/8/2026

YouTube動画『【トップ5％社員の習慣】AI時代に成果を出す人は「成功」より何を分析するのか？』では、クロスリバー代表の越川慎司氏へのインタビューで、815社・17万人の分析をもとに、AI時代に会社から期待される人の行動習慣が紹介されています。
興味深いのは、AIを使っているから成果が上がるという単純な因果関係は確認されなかったという点です。AIはすでに電気や水道のような仕事のインフラになりつつあり、重要なのは、AIを使うこと自体ではなく、どのように成果につなげるかです。成果を出す人は、成功事例をそのまままねるのではなく、うまくいかなかった理由を分析して「失敗確率」を下げています。生成AIにも成功例だけでなく失敗例を学ばせ、複数のAIを使って反証や確認を行っていると分析しています。
また、AIが情報収集、整理、資料作成を担うようになるほど、人間には、現場で得た経験、独自に蓄積した失敗データ、仕事を成功させたいという欲求、そして周囲を巻き込む力が求められます。特に重要なのは、単なる「情報共有」ではなく、相手の考えや感情を受け止める「感情共有」です。反対意見にもまずうなずき、意見を出すことと最終的な判断を分ける姿勢が、協力を引き出し、大きな成果につながると説明されています。
AI時代に価値を持つのは、AIより速く作業する人ではなく、AIを活用しながら失敗を減らし、人との信頼関係を築き、成果が生まれやすい環境をつくれる人なのかもしれません。
AI時代に成果を出す人について、生成AIに徹底的に比較させましたので、ご参照ください。なお、生成AIによる調査・分析結果は、公開された情報だけに基づくものであり、必ずしも実情を示したものではなく、誤った情報を含む可能性があることにご留意のうえ、ご参照ください。

【トップ5％社員の習慣】AI時代に成果を出す人は「成功」より何を分析するのか？
https://www.youtube.com/watch?v=sQcApYVl0IU

In the AI Era, High Performers Analyze “Failures” Rather Than “Success Stories”
The YouTube video “Habits of the Top 5% of Employees: What Do High Performers Analyze Instead of ‘Success’ in the AI Era?” features an interview with Shinji Koshikawa, CEO of Cross River, and introduces the behavioral habits of employees who are highly valued by their companies in the AI era, based on an analysis of 170,000 employees across 815 companies.
One particularly interesting finding is that no simple causal relationship was identified between using AI and achieving better results. AI is already becoming part of the basic infrastructure of work, much like electricity and water. What matters is not simply whether people use AI, but how they translate its use into tangible results. High performers do not merely imitate successful cases. Instead, they analyze why things did not work and seek to reduce the “probability of failure.” The analysis also suggests that they have generative AI learn not only from successful cases but also from failures, while using multiple AI tools to challenge, cross-check, and verify their conclusions.
Furthermore, as AI increasingly takes over information gathering, organization, and document preparation, people are expected to bring qualities that AI cannot easily replace: hands-on experience gained in the field, proprietary knowledge accumulated from past failures, a strong desire to make their work succeed, and the ability to engage and mobilize others. Particularly important is not merely “sharing information,” but “sharing emotions”—acknowledging and understanding the thoughts and feelings of others. The video explains that first acknowledging opposing views, and clearly separating the process of expressing opinions from the process of making final decisions, can encourage cooperation and ultimately lead to greater results.
In the AI era, perhaps the people who will create the most value are not those who can work faster than AI, but those who can leverage AI to reduce failures, build relationships of trust with others, and create an environment in which successful outcomes are more likely to emerge.
I asked generative AI to conduct an in-depth comparative analysis of the characteristics and behaviors of people who achieve strong results in the AI era. Please refer to the findings for your reference.
Please note, however, that the research and analysis conducted by generative AI are based solely on publicly available information and may not necessarily reflect actual circumstances. The results may also contain inaccuracies or errors, so please keep these limitations in mind when reviewing the findings.

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