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Reconstructing Professional Identity in the AI Wave

Feb 25-2026   



Reconstructing Professional Identity in the AI Wave: How Should Individuals Position Themselves as AI Restructures Organizations?

In this era of AI sweeping across industries, no one can remain unaffected in their career evolution. How do we redefine our position in organizations in the age of AI? And how should we make more proactive choices for the future?

Understanding AI in the Long River of Technological History

Dr. Ling Zong, a former senior engineer and scientist at IBM, takes us on a journey back through the century-long history of technology: When computers first entered the human world, debates mirrored today's discussions about AI—Is it merely a tool, or an entirely new science? Subsequently, computer science entered higher education, enterprises reorganized their processes around digital systems, and the underlying logic of social operations was quietly rewritten.

Unlike artificial intelligence, which relies on repetitive training, human intelligence emerges continuously within long-term social and cultural environments. It is not a piece of replicable code, but a complex product of continuous interaction and coupling with family, culture, and the zeitgeist since the beginning of life. This generative process remains implicit and historically contingent, defying complete deconstruction to this day. By contrast, contemporary AI lacks "intergenerational transmission." Each AI upgrade relies on re-feeding data and remodeling. Consequently, while AI can achieve rapid progress in specific tasks, it struggles to continuously generate novel intelligence through the flow of real-life experiences as humans do.

When AI Enters the Real World: Value Boundaries, Trust, and Social Narratives

Currently, many professionals use AI for tasks such as writing repetitive code, creating PowerPoint presentations, retrieving information, or generating inspiration for non-critical matters. These tasks share common characteristics: high fault tolerance, results that can be iteratively revised, and low accountability requirements. In these scenarios, AI can significantly free up human resources while delivering tangible and sustainable efficiency gains for organizations.

At present, most enterprises are in a transitional phase, moving from tool adoption to process integration. Employees extensively utilize AI in their daily work, while organizations have begun exploring more systematic approaches to human-machine collaboration. However, as AI transitions from an "auxiliary tool" to an "agentive tool," boundary issues emerge. AI fosters an intuitive trust that it can think, plan, and make decisions. Yet this trust often proves unreliable in critical scenarios where consequences must be borne.

If enterprises face the challenge of "how to leverage AI effectively," society at large grapples with "how to understand AI." Mei Liang, an associate researcher at the National School of Development at Peking University, notes that societal narratives about AI profoundly shape individuals' cognitive frameworks for adapting to technological change. Psychological research indicates that when narratives consistently emphasize "replacement," "crisis," and "obsolescence," individuals tend to develop cognitive patterns such as perceiving goals as unattainable and tasks as unworthy of investment, thereby weakening self-regulatory efforts. When AI is framed as a tool for augmenting human capabilities, and when narratives integrate core identities—composed of essential meanings—with the boundary redefinition brought by AI, individuals can maintain self-consistency and coherence. This proves more conducive to establishing stable self-perception and behavioral motivation.

Reconstruction of Professional Identity in the AI Era

As AI evolves from a tool into a deeply embedded organizational element, the challenges professionals face will shift from "improving efficiency" to deeper identity reconstruction. Moving forward, whether in entrepreneurship or in assuming new roles within companies, what remains truly stable is no longer the position itself, but the combination of individual capabilities and the capacity to collaborate with AI to create value across diverse contexts. Workplace demands for accountability, decision-making competence, and leadership have not diminished; rather, they have grown more crucial amid high uncertainty. Abilities rooted in contextual understanding, value judgment, and complex decision-making are becoming both the most difficult to automate and the most valuable in the long term.

Throughout this process, we must remain vigilant against a hidden risk: metacognitive complacency. When AI can rapidly generate answers, solutions, and analyses, will humans still engage in critical self-reflection? Can they still identify underlying assumptions and biases? Do they retain the capacity to make independent decisions and bear consequences amid information asymmetry? If humans gradually neglect the discipline of "how to think," even mastering more tools may not prevent them from losing direction at critical junctures.

The survival and development of professionals require individuals to reject rigid self-definitions as mere "executors" or "job titles," instead actively participating in this identity reconstruction centered on "collaboration." They should embrace AI as an opportunity to expand self-worth and, through continuous learning, judgment, and responsibility, strive to become self-actualizing professionals—driven by core competencies and values.


Reconstructing Professional Identity in the AI Wave

Feb 25-2026   



Reconstructing Professional Identity in the AI Wave: How Should Individuals Position Themselves as AI Restructures Organizations?

In this era of AI sweeping across industries, no one can remain unaffected in their career evolution. How do we redefine our position in organizations in the age of AI? And how should we make more proactive choices for the future?

Understanding AI in the Long River of Technological History

Dr. Ling Zong, a former senior engineer and scientist at IBM, takes us on a journey back through the century-long history of technology: When computers first entered the human world, debates mirrored today's discussions about AI—Is it merely a tool, or an entirely new science? Subsequently, computer science entered higher education, enterprises reorganized their processes around digital systems, and the underlying logic of social operations was quietly rewritten.

Unlike artificial intelligence, which relies on repetitive training, human intelligence emerges continuously within long-term social and cultural environments. It is not a piece of replicable code, but a complex product of continuous interaction and coupling with family, culture, and the zeitgeist since the beginning of life. This generative process remains implicit and historically contingent, defying complete deconstruction to this day. By contrast, contemporary AI lacks "intergenerational transmission." Each AI upgrade relies on re-feeding data and remodeling. Consequently, while AI can achieve rapid progress in specific tasks, it struggles to continuously generate novel intelligence through the flow of real-life experiences as humans do.

When AI Enters the Real World: Value Boundaries, Trust, and Social Narratives

Currently, many professionals use AI for tasks such as writing repetitive code, creating PowerPoint presentations, retrieving information, or generating inspiration for non-critical matters. These tasks share common characteristics: high fault tolerance, results that can be iteratively revised, and low accountability requirements. In these scenarios, AI can significantly free up human resources while delivering tangible and sustainable efficiency gains for organizations.

At present, most enterprises are in a transitional phase, moving from tool adoption to process integration. Employees extensively utilize AI in their daily work, while organizations have begun exploring more systematic approaches to human-machine collaboration. However, as AI transitions from an "auxiliary tool" to an "agentive tool," boundary issues emerge. AI fosters an intuitive trust that it can think, plan, and make decisions. Yet this trust often proves unreliable in critical scenarios where consequences must be borne.

If enterprises face the challenge of "how to leverage AI effectively," society at large grapples with "how to understand AI." Mei Liang, an associate researcher at the National School of Development at Peking University, notes that societal narratives about AI profoundly shape individuals' cognitive frameworks for adapting to technological change. Psychological research indicates that when narratives consistently emphasize "replacement," "crisis," and "obsolescence," individuals tend to develop cognitive patterns such as perceiving goals as unattainable and tasks as unworthy of investment, thereby weakening self-regulatory efforts. When AI is framed as a tool for augmenting human capabilities, and when narratives integrate core identities—composed of essential meanings—with the boundary redefinition brought by AI, individuals can maintain self-consistency and coherence. This proves more conducive to establishing stable self-perception and behavioral motivation.

Reconstruction of Professional Identity in the AI Era

As AI evolves from a tool into a deeply embedded organizational element, the challenges professionals face will shift from "improving efficiency" to deeper identity reconstruction. Moving forward, whether in entrepreneurship or in assuming new roles within companies, what remains truly stable is no longer the position itself, but the combination of individual capabilities and the capacity to collaborate with AI to create value across diverse contexts. Workplace demands for accountability, decision-making competence, and leadership have not diminished; rather, they have grown more crucial amid high uncertainty. Abilities rooted in contextual understanding, value judgment, and complex decision-making are becoming both the most difficult to automate and the most valuable in the long term.

Throughout this process, we must remain vigilant against a hidden risk: metacognitive complacency. When AI can rapidly generate answers, solutions, and analyses, will humans still engage in critical self-reflection? Can they still identify underlying assumptions and biases? Do they retain the capacity to make independent decisions and bear consequences amid information asymmetry? If humans gradually neglect the discipline of "how to think," even mastering more tools may not prevent them from losing direction at critical junctures.

The survival and development of professionals require individuals to reject rigid self-definitions as mere "executors" or "job titles," instead actively participating in this identity reconstruction centered on "collaboration." They should embrace AI as an opportunity to expand self-worth and, through continuous learning, judgment, and responsibility, strive to become self-actualizing professionals—driven by core competencies and values.