Read More《成功驾驭AI的领导者，都在修炼这5项能力》
Positive Comments: The Five Competencies Framework Provides Practical Guidelines for Leadership in the AI Era
In the current era when generative AI technology is rapidly penetrating the business field, enterprises’ investment in and expectations for AI are increasing day by day. However, the dilemma of “mismatch between technology investment and value output” has become more prominent. As mentioned in the news, although S&P 500 companies frequently mention AI, they have difficulty clearly elaborating on its actual benefits. Instead, the descriptions of risks are more specific. The core contradiction of this phenomenon lies in the fact that the advancement of technology itself cannot automatically be converted into commercial value, and the lag of organization and leadership has become the biggest bottleneck. The five – competencies framework of “crossing organizational boundaries, redesigning organizational structures, coordinating team collaboration, guiding and cultivating talents, and leading by example” proposed in this article precisely addresses this pain point and provides a systematic solution for leadership development in the AI era.
Firstly, the framework fills the theoretical gap between the implementation of AI technology and organizational change. Previous research on digital transformation has mostly focused on technological tools or process optimization, but has paid little attention to the role of “people” – especially how leaders can promote organizational adaptation to new technologies through capacity upgrading. Through a large number of cases (such as Microsoft’s Nadella inviting the CEO of the acquired company to participate in strategic meetings, SAP’s CFO redesigning core functions, and Walmart’s chief human resources officer personally using AI tools), this article disassembles the abstract “leadership” into specific and operational competencies. For example, “crossing organizational boundaries” requires leaders to actively build interpersonal networks across industries and fields, and accelerate the diffusion of AI awareness through the spread of tacit knowledge; “redesigning organizational structures” emphasizes the supporting changes in processes, incentives, and structures, rather than simply adding AI tools. This transformation from “concept” to “action” enables corporate managers to more clearly identify their own capacity shortcomings and make targeted improvements.
Secondly, the proposal of the framework responds to the core needs of organizational change in the AI era. As mentioned in the news, the real value of AI comes from “rethinking business processes, achieving hyper – personalization, and building new business models”, rather than simply cost reduction or labor substitution. This means that enterprises need to shift from being “efficiency – driven” to “innovation – driven”, and the role of leaders also needs to change from “process supervisors” to multiple identities such as “architects”, “coordinators”, and “coaches”. For example, Russell Reynolds Associates uses AI agents to perform simple tasks to increase the difficulty of employees’ tasks, and PepsiCo merges strategic and technical responsibilities to promote organizational restructuring. These cases all show that only when leaders actively take on the responsibility of “redesigning” can AI be upgraded from a “tool” to a “strategic lever”. This redefinition of the leadership role provides a key path for enterprises to break through dilemmas such as “failed AI pilots” and “employees’ fear of technology”.
Finally, the framework emphasizes the practical logic of “leading by example”, directly hitting the common problem of “disconnect between cognition and action” in the AI era. Research shows that although senior executives are very interested in AI, their actual usage rate is lower than their stated interest, resulting in the phenomenon of “promoting technology through slogans”. This article uses the case of Walmart’s Morris to illustrate that leaders’ personal use of AI tools (such as recruitment assistance and travel recommendations) can not only enhance their own understanding of technology, but also stimulate the team’s willingness to try through the “social identity” effect. This penetration logic from “individual to organization” is more in line with the diffusion law of AI technology than the traditional “top – down command” – as revealed by the theory of “Diffusion of Innovations”, the demonstration effect of credible peers is the key driving force for technology popularization.
Negative Comments: The Universality and Implementation Challenges of the Five Competencies Framework Need Further Verification
Although the five – competencies framework proposed in this article has significant practical value, there is still room for discussion regarding its universality across different industries and enterprise scales, as well as the complexity of its specific implementation.
Firstly, the framework does not fully explore its adaptability to industry differences and enterprise development stages. For example, there are significant differences between the technology industry and traditional manufacturing in terms of AI application scenarios (such as the former focusing on algorithm research and development and the latter on production process optimization) and organizational culture (such as technology companies being more tolerant of trial – and – error and traditional companies relying more on experience). The competencies required by leaders may vary. Most of the cases mentioned in the news are concentrated in fields such as technology (Microsoft), software (SAP), and retail (Walmart), lacking coverage of industries with strong regulatory requirements or heavy reliance on human resources, such as healthcare and education. For example, the application of AI in the healthcare industry needs to strictly follow compliance requirements, and leaders may need additional “risk management competencies”; the implementation of AI in the education industry depends more on the in – depth collaboration between teachers and technology, and leaders may need to strengthen their “cross – functional communication competencies”. If the framework is not refined according to industry characteristics, some enterprises may “copy it mechanically”, which may exacerbate organizational chaos.
Secondly, the implementation difficulties of “redesigning organizational structures” and “coordinating team collaboration” are underestimated. As mentioned in the news, the redesign of organizational structures needs to be accompanied by cultural change (such as the reform of quarterly business evaluations by a multinational food company). However, cultural change often takes several years and involves the redistribution of interests (such as the vested – interest groups in the old processes may resist the change). For example, the culture of “inspection and control” in traditional enterprises may be deeply rooted. If the transformation is only promoted through “reforming the meeting form”, it may be difficult to touch the core of the culture. In addition, “coordinating human – machine collaboration” requires leaders to balance algorithm input and human judgment. In high – risk decision – making (such as financial risk control and medical diagnosis), how to define the role of AI as a “contrarian”? If the doubts of AI lack transparency (such as the “hallucination” problem of large models), it may lead to a trust crisis within the team. Although the news mentions “creating psychological safety”, it does not provide a specific conflict – resolution mechanism (such as the decision – making process and responsibility attribution in case of disagreements), which may result in “collaboration in name only” in practice.
Finally, the evaluation criteria for the effectiveness of the competencies and the verification of long – term value are ambiguous. The article emphasizes that leaders need to promote AI application through “guiding and cultivating talents” and “leading by example”, but how to quantify the effectiveness of these competencies? For example, does the “guidance culture” improve employees’ efficiency in using AI? Does “leading by example” really accelerate the diffusion of technology? The news mentions that Microsoft’s sales department saved thousands of hours of customer interaction time through the guidance culture, but this result may be affected by multiple factors (such as changes in the market environment and other management measures) and is difficult to attribute solely to leadership competencies. In addition, AI technology is still evolving rapidly (such as the development of multimodal models and autonomous agents). Do leaders’ competencies need to be adjusted dynamically? For example, when AI starts to have the ability of “autonomous task decomposition”, leaders may need to strengthen their “goal alignment” competencies to ensure that AI’s autonomous actions are consistent with the enterprise’s strategy; when AI ethical issues become prominent, “compliance and risk management” competencies may need to be added.
Advice for Entrepreneurs: Use the Five Competencies as an Anchor and Dynamically Adjust Leadership Strategies
For entrepreneurs exploring AI applications, the five – competencies framework proposed in this article provides important action guidelines, but it needs to be flexibly implemented in combination with the actual situation of the enterprise. The following are specific suggestions:
Build a “cross – boundary” network to avoid limitations in technological cognition: Entrepreneurs need to actively break through industry and organizational boundaries. They can obtain non – repetitive information by participating in AI technology summits, joining cross – industry communities (such as forums on the integration of technology and traditional industries), and collaborating with AI startups or academic institutions. For example, entrepreneurs in the retail industry can establish connections with AI vision technology companies and consumer behavior research institutions to understand how “AI – driven precision marketing” can be combined with their own businesses. At the same time, they need to encourage the core team to participate in such interactions (such as organizing regular cross – departmental technology sharing meetings) to avoid the cognitive gap between “leaders understand AI, but the team does not”.
Take “redesign” as the core and be vigilant against the “technology superposition” trap: The value of AI lies not in replacing existing processes, but in reconstructing business logic. Entrepreneurs need to systematically evaluate which processes can be automated by AI (such as customer service and data sorting), which processes need to enhance human judgment (such as in – depth analysis of customer needs), and which processes must be led by humans (such as high – risk decision – making). For example, when an education technology company introduces an AI tutoring tool, it should not only replace the teacher’s “knowledge transmission”, but should use AI to analyze students’ learning data to help teachers focus on “personalized teaching”. At the same time, it needs to adjust the incentive mechanism synchronously (such as changing the teacher’s KPI from “number of teaching hours” to “student progress rate”) to avoid implementation resistance caused by “the process has changed, but the assessment remains the same”.
Design “human – machine collaboration” rules and establish a trust and responsibility mechanism: When promoting AI to participate in decision – making, entrepreneurs need to clarify the role of AI (such as “advisor”, “analyst”, “contrarian”) and its applicable scenarios. For example, when a fintech company uses AI for risk assessment, it can stipulate that the AI output serves as “basic analysis” for the team’s reference, but the final decision needs to be adjusted by humans based on business experience. At the same time, a “disagreement recording and review” mechanism should be established (such as when the team has objections to the AI’s suggestions, the reasons need to be recorded and analyzed regularly) to gradually optimize the efficiency of human – machine collaboration. In addition, training (such as the use of AI tools and the interpretation of algorithm logic) should be provided to improve the team’s understanding of AI and reduce “fear” or “blind reliance”.
Shift from “inspector” to “coach” and cultivate an AI – adaptive culture: Entrepreneurs need to reduce their reliance on “process inspection” and instead help the team master new skills in the AI era through “guidance”. For example, they can regularly organize “AI application workshops”, encourage employees to share cases (including failure experiences) of using AI to solve business problems, and give rewards. Provide compound – skill training in “AI + business” for core members (such as data analysts learning large – model fine – tuning and product managers learning prompt engineering). At the same time, leaders need to lead by example in using AI tools (such as using AI to assist in meeting minutes and market analysis) and publicly discuss their usage experiences within the team (such as “this AI – generated plan has a clear logic, but lacks local market insights and needs to be supplemented”) to convey the concept that “AI is an assistant, not a replacement”.
Dynamically evaluate the effectiveness of competencies and adapt to technological evolution: The rapid iteration of AI technology requires leaders’ competencies to be upgraded synchronously. Entrepreneurs need to establish a “competency assessment – adjustment” mechanism. For example, collect team feedback quarterly (such as “whether the cross – boundary network has brought new opportunities” and “whether the organizational structure hinders AI application”) and adjust the focus of competencies annually in combination with technological trends (such as the development of multimodal models and AI agents). For example, when AI starts to have the ability of “autonomous task decomposition”, leaders may need to strengthen their “goal alignment” competencies to ensure that AI’s autonomous actions are consistent with the enterprise’s strategy; when AI ethical issues become prominent, “compliance and risk management” competencies may need to be added.
In short, leadership in the AI era is not about “mastering technology”, but about “managing the synergy between technology and people”. Entrepreneurs need to base themselves on the five competencies and dynamically adjust their strategies in practice to ultimately achieve a dual improvement in “technological value” and “organizational value”.
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