Abstract
This article explores the evolving role of Artificial Intelligence (AI) in leadership and its impact on corporate management and decision-making. While AI offers immense potential in data analysis, prediction, and process optimization, the human elements of leadership—such as empathy, intuition, and creativity—remain irreplaceable. By drawing on established frameworks like Porter’s value chain and Nash Equilibrium, the article emphasizes the need for a balanced approach, where AI complements human cognition rather than replaces it. The discussion extends into the complexities of human consciousness, suggesting that while AI can aid in decision-making, leadership must remain centered on human traits to sustain competitive advantage. Practical examples from companies like IBM and Unilever illustrate how AI can streamline processes, yet the strategic direction must ultimately come from human leaders. This article advocates for a thoughtful integration of AI in leadership, ensuring the preservation of human insight in navigating the rapidly evolving digital landscape.
Keywords
Artificial Intelligence, leadership, management, human cognition, value chain, Nash Equilibrium, corporate brand equity, decision-making, AI integration, empathy, intuition, AI in business
Niels and Fabiola published a thought-provoking article titled “The Now, New, and Next of Digital Leadership: How Artificial Intelligence Will Take Over and Change Leadership as We Know It.” The three dimensions of time were linked to leadership, as depicted in the figure below.
This very contentious discussion is making present-day CXOs either too dependent on Artificial Intelligence (AI) or trying to be protective by totally ignoring AI as machines and more likely as a threat. More importantly, AI is becoming the key to building mindsets of the global networked population, particularly, managers. Quite clearly, the AI divide, like the digital divide, is a serious agenda and must not be ignored. Thus, it is extremely important for all to assess the role of AI in the overall value chain of management hierarchy and within the management function.
The ultimate purpose of a value chain has always been to manage the relationship between work undertaken by the various people within the organisation and the value which the work creates for building the corporate brand through the ultimate satisfaction of the consumer. Thus, the value chain outcome should dictate the function across the organisation structure, never ever the other way around. However, it is also crucial to recognize that factors such as the founder’s vision, unique organizational core values, and historical practices may also shape the organization structure and influence how the value chain is managed. The value chain of a corporate brand comprises shareholders, people within the organisation, external stakeholders, partners, and the ultimate consumers. This network is overwhelmed by the five senses (feel, taste, see, hear and smell); the meaningful fulfilment of all these senses amongst the value chain partners ultimately measures the equity value of the corporation. The better this network of senses of the value chain partners is managed, the easier and more impactful it is for optimising the outcome. Most importantly, the combination of the five senses moulds the distinct behaviour of each of the individuals in the value chain, which changes based on the everchanging input as an impulse within the distinct neural network. The reference to the five senses is metaphorical, representing the different ways value chain partners perceive and engage with the organization. For instance, the sensory experience symbolizes how different stakeholders—through their cognitive tasks and emotional interactions—perceive the brand’s offerings. While other aspects such as cognitive tasks and emotional well-being are equally significant, the five senses are used here as a symbol of the diverse experiential inputs from the network.
This is where the relevance of AI is considered most important: to collect, cohere, and illustrate the outcome of all the impulses impacting the five senses across the value chain partners to ensure greater equity of the corporate brand through real-time reaction and thus appropriate prediction. This is an extremely difficult but very important task and may be considered as the foundation for efficiency across the value chain. One of the most intricate and recent models on AI integration with Porter’s value chain model has been synthesised by Ismar Huskic (Figure below).
The figure clearly outlines the intervention opportunities using AI to strengthen and forecast various business functions and thus ensure reduced risks. Obviously, companies using AI will be in a better position compared to those who are below the AI divide. However, in a competitive environment where the competition is likely to use AI technology, the technology may ultimately transform into a standardized generic solution. Thus, the basic question will be, will it result in the age-old theory of “Nash Equilibrium ” where no one can gain by changing their own strategy since they are monitored in real-time? Nash Equilibrium, a concept from game theory, describes a situation where competing entities cannot gain any advantage by changing their strategy because their actions are being monitored in real-time, which is similar to how AI constantly tracks and analyzes actions in competitive environments.
Therefore, the strategic question should not be about how AI will take over the leadership but how the leadership can take advantage of AI to sustain strategic advantage. Differentiation strategy is used in business for survival through creating exclusive positioning strategies that are likely to enhance brand equity. This is true for products, the corporate, and even a nation. It is, therefore, evident that leadership must ensure that this edge, through differentiation, is maintained, assuming that all are equipped with AI. While the current integration of AI with Porter’s value chain model provides a framework for operational efficiencies, there is room to develop this further by incorporating more specific AI tools and strategies that can predict, analyze, and improve various functions such as supply chain management, customer service, and even leadership behaviors. A deeper understanding of how AI can create strategic advantages in these areas will ensure organizations remain competitive
The argument that AI will ultimately take over the leadership helm is perhaps too optimistic and unrealistic. If AI prediction becomes the ‘Holy Grail,’ I am sure this will be the next analytics to predict dark energy, which corresponds to almost 70% of the environmental impact of the universe or determining the journey of human consciousness. Unfortunately, AI is still primitive compared to human cognition and predicts the real-time evolving recipe, influenced by the mix of emotions, as it is cooked within individual minds. While AI holds great potential in analyzing data and making predictions, it is crucial to recognize that AI is a tool to complement, not replace, human cognition in leadership. Leaders must leverage AI for operational insights but rely on human intuition, empathy, and creativity to navigate complex and evolving environments. On top of that, as the individual moves along the life cycle within a society, the number of environmental influencers can be overwhelming, and the probable outcomes may result in billions of combinations. Thus, measurement of outcome is unlikely to provide worthwhile direction. It is extremely important to understand that one needs to focus on the journey rather than the outcome. The reality of information may far exceed the ability of AI, even in the era of machine learning. The space within the human mind could be the same, but the expansion of consciousness with the input of knowledge expands even when someone tries to bind the mind with chains. It is interesting to note that the brain’s default mode (when doing nothing or in a ‘super normal state’) is 20 times more active compared to when working under cognizant mode and most importantly, thoughts and beliefs are created during default mode, which are almost impossible to measure . Although current AI technology is not designed to understand dark energy or the entirety of human evolution, the ongoing advancements in generative AI hold the potential for future breakthroughs. The reference to dark energy serves as a metaphor for the unexplored potential and complexity that AI might one day help humans comprehend, much like how AI’s evolving capabilities can revolutionize corporate decision-making.
The role of the leadership, therefore, centres around understanding the conscious behavior of humankind through AI data analytics. This task should be coordinated by the mid-managers at the various functional levels. It is important to ensure that the information collection properly registers the environment and the circumstances to ensure least deviation from reality. One must appreciate that even though AI may be able to provide alternatives, it will be difficult to provide directions to influence the customers as it shifts in real-time due to the influence of dark energy. Thus, the task of the senior leadership will not be to analyze but rather how to differentiate through cognitive learning of the human mind.
It is important to understand that we are focusing on personalized marketing in the age of AI and machine learning since it is easier to predict individuals when at conscious state. However, things get difficult when dark energy takes over and even more difficult when individual beliefs collide with other beliefs and form reality at an ethereal speed. In addition, the market, as described by the collection of space within the human mind, comprises both the default and cognizant mode and, thus, requires direct intervention by the human mind to predict. Otherwise, in a competitive environment with millions of influencers, catering to individual minds as the ultimate package will be too expensive since every marketer will have to promise greater number of utilities for the enhancement of the customer value package. The intervention of the human to human thus remains the key to both understanding and satisfying customer value. This will take place at all levels of management, from the front-line salesforce to the top management. The strategic monitoring and direction will remain the task of the senior management not C’AI’O. In practice, AI has been successfully applied by companies such as IBM, where Watson helps leaders make data-driven decisions by processing large amounts of data. Similarly, Unilever’s AI-powered recruitment system has demonstrated how AI can streamline processes, reduce bias, and enhance the overall decision-making framework.
In conclusion, let us remind ourselves of the classic Kurt Lewin’s behaviour equation “B = f (P, E)” . Put simply, an individual’s behavior is a function of that individual and the environment. The equation is far more complicated. “P” corresponds to the entirety of the person, including their past, their present, their expectations of the future, their personality, their capabilities, their motivations, their desires, and so on. “E” includes all aspects of the individual’s environment at the time of measurement of any behaviour, including the physical, social, technological, political, and economic environment and other such contexts. Based on our discussion, the model takes a more complicated structure when adding two more dimensions to it: first, the paranormal individual dimension, which is the brain’s default mode (S), along with the environmental factor, the Dark Energy (Λ). The equation thus will take the following structure: B = f [(P, E)*(S, Λ)] . It is indeed difficult to measure the relationship between S and Λ. However, it is accepted that S is 20 times stronger than the combined impact of P and E at an individual level. More importantly, the physical environmental dimension E, as outlined in the equation, is only equal to 5% of the total impact humankind influences, while Λ corresponds to 68-70%. Clearly, the task of humankind is far from over. The task is more than what the eye can see. The opportunities, therefore, are also beyond the bounds and AI and scratch only the surface. The rest must be the task of the leadership.
We can infer from the above that managers’ ignorance of AI may result in a serious loss of competitive advantage for the corporate brand. At the same time, blindfolding the human mind with the arguments that AI will ensure agnostic, productive, approachable, stress-free, real-time leadership may result in the mental death of the manager and the ultimate death of the corporate brand. Leaders should focus on using AI to assist with decision-making by analyzing data patterns, predicting outcomes, and streamlining processes. However, they must retain the human aspects of leadership—empathy, creativity, and vision—to guide AI’s application in a meaningful way. The balance between AI and human cognition will be the key to unlocking AI’s full potential in leadership roles. In summary, while AI presents significant opportunities to enhance leadership and management functions, it is crucial to focus on the ways AI can be leveraged to complement human cognition, rather than replace it. Leaders must strike a balance, using AI to inform decisions while maintaining the human elements of empathy, intuition, and creativity in leadership.
REFERENCES
- Niels Van Quaquebeke and Fabiola H Gerpott. “The now, new, and next of digital leadership: How artificial intelligence (AI) will take over and change leadership as we know it”. Journal of Leadership and Organizational Studies. Volume 30, Issue 3. June 2023.
- Ismar Huskic. Porter’e value chain model with AI integration”. CEO Insights: Research and Impact. Edinburgh Business School, Heriot-Watt University. January 2024.
- John Forbes Nash, Jr. “Nash equilibrium”. Proceedings of the National Academy of Science. Princeton University. 1950.
- Chelsea Gohd. “What is dark energy? Inside our accelerating, expanding universe”. NASA, 2024.
- Tom Cahalan. “Dark energy and human consciousness: humanity’s part to freedom”. Balboa Press. 2014.
- Kurt Lewin. “Principles of Topological Psychology”. New York, McGraw-Hill. 1936.
- Syed Ferhat Anwar, proposed formulation for this this article.
Author:
Professor Syed Ferhat Anwar
President, Asia Marketing Federation
Vice-Chancellor of BRAC University, Bangladesh
