Artificial intelligence is profoundly transforming the world of work and the organisational models adopted by businesses. For decades, management has been built around planning, coordination and control. Today, however, the automation of processes and the development of intelligent systems are redefining managerial responsibilities and the skills required to lead an organisation.
The challenge is no longer simply to introduce new technologies to improve performance, but to rethink how businesses operate, make decisions and develop their people. In this context, the role of managers must evolve: less focused on overseeing day-to-day activities and increasingly centred on strategic vision, change management and the ability to integrate human and artificial intelligence.
From Efficiency to Organisational Transformation
For more than a century, the traditional business model has assigned managers the responsibility of planning, organising, coordinating and controlling people’s work. Artificial intelligence is now challenging this paradigm, making it possible to automate numerous operational activities and, increasingly, to support complex decision-making processes.
AI agents can already contribute to the management of sophisticated workflows, while advances in robotics are opening up new possibilities for integrating digital systems, machines and people. As a result, businesses may progressively adopt leaner organisational structures, characterised by fewer hierarchical layers and greater decentralisation of decision-making.
This change does not necessarily diminish the importance of management; rather, it redefines its functions. A manager’s value will depend less on the ability to supervise every individual task and more on the capacity to set objectives, establish priorities, interpret information and guide people towards shared outcomes.
AI Adoption in Business: The Gap Between Use and Strategy
Artificial intelligence has already become part of many employees’ daily activities, but using AI does not necessarily mean that an organisation has undergone a structural transformation.
According to figures cited by Professor Mariano Corso, Scientific Director of the HR Innovation Observatory at Politecnico di Milano, nearly one in two workers already uses AI in their daily activities, while only one in four organisations has begun to fundamentally rethink its processes, roles and working models in response to this technology.
This gap highlights a central issue: many businesses use artificial intelligence to perform existing tasks more quickly, without fully exploring the opportunities offered by a deeper redesign of their processes.
AI, however, delivers its greatest potential when integrated into a strategy that is consistent with business objectives. Introducing new tools without redefining responsibilities, decision-making processes and skills risks doing little more than improving an organisational model that may no longer meet future needs.
For this reason, technological innovation must be accompanied by organisational and cultural change, involving both management and all the people who contribute to the business.
A New Leadership Model: Technology, Purpose and Human Value
The transformation of management also affects how decisions are made and how people are managed.
Research by the Purpose in Action Observatory at POLIMI Graduate School of Management shows that AI is already being applied across several managerial functions. Among the managers surveyed, 30% use it to support decision-making, 27% for people analytics and skills planning, and 25% for performance evaluation and employee development.
These figures show that AI is increasingly becoming part of functions that directly influence how organisations and their people are managed. At the same time, there is a risk that its adoption will be driven primarily by short-term objectives.
According to the same research, 60% of managers identify efficiency and cost reduction as the main drivers behind the adoption of AI models and agents. By contrast, only 13% state that the objectives assigned to these systems are fully clear and aligned with their organisation’s purpose.
This finding underlines the importance of leadership that goes beyond purely operational considerations. Efficiency is a legitimate objective, but it cannot be the sole criterion for evaluating the introduction of new technology. Businesses must also consider the value they aim to create, the quality of their decisions, the implications for people and the alignment between innovation and strategy.
In this context, purpose can provide a framework for guiding technological choices, provided it is translated into concrete objectives and measurable criteria. Declaring a corporate purpose is not enough: it must genuinely influence decisions, priorities and expected outcomes.
However, organisational readiness remains a significant challenge. According to the same research, only 9% of managers believe their organisation is fully prepared to lead teams in which people and AI systems work together. Future leadership will therefore require not only technological expertise, but also sound judgement, accountability and an understanding of human dynamics.
The Future of Skills: Rethinking Training for the Next Generation
Another fundamental issue concerns professional development and the opportunities available to younger workers.
Generative AI can already perform many tasks traditionally assigned to interns, trainees and junior employees. Information research, preliminary analysis, document preparation and repetitive activities can be automated or accelerated through digital tools.
This scenario offers productivity benefits, but it also raises a strategic question for businesses: if some of the tasks through which young professionals traditionally gain experience are progressively automated, what opportunities will remain for them to develop the skills needed to become independent professionals and future managers?
Early-career experience is not merely an operational phase of employment. It is a learning process through which individuals acquire working methods, develop judgement and become familiar with organisational dynamics. Significantly reducing these opportunities could, over time, undermine the development of the very skills businesses will need in the future.
It is therefore essential to rethink learning pathways and find a balance between automation and practical training. AI can help accelerate learning and make certain knowledge more accessible, but it must be incorporated into development programmes that allow young professionals to build autonomy, critical thinking and problem-solving skills.
For businesses, investing in training means more than updating employees’ technical knowledge. It means creating the conditions for people to continue developing in a professional environment that is constantly evolving.
Metacognition and Continuous Learning: The Value of Reflective Professionals
In an environment characterised by an ever-growing volume of information and the widespread adoption of artificial intelligence, the ability to evaluate not only what we know but also how we learn and make decisions is becoming increasingly important.
This is where metacognition becomes relevant: the ability to observe, understand and regulate one’s own cognitive processes. In a professional context, it helps individuals recognise the limits of their knowledge, assess the effectiveness of their strategies and adjust their approach when circumstances require it.
This concept is closely connected to the idea of the reflective practitioner, developed by philosopher Donald Schön. A reflective practitioner does not simply apply established procedures, but observes situations, interprets results, experiments with solutions and adapts their actions in response to experience.
This ability becomes particularly valuable in an environment where AI can generate answers and suggestions within seconds. Access to an advanced tool does not eliminate the need to verify information, understand the context and critically evaluate the alternatives it proposes.
The same principle applies to organisations. Businesses that learn from their results, acknowledge mistakes and review their processes are better equipped to navigate change. Innovation thus becomes an ongoing process in which technology supports improvement without replacing human responsibility.
Conclusion: Managers as Leaders of Change
Artificial intelligence is reshaping the relationship between technology, work and organisations, requiring businesses to reconsider established management models. This transformation concerns not only the tools being used, but also managerial responsibilities, professional development and the way business value is defined.
In this context, managers must develop a form of leadership capable of combining operational efficiency, strategic vision and the development of people. Technology can automate tasks, support decisions and improve processes, but its effectiveness also depends on clear objectives, strong capabilities and an organisation’s ability to adapt.
The real challenge, therefore, is not simply to adopt artificial intelligence, but to understand how it can be used to build businesses that are more adaptable, forward-thinking and aware of their responsibilities. Competitive advantage will depend not only on access to the most advanced technologies, but also on the ability to integrate them into an organisational model that combines innovation, learning and human value.
