From Systems Thinking to Agentic Thinking: Rethinking Management in the Age of Intelligent Systems

• by Simran Rajpal

When McKinsey’s Superagency in the Workplace (2025) report claimed that “AI is multiplying human agency,” it wasn’t merely describing an upgrade in workplace tools. It was declaring a conceptual shift in how management operates from coordination to coexistence.

For over a century, management has been the architecture of order: the art of organizing people, processes, and purpose to create efficiency and control. Yet in the age of artificial intelligence, these principles no longer suffice. AI doesn’t merely execute management decisions, it redefines how decisions are made.

This shift is more than technological. It’s philosophical. And it challenges every theory of management I have studied at the Alliance Manchester Business School.

1.  From Taylor to Tensor: The End of Predictability

Frederick Taylor’s Scientific Management gave us predictability breaking down labor into measurable motions. Henry Ford industrialized that principle, creating an age of “mechanical precision.” The twentieth century belonged to those who could optimize processes.
But the twenty-first century belongs to those who can interpret systems that learn.
AI transforms predictability into fluidity. Algorithms no longer obey what they anticipate. As Actor-Network Theory (Callon, Latour) suggests, technologies are not neutral tools but actors that shape networks, redistribute power, and alter human agency.
Managers, therefore, no longer manage people or processes, they manage relationships of intelligence. The challenge is not efficiency, but alignment: ensuring that human ethics, machine logic, and organizational goals co-evolve rather than collide.

• 2.  The Organization as an Intelligent Network

During my Image Analysis course, we examined Morgan’s metaphors seeing organizations as machines, organisms, cultures, and political systems. Each metaphor revealed a truth and a limitation. But in an AI-enabled world, a new metaphor is emerging, the organization as a cognitive network. Here, information is not communicated; it is computed. Knowledge is not stored; it is synthesized. This mirrors Bruno Latour’s ANT perspective power that doesn't flow from the top but circulates through networks of human and non-human actors. In modern enterprises, those non-human actors are algorithms. The “management hierarchy” has become a “management web.”

Gareth Morgan’s “brain” metaphor once metaphorical is now literal. Machine         learning systems enable firms to analyze behavior, predict outcomes, and           autonomously act upon data in ways even senior leadership cannot fully explain.

3.  Drucker’s Knowledge Worker, Reimagined

Peter Drucker defined the knowledge worker as the central figure of modern capitalism, a professional who creates value through thought, not manual labor. But AI now thinks faster than we can.

This doesn’t invalidate Drucker, it completes him. If Drucker’s management sought to make human strengths productive and human weaknesses irrelevant, AI does precisely that at scale.

Yet Drucker also warned that management’s ultimate task is to give work meaning. Here lies the paradox. AI can generate outputs, but not meaning. It can optimize, but not purpose. The human task, then, evolves: not to compete with machine intelligence, but to contextualize it to ensure that the why of management survives the how of automation.

4.  The Rise of Agentic Management

McKinsey’s concept of superagency reflects a broader shift I’ve observed through the lens of engineering management a move from systems thinking (Senge) to agentic thinking. Systems thinking aimed to understand interconnections within stable frameworks. Agentic thinking accepts instability and works through it.

AI introduces non-linearity into management logic. It transforms the manager’s role from planner to sense-maker, from controller to curator of intelligence.

This is consistent with Mintzberg’s real-world observation that management is not a science of decisions but an art of interpretation. In the era of AI, that art becomes indispensable. The new managerial capital isn’t data, it's discernment.

5.  Schein’s Culture in a Coded World

Edgar Schein’s model of organizational culture artifacts, espoused values, and underlying assumptions rests on shared human meaning. But what happens when part of that meaning is written in code?

AI-driven organizations now possess dual cultures: the human and the algorithmic. One is built on emotion, trust, and symbolic language; the other on data structures and optimization. Leadership, therefore, must evolve into cultural translation ensuring that algorithmic systems reflect ethical and emotional intelligence, not just computational accuracy.

This is not an HR problem, it’s a strategic one. Culture, as Schein insisted, is learned through adaptation and AI is now part of that learning loop.

6.  Taylorism 4.0: When the Machine Manages Back

 

Critics of AI in management often resurrect Taylor’s ghost warning of digital Taylorism, where algorithms track performance metrics and replace human judgment. But perhaps this fear misses the point.

Taylor’s objective was to replace inefficiency with logic. AI simply takes that logic to its inevitable conclusion, self-optimizing organizations. What seems like “replacement” is actually evolution removing layers of administrative redundancy so that human creativity can focus where it matters: purpose, ethics, innovation. AI is not dehumanizing management, it is de-bureaucratizing it.

7.  Bridging Academia and Application

At university, I learned models that seemed distinct: Porter’s competitive forces, Chandler’s structure follows strategy, Schein’s culture layers, Morgan’s metaphors.

AI blends them all.

Strategy becomes adaptive. Structure becomes fluid.

Culture becomes data-encoded behavior.

And leadership once about inspiration becomes interpretation under uncertainty.

The transition from classroom frameworks to real-world algorithms taught me something crucial: management theory isn’t obsolete, it's becoming executable code.

8.  Beyond Fear: The Ethics of Replacement

To say AI is replacing management isn’t dystopian; it’s descriptive. The question isn’t whether replacement will happen it’s how consciously we design it.

AI can lead meetings, assign resources, and predict employee burnout before it happens. But it cannot yet decide what a “good” organization looks like. That moral architecture still depends on us.

The true managerial challenge is no longer “How do I make better decisions?” but “How do I ensure the system’s decisions remain humanly accountable?”

We are not losing control; we are losing monopoly and perhaps that’s progress.

9.  The New Managerial Literacies

From this synthesis of theory and technology, five literacies define the new age of engineering management:

1.     Interpretive literacy – translating algorithmic logic into organizational meaning.
2.     Ethical literacy – aligning AI outcomes with social and cultural values.
3.     Integration literacy – embedding intelligent systems within complex human ecosystems.
4.     Creative literacy – using AI to expand, not automate, ideation.
5.     Resilience literacy – leading through volatility, ambiguity, and algorithmic bias.

These literacies are not technical, they are philosophical. They represent a new social contract between intelligence (human and artificial) and leadership.

10.  Closing Reflection: The Manager After Management

In Images of Organization, Morgan wrote that “management is not a fixed concept but a metaphor that evolves with the world.” AI offers the next metaphor, the organization as consciousness one capable of self-learning, self-correcting, and self-evolving.

Engineering management, therefore, is not ending. It is transcending its human limitations. AI doesn’t replace managers; it replaces managerial complacency. The manager of tomorrow will not be defined by their ability to plan or control, but by their ability to interpret, design, and dialogue with intelligence itself.

If the twentieth century’s motto was “efficiency through structure,” then the twenty-first’s must be “ethics through intelligence.”

References

Avent, L. (2025). The Role of AI in Engineering Management. MEM Blog, North Carolina State University.

Mayer, H., Yee, L., Chui, M., & Roberts, R. (2025). Superagency in the Workplace: Empowering People to Unlock AI’s Full Potential. McKinsey & Company.

Morgan, G. (2019). Images of Organization. SAGE Publications. Schein, E. (2010). Organizational Culture and Leadership. Jossey-Bass. Drucker, P. (1959). The Landmarks of Tomorrow. Harper & Row.

Taylor, F.W. (1911). Principles of Scientific Management. Harper & Brothers.

Latour, B. (2005). Reassembling the Social: An Introduction to Actor-Network-Theory. Oxford University Press.

About the Contributor:

Simran Rajpal is a postgraduate student of Management at Alliance Manchester Business School, specializing in innovation, strategic leadership, and organizational transformation in the age of AI. Her academic work explores how technology is redefining management theory from Taylorism and systems thinking to the emergence of agentic, intelligent organizations. At the intersection of business strategy, human creativity, and artificial intelligence, Simran writes reflective essays that bridge theory and practice, inviting readers to rethink what leadership, work, and ethics mean in a world increasingly managed by machines. She is the creator of the forthcoming newsletter “Beyond the Case Study,” where she blends management scholarship, current affairs, and personal inquiry to make complex ideas accessible and relevant for the next generation of leaders. (Subscribe on LinkedIn https://www.linkedin.com/build-relation/newsletter-follow?entityUrn=7369350312345726978) When she’s not studying or consulting, she can be found exploring how emerging technologies from generative AI to digital ecosystems are reshaping global business models, governance, and the human side of innovation.

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