Using Ancient Frameworks to Navigate the AI Era

• In the AI age, researchers can use frameworks developed by Plato and Aristotle to question assumptions, integrate multiple ways of knowing, and make ethical choices.
• Using modern research tools based on ancient Greek concepts, organizations can assess how AI affects their operations, determine the reliability of AI-generated insights, and measure the impact of their actions on human flourishing.
• The Greeks uncovered knowledge through inquiry and debate. Contemporary business schools can establish research communities where diverse stakeholders discuss important issues.

 
Today’s increasingly complex challenges demand thought leadership that transcends traditional academic boundaries. As business schools grapple with their responsibility to address society’s most pressing issues, we face a fundamental question: How do we reimagine research when the very nature of business, knowledge, and human agency is being transformed by artificial intelligence (AI)?

The answer requires us to undertake research that does more than incrementally advance knowledge within established frameworks. Because AI challenges the frameworks themselves, we need a paradigm shift. We need scholarship that questions foundational assumptions, integrates multiple ways of knowing, and guides ethical action in unprecedented circumstances.

Surprisingly, the most innovative research paradigm for our AI future may come from humanity’s most ancient educational institutions: Plato’s Academy and Aristotle’s Lyceum. Those weren’t just schools—they were laboratories for fundamental inquiry that combined rigorous investigation with practical wisdom. Their research approach integrated what we now call ontology, epistemology, and ethics with the practical arts of governance and communication.

Our AI era demands that we break free of our traditional research silos and follow paths of integrated inquiry. Rather than simply studying AI’s business impacts, we need to examine the fundamental questions AI raises about the nature of reality, knowledge, and virtue in business contexts. Ancient frameworks can show us how.

Ontology: What Is Real?

Our first questions draw from ontology, or the study of being and existence. In Plato’s Academy, students began with the most fundamental question: What is real? Their answers were based not on observation, but on rationalism—an understanding that what is “real” is not just what can be seen, but what is actually there.

In the AI era of business education, we ask: What exists and will exist in the world of business? For instance, what defines a “customer” when AI predicts behavior? What is a “product” if software replicates infinitely? What is “work” when AI handles cognitive tasks?

In an AI economy, firms, markets, and value are fluid, so the answers to these questions are not fixed. Today’s business researchers need ontological sophistication to recognize shifting realities and question the validity of existing categories, not just optimize within them.

Consider the rise of business platforms, where traditional distinctions between producers and consumers blur. Or the emergence of AI-generated content, which challenges our understanding of creativity and intellectual property. These aren’t merely operational challenges that require technical solutions—they’re ontological disruptions that require philosophical inquiry.

Epistemology: What Is Known?

Next, we turn to epistemology, or the study of the origins and limits of knowledge. In Aristotle’s Lyceum, students pioneered a form of epistemology called empiricism, which holds that people gain knowledge through systematically observing what exists.

Today, business students need epistemological awareness to navigate an information landscape where AI can create convincing falsehoods, correlation and causation are difficult to discern, and traditional metrics fail to capture business reality. Students must understand not just how to analyze data, but how to question the data itself. How was it collected? What biases might it contain? What assumptions underlie the algorithms that processed it? When should human judgment override algorithmic recommendations?

Researchers must learn to hold their conclusions lightly, question their assumptions continuously, and remain open to paradigm shifts that could invalidate their entire worldviews.

To distinguish reliable knowledge from misinformation in an AI-driven world, future business leaders must understand the limitations of both human and artificial intelligence and know how to combine them effectively.

Similarly, researchers must move beyond methodological rigor to more relevant “epistemological humility.” They must learn to hold their conclusions lightly, question their assumptions continuously, and remain open to paradigm shifts that could invalidate their entire worldviews.

Ethics: What Contributes to Well-Being?

The third piece of the puzzle is an understanding of ethics. Plato and Aristotle both stressed that ethical behavior will lead to eudaimonia, or human flourishing. Aristotle further taught that virtues are good habits that we practice over time.

Traditional business ethics discussions often focus on compliance. However, in an AI-driven business world, eudaimonia-centered ethics conversations will explore deeper questions about societal impact, human flourishing, and the virtues guiding technology-related dilemmas.

For instance, challenges such as algorithmic bias and worker displacement demand to be solved through virtue-based reasoning. To manage complex ethical conflicts, students need practical wisdom, or what the Greeks called phronesis. The goal for a business educator is to help students cultivate the intellectual virtues—wisdom, justice, courage, and temperance—that will provide them with the sound ethical reasoning skills they need in an era of rapid change.

Two More Pieces: Governance and Communication

But students must learn more than how to incorporate ontology, epistemology, and ethics into business operations. They also must master politics and rhetoric.

In ancient Greece, politics wasn’t about partisan competition—it was about the art of collective decision-making and governance. Today, AI has transformed organizational politics in fundamental ways that force us to ask new questions. How can managers build consensus and maintain human agency when algorithms make many decisions? How can leaders govern across traditional boundaries when AI enables new forms of network organization?

Students need to grasp the political dynamics of technological change, learning to navigate stakeholder ecosystems that include shareholders, employees, affected communities, and the people who are data subjects—as well as algorithms. They also need to acquire skills in democratic deliberation and inclusive decision-making to handle the complexity and speed of AI-driven change.

Students must learn how to build trust in an environment of synthetic media, where the boundary between human and machine communication is unclear.

The practice of rhetoric—the art of persuasive communication—was essential to educated citizenship in Aristotle’s time. It remains essential today, as leaders seek ways to communicate effectively through AI intermediaries to maintain creativity and authenticity.

Students must learn to communicate with both human audiences and artificial audiences, such as social media algorithms and chatbots. They must know how to maintain and build trust in an environment of synthetic media, where the boundary between human and machine communication is unclear. And they must be able to translate between technical and nontechnical stakeholders so that everyone has the relevant information.

Bringing Knowledge to the Classroom

At the Academy and Lyceum, students learned crucial concepts through dialectical inquiry, refining their understanding through questioning and debate. At modern business schools, students can explore these concepts through simulations and role-playing exercises in which they practice making decisions that balance competing interests.

As an example, in my business course simulation, I split students into three groups that discuss the choice facing a publicly traded multinational company. Should the company implement an AI system that could improve customer service by 50 percent but would eliminate 70 percent of call center jobs in a country where livelihoods depend on these jobs?

Group One, composed of AI advocates, focuses on the company’s need to stay current and competitive. Group Two, which represents the affected workers, highlights the cost of social friction and questions the validity and reliability of AI opinions. Group Three considers a hybrid solution but has to demarcate where AI and human decisions begin and end.

Rather than rushing to a decision, students engage in the kind of extended deliberation that characterized Athenian democracy. Each stakeholder group researches its position, presents evidence, and engages in structured debate. Students learn that effective AI governance requires not just technical analysis, but also the political skills to build consensus across competing interests. Students also discover that managing AI-human organizations requires entirely new forms of democratic decision-making.

In another classroom exercise, students learn to assess misinformation and manage information warfare. Students are divided into Red Teams (misinformation creators) and Blue Teams (detection specialists). Red Teams use AI tools such as GPT-4 and DALL-E to create credible but false business intelligence—fake market research, manipulated data, fabricated expert interviews, and convincing company reports. Blue Teams simultaneously develop systematic verification methods, including source credibility matrices, data validation toolkits, and cross-reference protocols.

Mid-semester, Red Teams present their false intelligence to executive panels and business forums, while Blue Teams counter with detection findings designed to mitigate and manage misinformation. Students experience firsthand how easy it is to create believable misinformation and how difficult it can be to detect it. At the same time, they develop verification strategies, learn to recognize cognitive biases, and build practical epistemological skills.

It’s vital for classroom exercises such as these to be based on insights that faculty have uncovered in their research. When faculty research is accessible to students, it provides a wellspring of practical knowledge.

Redirecting Research

Faculty research itself is undergoing a transformation as AI reshapes the business landscape. Socrates engaged in “public philosophy” by discussing issues of great importance to the general population. Business school researchers must do the same, addressing fundamental questions on issues that matter to both academia and society.

Every empirical study should include ontological reflection: What business realities are we assuming exist? Every data-driven analysis should incorporate epistemological scrutiny: What are the limits and biases in our ways of knowing? Every strategic recommendation should undergo ethical examination: How does this serve human flourishing?

Business school researchers must engage in public philosophy by addressing issues that matter to both academia and society.

Answering these questions will require faculty to conduct research that extends beyond journal boundaries. Three types of research are becoming especially useful:

• Ontological impact assessments. Such assessments help organizations recognize when AI is fundamentally changing the nature of their businesses, not just optimizing existing processes. These frameworks serve practicing managers while generating new theoretical insights about business transformation.
• Epistemological audit methodologies. Organizations need practical tools to evaluate the reliability of AI-generated insights and human-machine decision-making processes. Such tools serve immediate business needs while advancing our understanding of how knowledge is created in hybrid human-AI systems.
• Eudaimonia-focused business metrics. New measures of organizational success evaluate companies based on their contributions to human flourishing, not just their financial performance. Such research generates novel theoretical frameworks that address society’s need for alternative progress indicators.

‘More Human Wisdom, Not Less’

The Academy and Lyceum succeeded not because they produced the most journal publications, but because they cultivated minds capable of thoughtful action in uncertain circumstances. Their students gained knowledge and refined understanding by engaging in questioning and debate. Graduates didn’t just master existing knowledge, but also developed the intellectual virtues needed to handle unprecedented challenges.

Business schools can create modern equivalents by establishing research communities that bring together academics, practitioners, policymakers, and affected stakeholders. These research communities can engage in sustained philosophical dialogue to investigate AI’s implications for business, and they also can lead society’s response to AI disruption. By bringing academic rigor to bear on the most fundamental questions of our era, researchers can provide insights related to AI governance, business transformation, and human flourishing.

In an age of artificial intelligence, society needs more human wisdom, not less. Business schools must decide whether they want to be reactive institutions that simply adapt to technological change or proactive leaders that help society think carefully about the future we’re creating.

The ancient Greeks remind us that true thought leadership begins with the courage to ask fundamental questions and the commitment to pursue them rigorously. The Academy and Lyceum show us the way forward today: toward research that serves both understanding and wisdom; education that develops both competence and character; and thought leadership that addresses not just how to navigate change, but how to shape it for the benefit of all.