Philosophical foundations every AI leader must understand

Johan Steyn, a human-centred AI advocate.

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I firmly believe the future success of artificial intelligence (AI) in business hinges not merely on technological prowess but on a deep engagement with the philosophical foundations that shape AI systems.

This perspective is at the heart of the seminal article “Philosophy eats AI”by Michael Schrage and David Kiron, published in the MIT Sloan Management Review, in January.

The authors argue that sustainable business value from AI depends on leaders critically examining and intentionally shaping the philosophical assumptions − teleology (the purpose of AI), epistemology (what counts as knowledge) and ontology (how AI represents reality) − that govern AI’s development, training and deployment.

Without this rigorous philosophical insight, organisations risk suboptimal returns and competitive disadvantage.

Much of the current discourse around AI focuses on ethics and responsible AI frameworks. While these are important, Schrage and Kiron caution that ethics represents only a small part of the philosophical perspectives that truly influence AI’s production and utility.

By privileging ethical guidelines alone, organisations risk overlooking the deeper questions of what AI systems are fundamentally designed to achieve, how they define and acquire knowledge, and how they conceptualise and represent reality. These philosophical dimensions shape not only the outputs AI produces but also the strategic value it delivers to businesses.

The growing recognition of philosophy’s role in AI is reflected in the practices of leading innovators and investors.

This broader philosophical framework helps explain why some AI initiatives succeed while others fail. For instance, Google’s Gemini project stumbled due to conflicting objectives and philosophical confusion about its purpose, whereas Starbucks and Amazon have thrived by clearly articulating and aligning their AI systems with well-defined philosophical priorities, such as enhancing customer loyalty or operational efficiency.

This illustrates that AI’s success is as much about clarity of purpose and epistemic rigour as it is about technical sophistication.

The three pillars of philosophy − teleology, epistemology and ontology − offer a powerful lens for leaders to evaluate and guide their AI strategies.

Teleology asks: What is the purpose of this AI system? Is it designed to maximise profit, improve customer experience, or disrupt an industry?

Epistemology probes what counts as valid knowledge for the AI − how data is selected, interpreted and validated.

Ontology examines how AI represents the world − what assumptions it makes about reality and how it categorises information.

Without explicit consideration of these dimensions, AI systems risk misaligned objectives, flawed knowledge bases and distorted representations of reality, which can lead to poor business outcomes.

Thoughtful engagement with these philosophical questions enables organisations to design AI systems that are not only technically sound but strategically coherent and aligned with organisational goals.

The growing recognition of philosophy’s role in AI is reflected in the practices of leading innovators and investors. Figures such as Peter Thiel (PayPal), Alex Karp (Palantir Technologies), Fei-Fei Li (Stanford University) and Stephen Wolfram (Wolfram Research) openly emphasise philosophical rigour as a driver of their AI work.

This trend is supported by empirical research indicating that organisations which cultivate philosophical insight in their AI initiatives achieve superior returns and competitive advantage.

Recent research supports the critical role of philosophical frameworks in enhancing AI’s interpretability and trustworthiness, which are essential for enterprise adoption.

For instance, a comprehensive study by Ferrario et al. proposes a novel philosophical framework integrating ontological, ethical, epistemological and existential perspectives to guide next-generation AI development. This approach addresses key challenges, such as AI’s unpredictable ethical impacts and its role in co-constituting human knowledge, thereby improving transparency and accountability in AI systems.

According to a paper by Ferrario et al., by grounding AI design in rigorous philosophical inquiry, organisations can better align AI capabilities with human values and societal needs, fostering greater trust and practical utility.

Similarly, in his paper, Dr Brent Mittelstadt, director of research, associate professor and senior research fellow at the Oxford Internet Institute, University of Oxford, emphasises that AI systems founded on clear teleological and epistemological principles demonstrate superior performance, especially in complex decision-making environments.

By advocating for a “glass-box epistemology”, Mittelstadt argues that transparency in how AI systems acquire and process knowledge is crucial for aligning AI outputs with ethical standards and organisational goals. This philosophical clarity enhances both the reliability and accountability of AI, making it more effective and trustworthy in practical applications.

For executives and AI leaders, the challenge is to move beyond tacit or unarticulated philosophical assumptions and to cultivate explicit philosophical literacy across their organisations.

This involves mapping the teleological, epistemological and ontological frameworks that underpin AI systems, ensuring alignment with corporate strategy and values.

It also requires fostering cross-disciplinary dialogue between technologists, ethicists and business strategists to surface and scrutinise these foundational assumptions.

Transparency about the philosophical choices embedded in AI systems can build trust among stakeholders and mitigate risks associated with bias, misinterpretation and unintended consequences. As AI increasingly exercises agency and makes autonomous decisions, philosophical clarity becomes indispensable for governance and accountability.

Philosophy is not an optional academic exercise but a strategic imperative for any organisation seeking to harness AI’s transformative potential. As Schrage and Kiron compellingly put it: “Philosophy eats AI.”

Leaders who embrace this reality and rigorously engage with the philosophical dimensions of AI will unlock superior business value, mitigate strategic risks and gain a sustainable competitive advantage.

The call to action is clear: executives must prioritise philosophical literacy and integrate philosophical frameworks into their AI strategies today.

Failure to do so risks ceding ground to competitors who understand that AI’s true power lies not just in algorithms and data but in the philosophical clarity that guides their design and deployment.