I’ve been thinking a lot this week about that MIT Sloan piece that I shared in FF686 on how ‘Philosophy Eats AI’. The piece argues that three branches of philosophy are already embedded in every AI deployment whether leaders recognise it or not: teleology (what should AI models achieve?), epistemology (what counts as knowledge?), and ontology (how does AI represent reality?). The challenge is whether organisations will cultivate a philosophical approach or just default to whatever philosophical assumptions are baked into the models and tools they’re using.
The authors of the MIT piece quote the Greek poet Archilochus: ‘We don’t rise to the level of our expectations; we fall to the level of our training.’ Every prompt, parameter, and deployment, they write, encodes philosophical assumptions about knowledge, truth, purpose, and value.
The point is that organisations need to develop not only an AI strategy, but an AI philosophy. One that goes beyond ethical guidelines and guardrails, and deeply into how AI should be designed, deployed, used and advanced. And how it should reason, think and act. Failure to deliberately design for these things means that your organisation will default to the same generic, widely used assumptions that everyone else is probably using.
This philosophical training problem becomes drastically more important as AI shifts to autonomous agents. Agentic AI systems don’t just process and generate language, they contextually understand goals, formulate plans, make decisions and take autonomous actions. How they do this in a way that aligns with an organisation’s values, outlook or positioning is as much about philosophy as it is about strategy.
So what’s the difference between an AI strategy and an AI philosophy? The short answer to that question is that the former should be focused on the what and the where – which capabilities to build, where to deploy them, how to resource and sequence the work, and what the expected returns look like. An AI philosophy however, defines the principles, assumptions and beliefs that should govern how AI reasons, decides, and acts within an organisation. It’s what makes an organisation’s AI distinctively their own rather than a generic deployment of someone else’s defaults. A few simple examples of the differences:
You might say that this is more related to organisational culture than strategy. If culture is comprised, as Edgar Schein defined it, of observable artefacts, stated espoused values, and unconscious basic assumptions, then developing a philosophy for AI is (for me at least) about attempting to codify that organisational positioning and culture. Peter Drucker reportedly once said that ‘culture eats strategy for breakfast’. Meaning that you can have the best strategy in the world but if the culture doesn’t support it the strategy will fail. So much of organisational culture is about the assumed ways of working, the daily habits that get stuff done, the unspoken beliefs that shape decisions. And it is so often those inherent systems of work that determine success rather than lofty leadership visions. As I’ve written before, systems beat goals.
An AI philosophy should articulate the organisation’s position on a set of fundamental questions. What is AI for in this organisation, and what is it not for? What counts as knowledge, insight or judgment here, and when should AI defer to human expertise? How do we represent the things that matter most to us (customers, markets, creative work, risk) and what do we lose or distort when we reduce them to data? What values and priorities should govern how AI acts when it operates with autonomy? And, whilst we’re at it, what is the relationship between AI and the people who work alongside it? Is AI a tool, a collaborator, a decision support system, or something else?
Strategy can, in theory, be borrowed or copied. You can look at what competitors are doing and build something similar. Culture, and by extension philosophy, can’t be reproduced because it is unique to every business. Which means that developing an AI philosophy forces a harder conversation than developing an AI strategy because it requires leaders and teams to articulate things they may never have had to make explicit before.
Archilochus was right. We fall to the level of our training. Every time an organisation deploys AI without making its own assumptions explicit, it accepts whatever defaults come baked in. And those defaults reflect someone else’s philosophy, not yours.
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Photo by Giammarco Boscaro on Unsplash
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