Tony Seale starts from a provocative question posed by Michael Schrage and David Kiron in MIT Sloan: "If software eats the world and AI eats software, what eats AI?" The answer is philosophy — not in its academic sense, but as a practical discipline indispensable for extracting real value from AI systems.
The central problem is that most organizations treat AI as a purely technical improvement. They build models, experiment with prompts, but never ask the fundamental question: what does AI actually learn? Behind every model lies a deeper issue: the absence of a structured philosophy defining the company's operational logic.
Every company creates value in its own unique way — through loyalty, optimization, trust, or efficiency. Yet most have not formalized this semantics into an ontology, that is, a machine-readable structure on which AI systems can reason. The author insists: "This is not a matter of mission statements. It is a matter of semantics formalized into an ontology." Without making these fundamental logical structures explicit, models learn from noise rather than meaningful patterns.
The article introduces the concept of the ontological core: the fundamental concepts that define a company's identity. This core emerges through the use cases that generate real value and the "competency questions" that test the relevance of each concept. It becomes a lens that focuses AI reasoning on what actually matters.
Seale then connects this philosophy to data architecture via Semantic Data Products, based on the open DPROD specification. This approach treats data as a valued asset with clear governance, rather than as raw material. The result is a "distributed, AI-ready knowledge graph, where each dataset knows what it is, why it matters, and how it fits into the bigger picture."
The conclusion identifies the real opportunity: creating a virtuous cycle where AI helps define the organizational ontology, while that ontology guides how AI thinks and the value it generates. The article positions ontology not as an academic modeling exercise, but as the missing link between AI investment and business value creation.