In this episode, Host Taylor Baker sits down with Dave McComb, CEO and co-founder of Semantic Arts, to discuss why “meaning” (semantics) gets lost inside large enterprises and how a data-centric approach can reduce software waste, simplify complexity, and make organizations truly ready for AI.
What You’ll Learn
- Why semantics is fundamentally about shared meaning, and why meaning breaks down as companies scale.
- How application sprawl creates thousands of disconnected data models and why that fragmentation becomes a major hidden cost.
- The concept of a single “core model” of the business, and how an enterprise ontology can serve as a stabilizing foundation.
- Why documenting the model isn’t enough value comes from implementation and aligning real data and systems to shared definitions.
- How knowledge graphs can help mainstream enterprises consume and connect information more like digital-native companies.
- Practical lessons from scaling a founder-led professional services firm: building a long-tenured team, creating repeatable delivery, and reducing project risk.
- A clear go-to-market insight: the three-circle challenge of finding organizations that need the work, have the budget, and truly “get it.”
- How AI can accelerate semantic and data-centric work when used as an assistant and why becoming “AI-ready” starts with organizing data and meaning first.
The overarching message is that most enterprise “data problems” are actually meaning problems. By rebuilding a shared semantic foundation then embedding it through implementation organizations can cut down on software waste, improve decision-making, and unlock AI’s value with far less risk and confusion.
To learn more about Dave McComb and their work.