
Years ago, my washing machine broke, and I had a repair person come by. By the time we got to the bottom step of the stairs, he knew exactly what was wrong. Ten minutes later, he came back up and handed me the bill for $120.
I wasn’t paying for 10 minutes of work—I was paying for 20 years of expertise. He knew the common failure points, how different manufacturers’ machines break down, and how to diagnose the issue instantly.
That knowledge—his ability to recognize patterns, categorize problems, and infer solutions quickly—is an ontology. It’s a structured way of organizing domain expertise. AI can work the same way. It doesn’t need to observe everything from scratch because someone already knows how things work.
What Is an Ontology in AI?
An ontology is a structured knowledge system that helps AI understand relationships between concepts. Instead of relying on massive amounts of data, AI can leverage pre-existing expertise to:
Make faster, more accurate decisions
Reduce reliance on large training datasets
Identify patterns without requiring constant observation
For example, a sales expert already knows:
The 10 essential steps to close a deal
The 4 types of messaging that resonate with buyers
The common objections and how to counter them
By structuring this knowledge into an AI ontology, teams can fast-track AI product development by enabling the system to:
Make informed recommendations based on established best practices
Ensure consistency and reliability instead of guessing
Spot gaps in existing strategies and suggest improvements
How Ontologies Improve AI Decision-Making
One of the biggest risks in AI is hallucination—when AI generates inaccurate or nonsensical results. Ontologies prevent this by:
Providing a structured framework that AI can rely on
Ensuring AI sticks to validated knowledge instead of making random associations
Allowing human experts to refine and expand AI knowledge over time
This human-in-the-loop approach creates a continuous learning loop, improving both AI accuracy and business insights.
Pre-Built Frameworks: The Shortcut to AI Product Success
Most AI applications aren’t solving completely new problems. They’re tackling challenges that people have already structured into frameworks.
If you think about cooking there are known techniques—grilling, roasting, sautéing. You don’t invent a new way to cook every time. Instead, you apply proven methods and focus on what makes your dish unique.
The same principle applies to AI product development. Pre-built AI frameworks allow teams to:
Reduce development time by building on existing knowledge
Improve AI reliability with structured methodologies
Accelerate time to market by skipping unnecessary trial and error
Choosing the Right AI Framework
When integrating AI ontologies into your product, consider:
Alignment with your core problem – Does the framework fit your AI use case?
Flexibility and customization – Can it adapt to your needs?
Industry support and documentation – Is there a strong knowledge base behind it?
Humans in the loop - Do you have access to experts to help
AI Reliability Through Ontological Design
By incorporating ontologies, AI product teams can move faster, avoid unnecessary guesswork, and build more reliable systems by:
Spotting gaps before they become problems
Reducing the risk of AI generating inaccurate outputs
Providing teams with a structured approach to development
This approach isn’t about limiting creativity—it’s about focusing innovation where it matters most.
Build Smarter, Move Faster
Leveraging ontologies and pre-built AI frameworks allows businesses to:
Accelerate AI development without starting from scratch
Ensure reliability and consistency in AI decision-making
Optimize efficiency and focus on innovation
With structured knowledge guiding AI, teams can get to market faster, improve product quality, and drive better business results.
Image: Took this in the glass-ceilinged Robert and Arlene Kogod Courtyard at the National Portrait Gallery in Washington DC.