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A Blog for all things Tag, AI, and Document Generation

Rob Brown Rob Brown

AI Data Modeling - Intro

AI has fundamentally changed how organizations process data, but our traditional data modeling approaches weren't designed for this reality. AI data modeling bridges the gap - extending proven data management principles to account for prompts, confidence scores, unstructured inputs, and human-AI collaboration patterns. It's not about discarding what you know; it's about making AI integration deliberate, documented, and repeatable.

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Rob Brown Rob Brown

Visualizing AI Data Flows

Traditional data flow diagrams show how information moves through systems, but when AI enters the picture, critical details become invisible. Where does AI interpret unstructured data? Which models process sensitive information? Where do humans review AI decisions? AI data flows extend traditional data modeling to make AI touchpoints explicit, governable, and reusable. This article shows you how to visualize, document, and operationalize AI-augmented workflows using practical examples and proven methodologies.
Artwork by vecteezy.com.

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Dawn Brown Dawn Brown

Who is “At-the-Wheel” and Who is “In-the-Loop”?

Discover why humans should be at the wheel, not just in the loop, when using AI for professional work. Learn how corporate AI model training often fails due to errors, hallucinations, and model collapse, and how breaking tasks into small, well-scoped steps with tight human oversight preserves judgment, accountability, and workflow integrity. Explore Tag’s AI-in-the-Loop approach, where humans control relevance and decision-making while AI handles busywork safely and efficiently.

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Dawn Brown Dawn Brown

Finding Meaning is Still a Human Task

This post presents the argument that meaning is inherently human and cannot be generated by AI, emphasizing responsibility and choice as prerequisites for genuine significance. It critiques anthropomorphizing AI, showing how “language fluency” and the “illusion of wisdom” can mislead us into outsourcing judgment and moral accountability. By connecting Frankl’s existential insights with McLuhan’s media theory, the post highlights both the cultural allure and the ethical hazards of relying on AI for understanding or guidance, ultimately underscoring that while AI can simulate comprehension, the pursuit of meaning remains a human endeavor.

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