Weaving Intelligence
Natural Language Interfaces & AI in BI
AI + BI
The Names Were the Guard-Rails: What Early Smart BI Left the AI That Calls It
Before language models, the intelligence in the business intelligence I built was mostly translation, held up by naming standards nobody was allowed to break. Some of that wave was retired. Most of it was absorbed, and today's AI reads the same names and calls the libraries that inherited its…
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Just a Tool: The One AI Myth Worth Naming, and What Is True Instead
The one AI myth worth naming is that it will end everyone's job. What the evidence says, what is true instead for natural-language query on master data (garbage in, garbage out; people still frame the question and agree the definitions), where the practitioner's own answer goes too far, and what…
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Precise and Wrong: Master Data, Semantic Layers and the Next Decade of Natural-Language Answers
A semantic layer built first makes natural-language answers exact, repeatable and traceable; it does not make them true. Why master data quality comes first, why a useful measurement of the gap is already a project plan, and why the machines will write the semantic layer long before anyone agrees…
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The Demo and the System: Implementing and Governing Natural-Language Query
Natural-language query is sold as one thing and built as two: a funding demo that needs only a model and a data store, and a system that needs agreed definitions, an approved semantic model served through one governed route, and mature master data underneath. What each part asks of the people…
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