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A place for practical thinking from the field: building BI foundations, decision systems, AI workflows, and products from unclear starting points.
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Why permanent dashboards and recurring AI prompts both fail, and how enterprises can separate exploratory analytics from governed analytical products.
Why giving teams access to ChatGPT, Claude, Gemini, or Qwen is not the same as redesigning how work actually moves.
A practical view of where AI agents help analytics teams, where they create risk, and which logic still needs human ownership.
Why strong analytics teams increasingly build internal products, workflows, interfaces, and automations instead of only answering questions.
How analytics teams can move beyond request queues and become part of the way the business actually operates.