Business users aren't data engineers. And that's the problem (blog.astrobee.ai)

AI assistants and LLMs can reason, plan, and even write SQL — but they routinely fail in real business settings because the underlying data is messy, fragmented, and semantically inconsistent. The article argues that most AI projects stall not for lack of model capability but because models are asked to “think” over conflicting definitions (multiple campaign IDs, duplicate revenue columns, differing attribution windows) and disparate sources (CRMs, ad platforms, spreadsheets). The result is confident but wrong answers: LLMs can generate logic but can’t establish a persistent, unified ground truth or remember corrections across sessions. The fix isn’t a bigger model but a persistent data layer that provides entity resolution (map “user,” “lead,” “customer”), normalization (currencies, time zones, formats), semantic alignment (consistent definitions of “revenue” or “ROI”), and proof/lineage (traceable calculations). Without those building blocks, every query becomes a one‑off cleanup task and business users remain dependent on data engineers. Platforms like AstroBee are presented as examples of that layer — mapping systems to shared entity definitions so AI can reason reliably. For the AI/ML community, this reframes the priority: invest as much in data foundations, schemas, and traceability as in model improvements to turn AI insights into trustworthy, repeatable business decisions.