Engineer the Enterprise Before Teaching AI
💡Learn why enterprise AI should start with standardized workflows and data—not bigger models.
⚡ 30-Second TL;DR
What Changed
Business operations should be standardized before introducing AI models.
Why It Matters
The approach shifts enterprise AI investment from model-first experimentation toward process, data, and governance foundations. It suggests that companies with fragmented workflows may need operational engineering before they can realize value from generative AI.
What To Do Next
Map one high-value workflow, define its canonical data schema, and expose the standardized records through an internal API before adding an LLM.
Key Points
- •Business operations should be standardized before introducing AI models.
- •Data standardization is a prerequisite for reliable enterprise AI applications.
- •Models and computing power are treated as infrastructure after the operating system is established.
- •BELLE’s transformation emphasizes building a structured, process-oriented enterprise over pursuing AI alone.
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Original source: 钛媒体 ↗
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