Lessons Learned About Data & AI at Enterprises and SMEs
summary
Claim: enterprises and SMEs behave differently enough that cannot buy AI must build it becomes operational advice — commodity APIs ok, domain models require end-to-end data, infra and skills. Evidence: many lighthouses fail because of single-person prototypes and no tests; lighthouse problem: prototype collapses leads teams to trash or rebuild. Concrete tools and steps: Python and open source stacks, data lakes/hubs, APIs, BI, NLP pipelines; one OEM project succeeds with clustering and entity extraction on a research corpus. Quantified surprise: only ~5% of research models reach production.
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