OP, updating graph to include 2018-2023

Everyone's rushing to implement AI tools, but nobody wants to talk about the fact that their data is inconsistent, poorly labeled, scattered across 15 systems, and has zero governance.

You can't just dump messy data into an LLM and expect magic. Garbage in, garbage out still applies.

Companies keep buying expensive AI tools and then wonder why they're not getting value. It's because they skipped the boring foundational work: data classification, access controls, cleaning up duplicates, actually documenting what data means.

Am I crazy or is everyone else seeing this too? How are you convincing leadership that data prep isn't optional?