Enterprise hits and misses - agentic AI project failure versus success, open source versus AI, and the perils of disconnected CX

My editorial self-challenge: boil down a year's worth of research/dialogue on AI project success (or lack thereof) into one post. And do it for an audience that must make sense of wildly disparate (and at times goofy) daily narratives:

What is an executive to make of so many conflicting headlines? AI is supposedly transforming industries, but AWS is knocking systems offline with vibe coding. We are in dire need of jugular context that weighs the upside and the downside.

Set aside the over-hyped bluster of failed/underwhelming enterprise tech (blockchain, metaverse). In past technology surges that actually mattered to culture and commerce (web, mobile), the enterprise was unquestionably a laggard. But in AI, the enterprise may lag at times, but the good ol' risk-managed enterprise mentality is exactly what good AI projects need. To that end, I single out three crucial enterprise AI advances:

• Context at the time ...