Nearly everything you've heard about AI and job cuts is wrong - here's why (www.zdnet.com)

The headline claim that AI will eliminate 50% of white‑collar jobs is greatly overstated. Tom Davenport argues that real AI-driven transformation takes enterprise‑level projects, process redesign, data and integration work, and years of sustained effort—not just running prompts against the latest models. Companies that cut entry‑level roles risk starving their talent pipeline; automation often changes job content rather than erases roles outright and may create new, unforeseen jobs. The practical implication for the AI/ML community is that measurable value comes from rethinking workflows, building production systems around models, and investing in change management, monitoring, and data pipelines—not from hype or headline predictions. At the same time, generative AI is democratizing development—“vibe coding” or citizen development—and that’s powerful but risky without governance. Davenport recommends a red/yellow/green framework: block critical systems (payroll, core banking) from citizen-built solutions, allow lower‑risk use cases with strict oversight, and permit safe experimentation. Organizations need a single, accountable tech/data leader to orchestrate strategy, guardrails, and collaboration across CIO/CTO/CAIO roles. For practitioners, this means prioritizing robust governance, model risk controls, secure deployment practices, and engineering around processes rather than isolated prompts to realize real ROI from AI.