Last month a product manager with zero SQL or Python skills used a new AI tool to spin up a live dashboard. It connected to hundreds of data sources, ran queries, and produced client-ready insights with almost no hallucinations. My role as the data validator shrank to a quick accuracy check and a few tweaks. What used to take weeks of heavy lifting now lands in minutes. That experience flipped my world. I’d long believed raw analytics—formulating queries, wrangling data, building visualizations—was safe from automation. Instead, AI is handling those tasks. Now I spend most of my time on data governance, defining schemas, curating training sets and writing guardrails. I oversee permissions, audit logs and ethical guidelines to keep the AI honest. The shift means I’m less a “doer” and more a strategist. I focus on choosing the right metrics, designing experiments, tailoring prompts and interpreting results. I coach teams on asking good questions, spotting bias and trusting the numbers. My value lies in domain expertise, critical thinking and communication—skills that machines still struggle to mimic.