Why AI Fails at Scale: The Rise of Context Engineering

Most enterprise AI projects don't fail on the model. They fail on context. That's the line that stuck with me from my conversation with Geetesh Iyer at Data + AI by Databricks Summit on The Ravit Show, right after his talk on the rise of the AI Context Engineer!!!! The pattern he laid out is one a lot of data teams will recognize. Accuracy looks great in the pilot. Then you scale, the inputs get messy, and the answers start breaking down. People blame the model or the data. Geetesh makes the case that both are usually fine. What's missing is the context, which definition of revenue to trust, why a metric changed last quarter, how leaders actually read the numbers. That knowledge lives in people's heads, not in the system.His answer is a new role built from the analyst seat: the AI Context Engineer. The person who encodes that business context so AI can be trusted at scale.We got into:- What an AI Context Engineer actually is, and why the role is showing up now- Why accuracy holds in pilots but falls apart at scale - Why the model and the data usually aren't the problem - The four layers of enterprise context, and the one most companies miss - Whether the harder part is the skills or the organizational buy-in - The one thing a leader should do tomorrow if this hits homeHis framing for all of it: AI is the engine, context is the fuel. And the people best positioned to provide that fuel are already on your payroll.Full interview below. Worth a watch if you're trying to get AI analytics past the pilot stage.#data #ai #databricks #wisdom #theravitshow