Noelle RussellChief Executive Officer, AI Leadership Institute · 5x Microsoft Trustworthy AI MVP | 3.4+ million students | #1 Thought Leader in Agentic AI | AI-Powered Revenue Growth + Cost Optimization Expert ✨Best-Selling Author: Scaling Responsible AI | TEDx Sp

AI is Not Here to Replace You it is Here to Work with You #shorts

26 Nov 2025 · 1 min

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In this short, Noelle Russell emphasizes that AI does not act independently; it reflects the data and intentions humans provide. She advises starting AI projects by first understanding the human work and clearly defining the problem to solve. Using classic design‑thinking methods—whiteboarding, post‑its, and brainstorming—teams should map out potential problems and then narrow focus to a single, high‑impact area. Russell stresses the importance of inviting typically excluded stakeholders such as compliance, risk, and legal early in the conversation to surface viable use‑cases and mitigate pitfalls. The overarching message is to avoid “boiling the ocean” by keeping the AI initiative scoped and collaborative rather than attempting an all‑encompassing solution.

Chapters

Like AI is meant to, you know, I always say like AI is not this big AI in the sky that's gonna come and take things from you, right? Everything that AI does, you give it whether intentionally or unintentionally. And so the first thing you need to do is understand the human work. It kind of is aligned with the idea of what's the problem you're trying to solve. But when people say, when I, when I get into that conversation, they're like, ah, everything's all over the place. I'm like, well, what, let's just pick one area. And often we'll do good old-fashioned design thinking, right? Get on a whiteboard and put Post-its on there of like, What are all the problems? The more— I call it a symphony of talent. The more, you know, how— the more people we have in that room from compliance and risk and legal, all the people that usually never get invited to that first conversation, we want all them in there to think about what are the use cases that we should go after first? We— and so, so less about like, how do I solve? I mean, boiling the ocean is a big challenge because AI, that's, that's the promise.