A Practical Guide to Scaling AI

Today on the AI Daily Brief, a practical guide to scaling AI. The AI Daily Brief is a daily podcast and video about the most important news and discussions in AI. Alright, friends. Quick notes before we dive in. First of all, thank you to today's sponsors, KPMG, robots and pencils, Blitsy and Robo. To get an ad free version of the show, go to patreon.com/aidailybrief or you can subscribe on Apple Podcasts. Remember for those of you who hate ads, ad free is just $3 a month. And if you are interested on the other end of the spectrum in sponsoring the show, shoot us a note at sponsors@aidailybrief.ai. Welcome back to the AI Daily Brief. Today, we are talking about some practical useful frameworks for scaling AI. In other words, moving beyond the pilot stage. And the specific frame of reference and framework that we're going to be using comes from a new guide from OpenAI called from experiments to deployments, a practical path to scaling AI. But even outside of this particular document, it is very clear to me that a huge theme heading into next year is going to be this idea of whole org transformation and post pilot, post experimentation phase artificial intelligence inside the enterprise. If you look at literally any study of AI adoption and impact, the story is pretty clear. Massive and increasing adoption and usage, initial extremely promising ROI and impact, but some real barriers to converting individual value to whole org value. Indeed, the very first set of charts in McKinsey's state of AI doc shows this really crisply. On the one hand, the percentage of organizations that are using AI in at least one function continues to reach new highs, and increasingly, it is spread across multiple functions. But a huge number of organizations remain in really early stages. McKinsey found 32% still experimenting and another 30% in the piloting stages, meaning just 38% total were scaling or fully scaled, and only 7% of those were in that fully scaled phase. Now to be honest, the 7% in fully scaled, I don't think is all that bad. When you have a technology that is going to impact every single part of the organization, I would actually be surprised if those 7% actually are fully scaled. There's just so much to do to get there. The more concerning piece, especially if you are in that cohort, is the 62% that are still in those really early stages. I genuinely think that if you head into 2026 in those stages, you have to treat yourself as officially behind. One One thing we've recently done at Superintelligent is as part of our agent readiness audits, we were very frequently recommending some version of quick win pilots as part of the initial things to do, especially for organizations that found themselves in the explorer stage, which is very early in their AI journey. I've now basically hard blocked and demanded the removal of …