Introducing Maturity Maps — A New Way to Measure AI Adoption

Today, I am discussing a new way to think about AI and agent readiness inside your company, and it is called maturity maps. The AI Daily Brief is a daily podcast and video about the most important news and discussions in AI. Alright, friends. Quick announcements before we dive in. First of all, thank you to today's sponsors, KPMG, robots and pencils in Blitsy. To get an ad free version of the show, go to patreon.com/aidailybrief, or you can subscribe directly on Apple Podcasts. To learn more about sponsoring the show or really find out anything else about the show, like where we are with our agent madness bracket, which is going on right now, head on over to a idailybrief.ai. Today is the second day in our build week, which is happening while I'm traveling with my family. Yesterday, we did a very high level overview around everything that had happened last quarter and what it meant for this quarter, and if we put this in the framework of this being build week, yesterday was sort of the context setting, the environment in which your building is happening. Today's episode takes that down a level to discuss the benchmarks that show where others like you are. Now one of the things that I've been thinking about a lot over the last six months or so is just how much we need a totally different set of data and benchmarks for this new AI era. Everyone is adapting incredibly quickly right now or at least they're trying to. It's new processes, new workflows, new tooling, new everything. And by and large, we're doing all that exploration without a map. Let me give you a practical example of where I think our lack of benchmarks could actually very significantly and meaningfully negatively impact a company when it comes to their AI adoption. Let's say that you are an early adopter company. Across your different functions, you've had really strong hands on efforts to get your AI up and running. In the absence of knowing exactly what to measure, you're just trying to measure whatever you can, and early results are pretty positive. For example, in the marketing function, you have increased your content output 30% year over year without any sort of proportional increase in the resources it takes to produce that content. Now that 30% year over year growth sounds great, but what if I told you that all of your competitors had actually grown their content output by 50%? In AI world, this is actually not a far fetched scenario, and it shows how the need for better benchmarks and numbers is not just a vanity exercise. When we don't know how we're doing relative to peers and competitors, it makes it really hard for us to judge what we need to change, what we need to shift, and what we need to do next. Now at AIDB and at Superintelligent, which is my enterprise AI planning and strategy …