This One Chart Exposes Why Most Companies Are Failing At AI

0:00On today's show, we're going to show you the single most important chart in AI that explains why your company isn't going to be a winner just because you use the best models. You heard that right. The best AI models are not going to dictate who wins and who loses in AI. We're going to give you the actual winning formula and how you can implement it at your company today, all of that and more on this episode of Marketing Against the Green. Here's a quick word from HubSpot. HubSpot helped Tumblr solve a big problem. They needed to move fast to produce trending content, but their marketing team was stuck waiting on engineers to code every single email campaign. Now they use HubSpot's customer platform to email real-time trending content to millions of users in just seconds. The impact, three times more engagement, double the content creation. What a move faster like Tumblr, visit HubSpot.com.

1:02All right, Kip, we are here with yet another AI model, GPT 5.4 Azag. Another model beats lots of benchmarks, apparently one of the best models on the planet. And we're kind of here to argue in this 12 to 15 minute video that it doesn't even matter. That this model does not even matter, doesn't matter how good it is. Model capabilities are not the important thing right now in the AI industry. Yeah, we're going to instead tell you what the most important thing, instead of the model getting better, what that is and how you can leverage that for your business to actually change growth in this new era. And here, based on that, there's a chart that's going viral from Anthropic that I think was the lightning rod moment for the conversation we wanted to have. And a lot of people are interpreting it one way. We have like a very different interpretation of it. So GPT 5.4, great code and model, great intelligence model, beaten and tropic and lots of benchmarks.

2:05What we've been here before, every single model that comes out kind of goes a little bit more up the benchmarks that there are ways that they kind of align themselves to benchmark to do really well. So we are believers that model capabilities are already very, very good and actually given people even more capable models, it's not going to make much of a difference right now because of this chart. So this chart is being shared pretty widely around X and it's from Anthropic. So if I play to Anthropic, they're putting at these to try to show what the impact of AI could be across industries. So what it's showing you is theoretical AI coverage, which means how much of that industry could be theoretically automated with AI. And there are no surprises here. AI is really good at code in and math. It's very good at finance, it's very good at engineering type roles. It's very good at legal, it's very good at like arts and media, pretty great at all of the office and admin work. So it can get you a lot of coverage in a lot of different industries.

3:06And the red is the observed AI coverage. That's how much AI is being deployed within this industries and how much of that work it is automated. And I think what you and I talked about on Slack is we don't believe that for the average AI user, the model is already good enough. It really is not going to matter this year if they get another model, another model, another model. The really hard thing about AI is actually integrating it into your existing workflows. I think one important thing to understand is I think everybody is looking at this chart and they're looking at this blue area, which is theoretical, right? And they're like, oh gosh, AI is going to wipe out a lot of the jobs in all these categories. And like that's very theoretical, might happen, might happen, a year from now might happen a decade from now. We don't know. What's the most interesting, I think, what you and I are interpreting this differently is that there's this massive gap between the red and the blue and there's some work that's happening and coding and in business process and in sales and some of these customer

4:09service, these really important markets. But it's like still small relative to what the perceived opportunity is and that blue is really kind of the perceived opportunity. So if we all watch in the show today, agree that like, oh, there's some theoretical much bigger opportunity versus where we are now. Like what the heck does that mean is the big question. We just dropped the AI adoption playbook. It gives you the exact framework to actually redesign your business with AI. It's a proven roadmap that helped one company book 11,000 meetings and another to resolve issues 39% faster. Get it for free. Click the link in the description. So there's a good story that this has happened, you know, once before in time. Oh yeah. I was giving you a good quote from a dinner I was at and I'm with a bunch of founders and one of the founders in there is incredible and you have this incredible quote where he said you cannot walk into the future if you're looking back at the past. And I think this story I'm going to go through kind of explains what's happening, which

5:10AI has been compared a lot to electricity, right? It's like a fundamental thing that everyone is going to access and get a lot of value from. And Thomas Edison built electricity generating stations in Manhattan and London in 1881. And when in the year electricity was being sold as a commodity, so we had electricity as a commodity in 1881, this incredible new invention. But by 1900 less than 5% of mechanical drive power in American factories came from electric motors. And so companies had not integrated into how they were doing work. And if you looked at that, it's because those factories kept their old layouts. They just swapped steam for electricity and then ran the same processes. So think about that in terms of AI. The productivity explosion of electricity when in factories only happened when they redesigned their factory floor around electricity, right? So they started by stepping into the future with electricity by looking back at the past the same old processes. I'll keep the same processes.

6:11I will swap steam for electricity and I'll just do that thing. And there was only 5% adoption. It got wide adoption when all the factories, when they redesigned their exact factories around AI. That I think is going to be the fundamental shift that happens in companies, not AI model of capabilities, but redesigning the company to be an AI native company, team structure or skill sets, how people do work. And that's going to take way more time than it takes for these companies to come out with another model, another model. There's a cool thing you can do now. Go to Proplexi, go to Chatsby T, go to any one of these models and ask it to build a table of open jobs in Google DeepMind, Claude, Antropic and OpenAI and tell it to show you who they're hiring for. Because if you ask who they're hiring for, it tells you a lot about their strategy and I do this all the time. They are hiring for Ford deployed engineers, right?

7:12People who can help companies integrate it into that business but not swap steam for electricity but how you start to redesign your business around these models. I think this is honestly one of the most important points we've ever made on the show, is that the bottleneck is humans. That human bottleneck is going to take, I don't know, Karen, what do you think decades to fully get through? I think it depends if we get agents that get deployed as employees and can actually do a bunch of that work internally. But for the average customer, you and I have talked about this, there's like, we go to dinners with AI founders and it's a different planet. Everyone's in there, Claude, MD file, trying to get my open-claw agent to run a bunch of my stuff. Then there's a category who are kind of doing some stuff with AI and they're like, hey, I'm using Chatsby T on a day-by-day basis asking it some questions, maybe getting it to write an email and then there's a group of people that are like, what the fuck are you

8:13all talking about? That's the majority of humans by the way. That's the majority. There was a study of... That's the majority. Yeah, there was a study recently, maybe from OpenAI or another one of these companies where like normal people, just people surveyed, 84% had never used AI, right? That's how early we are. If you look at some of the data, only 8.6% of companies have even deployed an AI agent in production, right? There is such a small amount of usage. And OpenAI had a state of enterprise AI report, actually, that was pretty interesting, where it just showed the top 5% in terms of intensity, in terms of usage, where orders of magnitude weigh ahead to everyone else. Like the gap between the top percentile in everyone else has never been greater. Well, one, it shows you that the single biggest opportunity on this chart is actually the gap between the red and the blue. Right. If you can help any company transform and become AI native, then there's a lot of opportunity and a lot of money to be made there because, like we said, the models are smart.

9:14They can do all of these things. Before we continue, let me tell you about a podcast I love, Marketing School. Marketing Legends, Neil Patel and Eric Sue bring you daily, actionable digital marketing lessons learned from years in the trenches. Marketing school delivers bite-sized marketing wisdom you can implement immediately. Whether you have a new website or you're an established business, you'll learn the latest in SEO, content marketing, social media, email marketing, conversion optimization, and general online marketing strategies that work today. They just did a great episode called 10 SEO lessons that still work in the AI era. Listen to Marketing School wherever you get your podcasts. I think what's going on here is a few things. One, first of all, all the cost of these models is greatly subsidized right now. And if you compared the actual cost to get from red to blue, in terms of actual costs versus a subsidized cost, it would be pretty expensive, right?

10:18Like the amount of compute that you're paying for, inference that you're paying for to close that gap, it's going to be a lot. It's not going to be tens of dollars or hundreds of dollars, it's going to be tens of thousands of dollars, right? Yeah, I agree with that. Okay, but we are not the episode just to give you the opinions, just to give you, here's what we think. We want to give you the product calories. Can you have been working on a skill that can actually help companies close that gap? Okay. So I'm here in Proplexity Computer, which is one of many AI tools. It's kind of a consequential to what we're using here. And I had it to a task. I gave it a very complex prompt. It ran for well over an hour. And this is my V1. I'm going to edit through this and I'll give you the skill up into the day. But what I had to do is basically, can it build a skill that you could use in any AI tool, chat, GPT, Claude, Proplexity, Claude Code, what have you? Any tool to actually help you take your team, yourself, your team, your company from being

11:23that factory that isn't set up for electricity to your metaphor, Karen, to being an AI native organization to actually understand what it would take, okay? So Karen, what I really had to do was go and research current best practices in AI transformation, look at all the current frameworks, rethink them, and actually make a new framework for us and a new skill that's going to help people take the route. So here's what it did. Researched 20 existing frameworks, it did a bunch of real-world case study analysis, it did a failure analysis and current best practices from McKinsey and HBR and Wharton and all of the fancy folks. Here's the framework, Karen, I want your opinion on this. The new framework is called Rapid 5 and R is reveal. Assess the team's actual workflows, maturity, and map the jagged frontier of where AI helps versus hurts their specific work. A, architect design, the target AI native operating model with workflow by workflow before

12:25and after designs, technology selection and change management. He proved implement through two-week sprints on real-world pilots, not synthetic, measured across three horizons, efficiency, capability, and transformation. In grain, shift from tool adoption to identity shift through peer learning, AI first defaults and performance integration. Indeed, dynamize, build a 90-day reassessment cycles because AI capabilities change quickly. All right, what do you think about the framework here? Yeah, so that's changes quarterly and that's important because it actually understands that things are changing in a fluid way. This is really good. What you're really doing and what we're showing folks here, and I think if you really want us to go deeper on this specific use case, you're building a four-deployed AI skill. Four-deploy engineers, so people are listeners understand, they are going into companies, they are working for OpenAI, Claude, working for these different AI products, and they go in and they implement AI into your business.

13:26I've talked with a lot of AI founders. Their biggest problem is that companies are struggling to integrate AI and agents into their workflows, which is what that chart is saying. They hire the specialists who go in and do that for you. What you're actually building is a skill for the average business to be able to do four-deploy AI engineering within their own company. This here is a pretty good framework. What is the inputs to run this skill? The skill requires essential team profile, core workflows, 5 to 10, current AI state and transformation goals. That's the bare minimum. Important data systems environment, leadership culture context, constraints risks, and then helpful competitor benchmarks, individual skills inventory, and prior performance data. A lot of this is going to be tricky to get. Actually, I think to take away for folks here is if you want to close a gap between the red and the blue for your business because you think there's value in integrating AI across a certain team, that you can actually ask AI to actually start to build a skill that

14:30will allow you to give it some inputter on that team. I think to your point, one of the suggestions would be have your team just do a bunch of looms of how they work and then take those transcripts and then give it to your skill. This skill extrapolates that using some sort of framework like you have here and then creates the ways that AI can automate those things. Who will show that in practice? Maybe we'll look through an episode where we show. Let's do a part two of this where we'll really do the screen share, we'll try to simplify the inputs, and you and I will just keep iterating on the skill, like offline and part of that episode until we get it really, really tight, and then we'll give it to everybody. So I'm sure there's some people who want this skill now. We will give the skill away once we think is really good. We want to go through a bunch of fictional examples, and then we want to do a couple real-world examples, and then we'll know it's pretty good. Cool. Okay, that's the episode of Opportunity. You might be ahead of your competitors, but you're probably behind the market opportunity. Big market opportunity. I think it's funny that Anthropic put that chart out.

15:31There's a bunch you could talk about it, but I think the most interesting thing is that there's a real gap for a lot of businesses from where they are to where they could be. Appreciate you watching, hit like, hit subscribe. We'll see you real soon. I'm marketing Instagram. Hey, everyone, you know, Karen and I have been doing the podcast for a while now. We've been at this for a couple years. We love it. We could not be happier to be doing this, but we wanted to take things to the next level. We want to level up the impact we're having with marketing Instagram. So the next step of our journey is something we're really, really excited about. We're going to launch the marketing against the grain newsletter. And marketing against the grain newsletter is going to be amazing. If you are a marketing leader practitioner, you're in the trenches doing marketing every day.

16:31This is for you. We're going to deliver right to your email inbox, and you're going to get all the behind-the-scenes, frameworks, practices, tutorials from us, from guess we have on the show, and from people who even beyond the podcast that we think are going to be helpful and really have an impact on your day-to-day week-to-week doing marketing. You're going to love it. It is something we've been talking about for a while. We're really excited to have it out in the world. We've already got 100,000 marketers who are on this newsletter. Please join. It's completely free. We'd love to have you as part of the marketing against the grain community. And it's easy. You can click the link in the description below, or you can head to marketing against the grain.com slash subscribe.