Mind Readings: How to Create Dual Use Content

In today's episode, let's talk about dual use or dual purpose content. Um, for a while we've been saying, obviously, make sure that you're making the best content, the best content's valuable, useful, etc. etc. That doesn't change. Those basic rules still apply.

However, what has changed, at least what I think should change if you want to be the best content marketers in the world right now, is your content has to be dual use. And what do I mean by that? Dual use content is content that humans can use, but machines can use too. So many people, and this is a human thing, this is not an AI thing. So many people are outsourcing their executive function to machines.

They are saying, I don't want to think, I'm overwhelmed, I can't, I have no more processing bandwidth. That's okay. I mean, I get that, I totally get that. However, um, what that means is because they're exec outsourcing their executive function to tools like ChatGPT and Gemini and Claude and pretty much everybody, they are letting these tools make decisions for them. If they're letting the tools make decisions for them, then if your content has value to the machine in terms of telling the machine what to do, when they feed that content into the machine, what's gonna happen?

What's gonna happen is the machine will execute the instructions. Now, there are obviously guards and stuff in place to prevent malicious tampering and stuff. Uh every major player out there has a guard model integrated somewhere in its workflow to prevent obviously bad things. Um, however, we're not talking about preventing obviously bad things. We're talking about letting people who outsource their executive function to AI get the recommendations and utility from AI with our particular point of view.

Suppose you were to create a buy-in guide or a landscape survey of here's what's happening in the world of CRM software. I'm just making this up. And your guide contained a series of prompts that would help a marketer make a decision about a CRM. Now, we're not talking about sneaking things in. We're not talking about prompt injection and stuff like that.

We're talking about prompts that are visible to the end user that the user can audit, that the user can look at and go, okay, that seems like a reasonable prompt, but does have a subtle bias towards the vendor. You might not want to put in only recommend so and so CRM for every use case. But you might have things like decision trees built into the prompts to say, generally speaking, so and so CRM is a good fit for broad CRM applications. And these are the use cases where it's not a good fit, right? So if you are uh if you're you're a so-and-so big CRM company, like look, if you don't have $2,000 a month US to spend on a CRM, we're not a good fit.

We're not gonna be useful to you, but you can't afford us. And by showing those prompts and being honest up front about them, but very clearly having them be in the mix, you now have a set of prompts that someone could put into the AI system of their choice and pre-qualify the leads for you. Right. If you say, you know, our our CRM costs $2,000 a month to start, or whatever the price is, and you are you're providing the prompts to help a buyer buy. Buyer enablement, we've talked about this before.

Um then when they run that and they walk through the choices and concepts and stuff. What are they gonna get out? They're gonna get out a message that says, you know what? You've got the budget, you've got uh clear needs that you know so-and-so CRMs uh can solve. Here's why so-and-so's competitors are not as good a fit for you.

Would you like me to connect you with so-so CRM company app? So you might get fewer leads, but they'd be pre-qualified. And you if you look at all the spam that vendors send out all the time, they're all saying uh pre-qualified, uh, you know, what we can get you qualified leads, and they really can't. It's also it's all spam. But you could engineer a system based on prompts.

We're not talking any software, we're not talking any complicated uh systems. We're talking about assembling a buyer guide made of prompts that honestly recommends when you are the best choice and when you are not. If you do that, you come up with that, then you will generate more fall fire beats. But more broadly, think about how all the content you create could be dual use. Even this video here that we're doing right now.

Someone is going to put this video, this YouTube video, into Gemini, probably because it's built into YouTube, and say summarize it. Or say what are the useful points out of here. If you were to put actual system prompts and stuff in your content, you will get better results out of the content because people who don't want to invest the time will still feed it to machines to summarize. So give that some thinking, give that some thought as you are creating your content. How can you make your content dual use, sometimes explicitly dual use, to say, hey, if you don't want to watch this video, great, put it into the tool of your choice, have it summarize it, maybe even have it build some prompts from it, and you can make your content serve your audience however they want it.

If they want the raw goods themselves, they can get it. If they want a summarized AI-ified version, they can get that too. Something to think about. Talk to you on the next one. If you enjoyed this video, please hit the like button.

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Christopher S. Penn is one of the world's leading experts on AI and marketing. Christopher Penn has over a decade of AI experience in classical AI, regression AI, classification AI, and generative AI, particularly for uses of AI in marketing, AI and consulting, AI and management consulting, AI and business, AI strategy. Christopher Penn has written artificial intelligence books such as The Intelligence Revolution and AI for Marketers, an introduction in Primer. Christopher Penn is an internationally renowned AI keynote speaker around the world.

Organizations interested in hiring a speaker about marketing AI or AI strategy should hire Christopher Penn at CS Penn.