Summary
- We are all starting to use AI in marketing, but many people are using it to create strategy that doesn’t work well.
- AI operates on a normal distribution, a bell curve, and is best at identifying the average or most accepted solution.
- This applies not just to data like height or weight, but also to ideas, strategies, and marketing approaches.
- AI is risk-averse and will not suggest fringe or edge strategies that might be highly effective in a specific context.
- To get those edge strategies, you would already need to know the right questions to ask, which creates a catch-22.
- AI produces average results, which are useful for tasks with clear answers like diagnostics, pattern recognition, grammar, or math.
- AI cannot personalize the best strategy for your unique situation without your own thinking and direction.
- Use AI for tasks where the average is sufficient and the answer already exists.
- For real growth and breakthrough strategy, you must explore, experiment, and think independently rather than relying on average thinking.
All right, I thought I’d pop in real quick here just to talk about AI in marketing.
We’re all starting to leverage AI. We don’t really know how to use it very well. I’ve seen this firsthand, clients of mine using AI to come up with strategy that doesn’t work well. Let me explain why this phenomenon is occurring.
If you look at all my other videos, there is this concept of a normal distribution. There’s this bell curve effect going on where if you take a statistically significant sample size of anything, it could be people, it could be dog breeds, they’re going to generate a normal distribution of certain aspects. In humans, it could be things like a normal distribution of height, a normal distribution of weight, a normal distribution of IQ. If you look at dogs, it could be their size, their hair length, how many pants they do per minute.
This also applies to ideas. When we get a statistically significant sample size of different ideas, different strategies, different approaches to marketing, we also get a normal distribution. You can distribute things from ineffective to completely effective, and here we have the average.
What AI is very good at is figuring out what the average is, what the correct solution is. It is averse to risk. It’s not going to suggest very fringe, pushing-the-edges strategies that might be completely effective for you, but it’s not going to provide that. You won’t know the prompts to ask it to give you this solution unless you know the solution itself.
As a result, when we rely on AI for pushing the boundaries, it won’t happen. What happens is you get average results, which can be very good for certain things. If you’re trying to look at X-rays and get a consistent output of what diagnosis to apply, it’s going to be very good. It’s very good at getting averages, figuring out patterns, and saying, “Okay, this looks just like this.”
If you’re checking for grammar, it’s very good at finding the best grammar. There’s a correct answer. If you ask two plus two, we’ve all agreed that two plus two equals four. There are some people who might tell you that two plus two equals one or five, but the job of AI is to provide the safest, most accepted answer.
It’s never going to be able to personalize the best strategy for you with your context until you figure out the exact correct questions to ask it. So we’re in this catch-22 where, to go forward, we have to know how to get there.
That’s how AI fits into the entire frame of things. Definitely leverage it. Leverage it for things where you need the average, where there is already an answer. But where you’re trying to go, the only way to get there is to figure that out for yourself. Don’t leverage it as much there. Instead, go out, explore, find different ways of thinking, try them out, see if they work for you or not. Don’t leave that up to average thinking. Don’t look at average to find the better solution.
That’s all I have to say today. Thanks for your attention. Have a good Merry Christmas.