(This is part 3 of a 5-part series of posts attempting to explain the AI landscape in a practical and simple way. See other posts here.)

You probably know that the Wright Brothers invented flying as we know it, but you may not know that it wasn’t supposed to be that way. Neither of them had a high school diploma, and several more highly credentialed people were pursuing flight, some of whom were even funded by the US Government.

But while everyone else was thinking about adding power to gliders or making bigger wings–essentially trying to make souped-up paper airplanes to achieve flight–the Wrights were thinking beyond just getting a plane off the ground. They invented the three-axis control system–pitch, roll, and yaw–which opened the door to controlled, sustained flight. They were thinking about how to make flight useful and did so by ditching the approach of optimizing the known (glider) and instead pursuing the not-yet-known (but possible). 

Most conversations I hear happening in executive circles today are stuck in optimization mode: better chatbots, better segmentation, better personalization, better data analysis. Very few are thinking strategically in a three-axis control kind of way. Not coincidentally, this is yet another manifestation of my belief that functional role experience in a particular function is overrated and stifles fresh perspectives. 

To unlock your creativity to imagine what Generative AI may enable, forget what you know about digital marketing today, and take a first-principles approach based on its capabilities.

1. Generative AI can produce customized content at scale

I expect that conversations about manually defined customer segments will quickly become obsolete, and to a large extent, so will the concept of a marketing calendar.

Within a couple of years, most companies will have their own large language models (think of this as a super smart Intern you hire to create content, who you train according to your core offering, the value you create, your corporate voice, and brand style guide, but who also studies every other great content creator out there for tips). This will connect to data about all of your customers’ actions and preferences, and so this ‘assistant’ will be able to auto-generate contextually relevant content at any time. 

Since this will be ‘always on’ according to everyone’s unique preferences, you won’t need to target ‘segments’ or have arbitrarily planned seasonal campaigns. You won’t need e-mail calendars, because your Gen AI program will make sure people are getting the right messages at the right time (I’ve read about some concepts of video content adapting to viewer reactions in real time). Marketing automation as you understand it today won’t be a thing.

2. Generative AI can not only analyze data but also predict future impact

When my co-founders & I launched Sideview in 2013, potential investors were primarily interested in how our platform leveraged data in a predictive way. With Facebook, for example, data basically worked like this: Jim posts photo surfing in Bali → algorithm recognizes that Jim is interested in Bali and surfing → Jim starts to see ads for surf getaways in Bali. The only problem is that Jim already went to Bali, and since he lives 8 time zones away, he’s probably not going back anytime soon. (Keep in mind that this was 2013; advertising algorithms are a lot more sophisticated now). 

With our platform, it would see Jim’s post in Bali and determine that Jim is willing to travel long distances and seems to enjoy the beach and adventure sports. So we could serve him ads about an activity resort in Fiji, even though Jim had never mentioned Fiji anywhere. It was all about predicting what might be next, as opposed to rehashing what had already happened. 

We all love to talk about our data-driven approach, but it’s worth keeping in mind that all of that data is past. What Generative AI can do is take that data and essentially predict market trends and consumer behaviors that may be on the horizon, which can help you stay one step ahead. More advanced AI applications will be able to not only predict trends but even help you ideate prescriptive strategies and campaigns to position you to take advantage when those predicted trends happen. 

3. Generative AI can help set your road map

As a benefit of Generative AI’s ability to tap into incomprehensible data sets to predict trends, it will play an increasingly central role in how you develop products and validate commercial strategies. This will enable you to pivot faster, thus de-risking any bets you’re placing on the future. 

The other day I met with an agency selling all sorts of AI-enabled solutions, most of which were cute, augmented-reality games (using devices to layer virtual elements over your field of vision). Pretty cool stuff, sure, but do applications like this create sustainable value or are they fleeting novelties? If you’re Nike and you build some cool tech into your app, is it still a differentiator once Under Armour and every other apparel brand has done the same (which they will)?

This is where it’s hard for all of us to understand today what will soon be pretty normal. I can imagine Generative AI being able to simulate customer responses to product designs, features, and go-to-market strategies before you have to commit to any particular direction. Maybe we’ll even see asynchronous product rollouts based on individual user or market segment data–imagine shoe drops only available to certain people at any given time. These make for fun thought experiments, anyway. 

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Of course, I don’t know any of this for sure. My thinking at the moment is just as limited by what I know as everyone else’s is. That’s why I’m trying to learn all I can about the technology itself and the tools it is enabling as a foundation for building from first principles. That’s also core to this writing exercise, because having to explain AI principles gives an appropriate stress test to my understanding of them. 

Next post I’ll talk about how Generative AI will impact your org chart.