Olivia: Jackson, I've got a number for you: six trillion dollars. Jackson: Okay, is that the US national debt or Elon Musk's coffee budget for the next fiscal year? Olivia: You're not far off on the scale. It's the potential annual value that AI could create just for marketing and sales, according to McKinsey. Trillion with a 'T'. Yet, get this, a recent report from the authors' institute showed that 70% of marketers feel they lack the education and training to even use it. Jackson: Whoa. That sounds like a massive, terrifying opportunity. A six-trillion-dollar party, and most of the people with invitations don't know the address. Olivia: That is the perfect analogy. And it’s the exact gap that Paul Roetzer and Mike Kaput tackle in their book, Marketing Artificial Intelligence. Jackson: Right, these are the guys from the Marketing AI Institute. They're not just theorists; they're in the trenches teaching this stuff. Their whole mission is to make this less scary and more practical. Olivia: Exactly. And they wrote the book to be an accessible roadmap. It's not sci-fi; it's a business playbook. It’s been widely praised for making this incredibly complex topic feel manageable, which is a feat in itself. The first step, they argue, is to stop thinking about AI as some futuristic robot that's coming for your job. Jackson: I'm glad to hear that, because my mental image is still basically the Terminator, but with better ad copy. Olivia: Well, let's fix that. Where do you think you used AI this morning? Jackson: I didn't. I made coffee, I checked my email, I drove here. It was a very analog, human morning. Olivia: I think you used it at least three times.

Jackson: No way. Where? Olivia: Did you use Google Maps or Waze to check traffic on your drive? Jackson: Of course. Olivia: That’s AI. It’s analyzing real-time data from thousands of cars to predict the fastest route. Did you scroll through Netflix or Spotify last night? Jackson: Guilty. Olivia: Their recommendation engines are pure AI, learning your tastes to predict what you’ll want next. And when you checked your email, did Gmail try to finish your sentences for you? Jackson: All the time. It’s creepy and also incredibly helpful. Olivia: That’s AI, too. The book makes this brilliant point right at the start: AI isn't some far-off concept. It's already here, woven invisibly into the tools we use every single day. It’s become ubiquitous. Jackson: Huh. So it's less like a robot and more like a really, really smart assistant who’s already working for me and I haven't been paying him. Olivia: Exactly. The book uses a great definition from Demis Hassabis, the CEO of DeepMind, who calls AI simply "the science of making machines smart." That's it. And for marketing, it’s the science of making marketing smart. Jackson: Okay, that I can get my head around. But "smart" can mean a lot of things. How does the book break that down? Is it all just one big brain? Olivia: Not at all. The authors categorize it into three broad buckets, which makes it super easy to understand. The first is Language AI. This is anything to do with understanding and generating words. Think chatbots, automatic transcription like Otter.ai, or even AI that can write a blog post. Jackson: Okay, so that’s the "talking" AI. Olivia: Precisely. The second is Vision AI. This is about machines understanding images and videos. Your phone unlocking with your face is a perfect example. In marketing, it could be an AI that analyzes a thousand social media images to see which ones get the most engagement. Jackson: The "seeing" AI. Got it. What's the third? Olivia: This is the big one: Prediction AI. This is the ability to look at historical data and predict a future outcome. It’s the engine behind Netflix's recommendations and Amazon's "customers who bought this also bought..." feature. It’s about finding patterns we can’t see and making an educated guess. Jackson: So, talking, seeing, and guessing. That’s a lot less intimidating than world-dominating superintelligence. Olivia: And that’s the core message. One of the most important quotes in the book is, "AI is not going to replace you. Rather, it will replace specific tasks and augment what you are capable of doing." It’s a tool. A powerful one, but still just a tool. Jackson: I like the sound of "augment." It implies I'm still in charge. Okay, I get that it's not Skynet. But how does this actually make a company money? The leap from my Spotify playlist to a six-trillion-dollar industry feels huge. Give me a concrete example.

Olivia: I have the perfect story for you, right from the book. It’s about an Australian entrepreneur named Naomi Simson, who founded an experiential gifts company called RedBalloon. She was, in her own words, being "held to ransom" by her digital ad agencies. Jackson: I think every small business owner knows that feeling. You're pouring money into a black box and hoping something good comes out. Olivia: Exactly. She was spending $45,000 a month on retainers and paying $50 or more for every single customer acquisition. It was completely unsustainable. She was desperate. So, she started researching AI solutions and decided to pilot a platform called Albert. Jackson: Albert the AI. Sounds friendly. What did Albert do? Olivia: On day one, Albert took a single Google text ad and autonomously created and tested 6,500 variations of it. Jackson: Wait, sixty-five hundred? In one day? A human team couldn't do that in a year. Olivia: That's the power of AI. It began optimizing all of RedBalloon's campaigns on Facebook and Google, learning in real-time what worked and what didn't—which headline, which image, which audience, which time of day. The results were staggering. Simson fired her ad agencies and went all-in with Albert. Jackson: Okay, don't leave me hanging. What were the numbers? Olivia: Her return on ad spend, which was barely breaking even before, jumped to 500%. After a few months of learning, it was averaging 1,100%. On some campaigns, she said it hit 3,000%. Jackson: That's not a real number. That sounds like a typo. An eleven-hundred percent return? How is that even possible? Olivia: Because the machine could process data and run experiments at a scale and speed that is physically impossible for humans. But here's the most interesting part. The AI started finding customer segments the human team had never even considered. It discovered a lucrative market of Australian expatriates in the US and UK who wanted to buy experiential gifts for their families back home. Simson said, "I found markets... that I didn’t even know I had." Jackson: Wow. So the AI wasn't just doing the old job better, it was finding entirely new jobs to do. That’s a paradigm shift. But for someone listening who doesn't have the budget to hire 'Albert,' how do they even start thinking this way? It still feels a bit abstract. Olivia: That's where the book's frameworks come in. The authors introduce something called the "5Ps of Marketing AI" to make it actionable. It’s a way to organize your thinking. The Ps are: Planning, Production, Personalization, Promotion, and Performance. Jackson: Okay, walk me through that. Olivia: Planning is about using AI to decide what to do—like predicting which keywords will be most valuable for your SEO strategy. Production is using AI to create things—like automatically generating social media posts from a long article. Personalization is tailoring the experience—like Netflix recommendations. Promotion is about finding the right audience—like the RedBalloon story. And Performance is about using AI to analyze your results and tell you what to do next. Jackson: That’s actually a really helpful way to break it down. It turns "let's use AI" from a vague goal into a series of specific questions you can ask for each part of your marketing. Olivia: Exactly. It’s a playbook. It helps you identify the small, repetitive, data-driven tasks where AI can give you the most leverage first. Jackson: This is great for optimizing ads and content, but what about the human stuff? The things we marketers pride ourselves on, like creativity, strategy, and judgment? And, more importantly, what happens when this incredible power goes wrong?

Olivia: That is the million-dollar—or maybe six-trillion-dollar—question, and the book dives right into it. Let's start with creativity. Can a machine, a collection of algorithms, truly be creative? The book uses the story of AlphaGo to explore this. Jackson: Ah, the Google AI that beat the world's best Go player. I remember that. Olivia: It was a huge moment. In 2016, AlphaGo played a five-game match against Lee Sedol, the world champion. In the second game, the AI made a move—forever known as "Move 37"—that was so unexpected, so alien, that it completely baffled all the human experts watching. The commentators were stunned into silence. They thought it was a mistake. Jackson: What was so special about it? Olivia: Go is a game of intuition and pattern recognition built over thousands of years. This move was just... not on the map of human strategy. The AI had calculated there was a one-in-ten-thousand chance a human would ever make that move. But based on the millions of games it had played against itself, it determined this was the optimal path to victory. Lee Sedol, the champion, was so shocked he left the room for 15 minutes to compose himself. Jackson: Wow. So the machine came up with something truly new, something outside the realm of human experience? Olivia: Yes. And after the game, which AlphaGo won, Lee Sedol himself said, "I thought AlphaGo was based on probability calculation and that it was merely a machine. But when I saw this move, I changed my mind. Surely, AlphaGo is creative." The machine taught the master a new way to see the game. Jackson: That gives me chills. It’s not replacing creativity; it’s expanding the definition of what’s possible. But that's a machine playing a board game. When you apply that power to people's lives, there's a dark side. Olivia: A very dark side. And the book presents a powerful cautionary tale: the Apple Card launch in 2019. Jackson: I remember the headlines, but I forget the details. Olivia: A tech entrepreneur, David Heinemeier Hansson, tweeted that he and his wife applied for the new Apple Card. They filed joint tax returns and lived in a community-property state. Yet the algorithm offered him twenty times the credit limit of his wife. Jackson: Twenty times? That’s not a small discrepancy. That’s blatant. Olivia: It was a PR nightmare. When he contacted Apple support, they were helpless. They said, "We don't know, it's just the algorithm." Even Apple's co-founder, Steve Wozniak, chimed in saying the same thing happened to him and his wife. Hansson’s conclusion was scathing. He said Apple had "handed the customer experience and their reputation as an inclusive organization over to a biased, sexist algorithm it does not understand, cannot reason with, and is unable to control." Jackson: That is terrifying. The machine just inherits our worst biases from the data it's trained on and then scales them with brutal efficiency. And the humans who built it just shrug and say it's out of their hands. Olivia: And that is the critical point the book makes in its final chapters. AI is a tool of immense power. It can find new markets and create beautiful, unexpected strategies. But if you're not intentional, it can also codify and amplify sexism, racism, and other societal harms. The authors argue that every organization using AI needs a formal AI ethics policy. Jackson: It's not optional. It has to be part of the foundation. Olivia: Exactly. The ultimate vision of the book is not a world run by machines. It’s a world where machines handle the repetitive, data-intensive work, freeing up humans to focus on what we do best: strategy, empathy, ethics, and building real relationships. The goal of AI isn't to make marketing less human; it's to create the space to make it more human.

Olivia: When you pull it all together, the book paints a really clear picture. First, AI is not a monster under the bed; it's a practical set of tools that we're already using. Second, when applied correctly, those tools offer almost unbelievable leverage to optimize what we do. And third, that power comes with immense responsibility. Human oversight isn't just a nice-to-have; it's a non-negotiable necessity. Jackson: That makes so much sense. The message isn't 'learn to code' or 'become a data scientist.' It's 'learn what AI is capable of' so you can ask the right questions, both of the technology and of your own team. You need to be an intelligent manager of these smart tools. Olivia: Perfectly put. And the book suggests a very simple first step for anyone feeling overwhelmed. Don't try to boil the ocean. Just find one thing in your work week that is data-driven, repetitive, and time-intensive. Jackson: Like compiling that weekly analytics report that everyone glances at for three seconds. Olivia: Exactly. And just ask the question: "Could a machine do this smarter?" That's the starting point. That's the first step on the path to augmenting your own capabilities. Jackson: I love that. It’s not about a massive, top-down AI transformation. It’s about finding one small thing and making it smarter. Olivia: And that's how the revolution happens. Not with a bang, but with a thousand small, smart automations. We'd love to hear from our listeners: what's an "everyday AI" you use that you never thought of as AI before today? Let us know on our social channels. We're curious to see what you uncover. Jackson: This is Aibrary, signing off.