A year ago, we had perfect jobs at LinkedIn – fascinating work, great pay, and the stability we needed with young kids and big mortgages.
Then we saw something that made us step away from it all.
What we saw was the convergence of two revolutions: generative AI and marketing science. Over the past year, we’ve been pretty quiet about AI. For what? Because we were too busy using it, every minute of every day. We partner with the most prestigious marketing organizations to discover where AI fails, where it excels, and – most importantly – what tasks it should perform.
Today we would like to share our “Three Laws of Leverage”. These laws separate organizations that use AI to gain strategic advantage from those that simply play with the newest tactical toy.
Think of it as a field guide to the future of marketing.
The conductor’s code: amateurs vs. experts
Imagine yourself sitting in front of a Steinway grand piano. What kind of music will you play? Should we expect Bach’s Goldberg Variations, or the Baguettes? If you only know how to play sticks, is it because the Steinway is broken, or because you have never practiced the piano?
Switch instruments in this AI analogy and you’ll discover one of the big misconceptions at the heart of the AI conversation: the idea that using AI requires no skill or practice.
In fact, the leverage created by the AI is proportional to the skill of its player.
We’ve probably spent over 500 hours practicing AI piano, and we’ve gotten pretty good at it, if we do say so ourselves. But we’re just toddlers compared to our technical director, Brian Watroba, who might as well be Mozart. We can play just one melody; Brian can conduct a symphony. Like a conductor who knows when to call on each section of an orchestra, Brian can orchestrate a wide variety of AI models to play music far beyond our reach.
Different models at different “temperatures” excel at different tasks. Think of them as sections of your AI orchestra.
The conductors mark their score dynamically – from pianissimo (very soft) to fortissimo (very loud). AI experts mark their code by controlling the “temperature” of each model. Temperature determines how creative or curative the results are. Low temperature produces careful, predictable responses. Ask the AI to tell you a bedtime story, and the low-temperature model will say, “Once upon a time there lived a princess in a castle. » If you set the temperature higher, you’ll encourage unexpected jumps, like: “Once upon a time, Cat Stevens flew an avocado to Saturn.” »
Different models at different temperatures excel at different tasks. Think of them as sections of your AI orchestra. Some are like the brass section, powerful in terms of computer performance and logical reasoning. Others are like strings, bringing nuance and artistry to writing and creative tasks. Imagine that you are analyzing the concepts for a new advertising campaign or a new product. The brass section can operate at low temperature (0.2) to calculate the financial value of entry points of the relevant category. You can then turn to your string section, at a higher temperature (0.6), to think about unconventional ways to win in these specific buying situations.
Today, most marketers think that “AI” stands for “ChatGPT.” But ChatGPT is just one model among many, and its temperature is predefined to standardize the output. This is a major limitation. So remember, when someone says “AI can’t do X”, what they’re really saying is “I can’t get AI to do X”.
They mistake an inexperienced musician for a broken piano.
The Picasso prophecy: good answers versus good questions
In 1968, an interviewer asked Pablo Picasso what he thought about computers. “Computers are useless,” he scoffed. “They can only give you answers.”
No offense to Pablo, he did a pretty crappy job anticipating the computer revolution. But he did a fantastic job anticipating the AI revolution. Which brings us to our second law: the leverage created by AI is proportional to the combined skills of the marketer and programmer.
Instead of debating what AI should do, most of us are obsessed with what AI can do. But we’re missing an essential truth: AI is the stupidest there is, and it’s already as smart as many doctors.
The real challenge is not what AI can do – AI can increasingly do anything – but what AI should do. As answers become plentiful, the competitive advantage will belong to marketers who know how to ask smarter questions than their competitors.
Take brand health, for example. We’ve found that synthetic audiences can measure brand awareness with remarkable accuracy: correlations are consistently greater than 0.80. But when we share this data with our customers, we run into a more fundamental problem: awareness isn’t really helpful. AWS is almost 100% known among technology decision makers, but that doesn’t tell their marketing team anything about how to drive growth. What matters is mental availability: Does AWS come to mind when a startup needs to scale quickly, when a company faces security issues, or when a company outgrows its cloud infrastructure current? Pure awareness tells you if people know your brand exists. Mental availability tells you if they will think about AWS when it matters.
Now, without AI, you might be able to do one mental availability study per year, for a single market and a single category. With AI, you can do 100 studies per year, in 20 different categories and 50 different markets. But you must first know how to ask for mental availability rather than awareness, which means that you must have studied the literature on marketing effectiveness.
We constantly think of that scene from The Dark Knight, where the Joker compares himself to a dog chasing a car. “I wouldn’t know what to do with it if I caught it!” AI will make data much easier to capture. But data is only valuable when it drives decisions.
The synthetic strategy: difficult trades vs. easy trades
Would you like a robot to clean your floors or visit the Amalfi Coast for you? Do you prefer AI to write your ad copy or manage your market segmentation?
Our final law: AI gains are proportional to the difficulty of the marketing task.
And no task is more prone to AI-driven disruption than…strategy. That’s right, we’re talking about the art and science of segmentation, targeting and positioning (STP).
Traditional marketing strategy has always been a costly and time-consuming nightmare. Consultants charge hundreds of thousands of dollars for months of painstaking work. You need massive customer samples, in-depth surveys, and complex analyzes to identify market segments. Targeting workshops, competition mapping, endless positioning debates: the process is so unpleasant that most marketers ignore strategy altogether and jump straight into tactics.
If you give AI the hardest tasks – like segmentation, targeting and positioning – the gains can be revolutionary.
After six months and £600,000, BCG will offer you a static strategy. And if your sales teams reject targeting priorities or your positioning becomes outdated, you’ll do what most marketers do: put the 600-slide deck in a drawer and never think about it again.
Now, instead of a human-created strategy, imagine a lab-developed strategy, built by an advanced AI system. The lab-developed version will not only be faster and cheaper to produce – the time and money saved is staggering, but surprisingly, that’s not even the main benefit – the real revolution is transforming the strategic development of ‘an annual event into an ongoing conversation.
With AI, you can quickly test different strategic combinations, experimenting with various segment definitions, target priorities, and territory positioning until you find the most profitable combination of choices. You can then propose these options to internal stakeholders, incorporate their feedback, and generate new variations in real time. And when market conditions change, you can evolve your strategy to stay current, with just a click of a button.
Today, marketers are myopically focused on using AI for simple tasks like writing social media posts. But if you give AI the easiest tasks, the efficiency gains will be limited. If you give AI the hardest tasks – like segmentation, targeting and positioning – the gains can be revolutionary.
The real bubble is about to burst
We love contrarian stances more than anyone.
But “AI is overrated” might be the coldest take in the marketing world. We’ve spent the last year developing lab-grown marketing strategies for top brands. And let us tell you with complete confidence that AI is not overrated. On the contrary, it is underestimated.
The bubble is not in AI – it is in AI denial.
Most marketers treat AI as a copywriting assistant rather than a strategic brain.
This is the real bubble. And it’s about to burst.
If you’re not leveraging AI, consider our key principles: Are you approaching it like a conductor, orchestrating different models for different tasks? Are you using AI to find the wrong data or to make the right decisions? And more importantly, are you deploying AI to tackle marketing’s toughest challenges – like strategy – rather than the easier tasks?
The orchestra is waiting. Are you ready to lead?
Peter Weinberg and Jon Lombardo are the co-founders of Evidenza, an AI company that uses synthetic data to generate finance-friendly marketing plans.
