The Three Eras of Product Marketing for AI (so far): Setup, Hype, and Trust
I’ve been marketing AI products since 2019, back when we were still doing the awkward handoff between “machine learning” and “AI” and no one really understood the difference. The playbook has changed three times in seven years. Here’s what I’ve learned.
Era 1: The Setup Era (2019–2021)
When I was working on Alexa Routines, customers had to open an app, manually configure triggers and actions, and have at least a basic understanding of smart home ecosystems. It was semi-technical and it required effort. And that was fine, because “AI-powered” was still enough of a differentiator that people would tolerate the friction. The audience was narrow (people already invested in home automation, with a higher technical understanding), but they got it. They understood they were building something, not just turning something on.
Era 2: The Hype Era (2023)
Then ChatGPT hit the mainstage and everything changed. Suddenly AI wasn’t theoretical. It was something people used in their actual lives. And because it was delivering tangibly, marketing couldn’t lean on “AI-powered” as a general buzzword anymore. Every product was AI-powered. The phrase stopped meaning anything. Customers started asking a different question: what does this AI actually do? You had to explain the mechanics, not just slap the label on.
Era 3: The Trust Era (2024–2026)
Now we’re in a different place. Customers have ChatGPT and Claude-level expectations, even if they don’t understand that it’s just a predictive model (they think it’s magic). And they’re worried. Not about whether AI works, but about what it might take away. Will it act without permission? Will it automate the work they actually want to do and leave them with the tedious parts?
This is where you have to thread the needle. When building AI tools for SaaS products, it’s important to be really explicit about what the AI is doing and what the human still controls. AI will make recommendations. It will prompt people to think through things they might not have considered. But at this stage the products I’ve worked on will never act on someone’s behalf without them giving explicit approval first.
The framing I like to use is: take away the toil, not the joy. If the AI is handling the work people actually care about but leaving them with the boring parts, we’ve failed. If it’s handling optimization or grunt work? That’s where it should be.
What This Means for Product Marketers
We’re in an AI-native world now and if you work in tech the assumption is that your product uses AI. Saying “AI-powered” doesn’t differentiate you anymore. And here’s the truth: everyone has access to the same AI tools. When every company uses ChatGPT for copywriting and Claude for strategy, the outputs start to look the same. What breaks through isn’t better AI. It’s clearer positioning about what specific problem you solve that generic AI can’t.
Is it your first-party data? Deep industry knowledge? A specific use case? That’s how you stand out in an AI-saturated market. Be specific about what the AI does and what it doesn’t. Build your messaging to work for both the hands-on user who wants control and the hands-off user who wants it intuitive and done for them. Most importantly, build trust into the message: AI recommends, you decide.
The bar has moved. Customers have used great AI. They know what’s possible. If you’re shipping something that doesn’t reach that level of experience, you at least need to be in the ballpark. And you need to be clear about why your AI is worth using over the general-purpose tools they already have.