The New Era of AI Product Marketing: 3 Essential Principles

The field of science is undergoing a monumental transformation, particularly with the advent of AI. From how we build products to how we market them, AI has revolutionized every stage of the product lifecycle. However, selling AI products is fundamentally different from selling traditional SaaS products. In this article, I’ll walk you through three key principles that you absolutely need to understand to effectively communicate the value of your AI product.

By implementing just one of these principles, you can revamp your go-to-market strategy and accelerate your AI product's growth.

Principle 1: Position Your AI Product as a Co-pilot

One of the most common and successful ways to position AI products, especially in SaaS, is as a “co-pilot.” Think of it as the Iron Man suit for your average user. AI features can empower users to accomplish tasks they couldn't manage before, essentially giving them "superpowers."

This concept works well because, at present, business owners and buyers are hesitant to completely hand over crucial processes to AI-driven systems or automations. However, they are more comfortable with the idea of AI serving as a "co-pilot"—a trusted assistant that augments their abilities rather than replacing them outright.

If you position your AI product as a co-pilot that enhances the user's capabilities and drives specific outcomes, buyers are more likely to trust and invest in your solution.

Principle 2: Present Your AI as a Prediction Engine

The second positioning strategy is to market your AI product as a prediction engine—what I like to call the "Oracle play." This is particularly valuable for industries like manufacturing, where accurate forecasting is critical. For example, companies need to predict costs, supply chain risks, or production outcomes.

By offering your AI product as a prediction engine, you can tap into a company’s existing data and use your models to provide insights that enhance their decision-making process. Importantly, this method allows businesses to test the predictions without overhauling their entire workflow, making it a relatively low-risk proposition.

However, there’s a caveat: AI products offering predictions need to show their work. Buyers want transparency. Your product must explain how it arrives at its predictions, including the data inputs, weightings, and factors that influence the outcome. It's not enough to simply say "yes" or "no" or predict an up or down trend. You need to clearly articulate how the prediction was made.

Principle 3: Automate Subsets of Workflows

The third principle is positioning your AI product as an automation engine. This doesn't mean replacing entire human roles—at least not yet. Instead, AI can be deployed to automate specific, well-defined workflows.

A great example of this can be found in customer service. Automated agents now handle entire customer inquiries before they even reach a human agent. This kind of AI integration can significantly enhance operational efficiency without completely removing human oversight.

There is a distinct pecking order emerging in the world of AI product pricing. Co-pilot features tend to be the most affordable, with products like GitHub Co-pilot priced at around $9 per month. Prediction engines typically command higher prices due to the substantial value they provide, while automation engines, which can replace human tasks in specific workflows, often charge the most.

Positioning Your AI Product for Success

In today’s rapidly evolving AI landscape, simply calling your product “AI-powered” isn’t enough to stand out. Buyers are becoming more discerning, and you need to clearly communicate how your AI solution fits into one of these categories—co-pilot, prediction engine, or automation engine.

These are the keywords that buyers are becoming trained to understand. By using these terms, you can clearly convey the tangible benefits your AI product offers, rather than leaving customers guessing about what "AI-powered" really means.

Implementing a Go-to-Market Strategy

Now that we’ve discussed the three key ways to position your AI product, the next challenge is crafting a go-to-market (GTM) strategy that brings your message to life. Many aspects of a successful GTM strategy remain unchanged, but there are nuances when it comes to AI products, especially in how you communicate their value to a market that is still cautious about adopting AI.

The first step is building out your Ideal Customer Profile (ICP). This will define exactly who your product is for and why it’s relevant to them. Once you have a clear ICP, you can start positioning your product as a co-pilot, prediction engine, or automation engine, and craft your strategic narrative—what I call your "manifesto."

Your manifesto should explain the transformation your AI product will bring to a business. No one wakes up wanting more software, and today, no one simply wants to add AI without a clear benefit. What customers want is a solution to their problems, and your manifesto must communicate how your AI product delivers that solution.

Finally, the third step is executing what I like to call your "Broadway Show"—a consistent set of marketing and sales activities that educate your target customers about the transformation your product offers and ultimately drive them to purchase.

Conclusion

Positioning and selling AI products requires a unique approach, but by leveraging these three principles—co-pilot, prediction engine, and automation engine—you can differentiate your product and craft a compelling narrative in an increasingly crowded space.