A SaaStr post from Jason Lemkin highlights an important point every CEO needs to consider: if your AI investments haven’t materially moved revenue then your organization hasn’t created a fully formed AI strategy.
The reality today is that it’s easier for companies to measure their AI ROI on the cost (COGS and OpEx) side of the P&L. In fact, many organizations have rolled out AI-based productivity tools, automated a few workflows, and have quietly (or not so quietly) noted that the same work is getting done with fewer people hours. That’s material learning for sure, but if that’s the whole story, you’re playing defense instead of offense with one of the most powerful tools your business has ever had access to.
CEOs should be prioritizing ways they can spur revenue growth within their AI ROI formula. Cost savings matter, but they’re the second-order benefit, and without top-line growth you’re just relying on AI to find expense reductions. And that approach basically becomes a race to the bottom.
So what can the revenue-first approach look like?
Start with what your customers are telling you they need, and ultimately what they’re most likely willing to pay you for. AI can synthesize customer feedback, support ticket insights, sales call patterns, and churn signals at a speed and scale that outpaces what teams can do on their own. The output of this investment in customer insights isn’t just a more robust dashboard, it’s a sharper view of the specific problems worth building solutions for, including the ones your customers haven’t figured out how to even fully describe to you yet. Companies that use AI here are surfacing product opportunities in weeks that used to take quarters to identify and act on.
Next, use these customer signals to prioritize and accelerate what you build. Once you’ve identified the problems worth solving, AI dramatically compresses the cycle from insight to working prototype and testable solutions. The question your product and engineering leaders should be answering isn’t “what can we build with AI?”, but they should be asking “are we building something a customer would pay more for, or pay us for the first time?” With this question at the center of their AI strategy, Intercom rebuilt their core product around an AI agent called Fin and the resultant revenue growth tells a compelling AI ROI story.
The third step is about using AI to accelerate the iterations between your product development and go-to-market motions. Get your portfolio of product updates in front of customers before they’re fully baked. Rapid prototyping with AI means your sales and marketing teams can put a working solution “concept” in your customers’ hands and work through the “last mile” tweaks with current or prospective customers. Whether you use dedicated forward deployed engineers (FDEs) or pull your best customer-facing team members into these customer build efforts, the test is the same: does the customer say “I’d pay for that” or at least guide the process to ultimately making that commitment?
Finally, imagine how your AI investments can be expanded to determine whether there are new customers who have the same problem. The same process that helps you deepen relationships with existing customers can help you identify adjacent market segments, model new buyer personas, and run faster go-to-market experiments against them. With a revenue-centered AI investment focus your organization has the potential to widen its addressable market, thus creating another path towards revenue growth.
Once your team has wired your AI investments to drive revenue growth, the cost savings opportunity then becomes more aligned with how your company is scaling enterprise value. AI-driven margin expansion comes from enabling your team (even while it might grow in number) to serve customers more productively, while also reducing what you spend on third-party software and tools. To create enduring and compounding value, sequence your organization’s focus on how AI boosts revenue growth first, and then margin expansion as a fast follow.
In the coming weeks, bring your leadership team two questions. Start with: “Where in our business are we actually using AI to grow revenue beyond our current operating plan targets?” Then follow up with the more challenging one: “For the dollars we’re spending on AI tools and services, what percentage of that investment can we connect to a specific revenue line?”
The answers will tell you everything about whether you have an AI strategy that’s driving future growth or merely an AI story built on “activity” but not lasting value creation.
NextPlay>Forward AI Disclaimer: I very actively use artificial intelligence and large language models to generate the content you read here, but I do review it and edit it to make sure it can be generally useful to people who read it. Keep in mind that AI can make mistakes - check important information. Let me know if I make any errors and I will correct them.