More data, faster analysis, and new AI-driven tools are changing how marketers make decisions and what they need to measure. For CMOs in particular, having more information doesn’t necessarily make those decisions easier. Increasingly, competitive advantage depends on how quickly organizations can turn data into action, learn from the results, and understand what that action is worth.
Michael Kassan, founder and CEO of 3C Ventures, recently sat down with Henry Innis, co-founder and global CEO of Mutinex, for an episode of the Good Company Podcast to explore these shifts. Their conversation covered everything from organizational learning and marketing investment to measurement and AI visibility. The following is an excerpt from that conversation.
Michael Kassan: For years, competitive advantage came from having information that others didn’t. Now, we have almost too much information. What do you see as the competitive advantage?
Henry Innis: Think about why a startup typically beats enterprise, even though the enterprise has more resources, more intelligence. It’s typically because of their speed to decision-making. It’s much quicker, so they can be a lot more decisive, faster. That principle hasn’t changed. It’s been magnified in the age of AI.
In the past, it was relatively hard for any organization, even small ones, to aggregate a lot of information quickly. Now that’s become a lot easier, but the challenge has become how you use that data quickly. Most companies don’t have a clear decision process.
Kassan: With all this data, where does judgment fit in?
Innis: Judgment matters more, not less. Data doesn’t tell you who has a “golden gut.” Marketing has historically struggled to identify who’s actually driving growth. That’s why incentives and outcomes matter—you need systems that reveal whether intuition is creating impact.
Kassan: You’ve said organizations should “learn like a machine.” What does that mean?
Innis: Machines learn in a way where they don’t necessarily hold onto their bias. They react well to data, trying to do the task relatively quickly and without the kind of emotion that can sometimes color it.
When it comes to learning, you want an organization to be receptive to market feedback and focus on the outcome, not the input. If I’m running a marketing measurement program, the No. 1 thing I need to get good at doing is understanding and logging where we got something wrong rather than where we got something right.
You’ve got to understand every time you try to learn something, did it go well or did it go badly? And then understand the why. If you don’t capture the misses, you can’t improve the model.
Kassan: How should CEOs think about marketing investment today?
Innis: The CEO should think about marketing as the most controllable aspect of how they can influence sales growth and trajectories, as well as an EBITDA lever. CEOs should also see marketing as dollar in, dollar out—that’s really important. And because of AI, CEOs can get robust financial information more quickly, allowing organizations to be more nimble.
Kassan: I’m curious about your view of the level of transparency leaders should be demanding from measurement partners and how that will enable the trust you have to build.
Innis: Let’s talk about the power struggle happening in measurement. The advertiser ceded control from about 2008 to the sell side for all their financial reporting around how marketing was performing. Really, what the advertiser said was, “As long as I’m getting my growth, ultimately, I’m going to rely on the sell side to provision my metrics, my numbers.”
When we talk about the rise of vanity metrics, what we’re really talking about is the rise of sell-side metrics. We’re really talking about the rise of platforms provisioning financial numbers back to us rather than the advertiser telling a platform what it was worth to them. What that did is it made brands price takers, not price makers.
Kassan: How should companies be preparing, and what does a company need to measure before it can invest intelligently in AI visibility?
Innis: Every company in the AI category at the moment is building the wrong product. What they’re building are products designed to measure how visible you are, not what that visibility is worth.
I think for the average marketer, if I’m at their CMO level, the decision to make is: How much do I commercially invest in AEO or GEO? How much do I invest in that bucket vs. my paid media bucket vs. my traditional SEO bucket? And what are all those things worth to me?
Those are the questions to answer. As we go into this age of storytelling, understanding what moves the AI needle and what that’s worth is going to be critically important.
Kassan: Can you share an example of Mutinex’s work, like Hershey’s?
Innis: We’ve had some really great outcomes with Hershey’s, and were excited for the launch of “biddable outcomes” with them. For a long time, it’s been very hard for an advertiser to take what’s important to their P&L and inject it directly into the DSPs. That is what we’re doing with biddable outcomes, and very quickly. With this, an advertiser can see a combination of publisher, format, campaign, creative, and know that they should be willing to pay X percent more because their profitability is typically X percent higher on the P&L.
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Check out the full conversation between Kassan and Innis on the Good Company Podcast to hear more about turning information into action, building organizations that learn faster, and navigating what’s next in AI and measurement.
This interview has been edited and condensed for clarity.