Tip
Barbra Gago talks about Greenhouse’s failed category creation attempt: “The ATS or applicant tracking system category at the time carried so much disdain. People had such bad experiences with that whole category of products. We tried to create a new category—recruiting optimization platform. We spent time getting content out there, talking to press about it, positioning it in this way. Ultimately, the budget that customers had and the way that customers talked about what we did, no matter what we called it, was applicant tracking. So it didn’t really make sense to continue to go against the grain of, no, we’re going to be this new thing, if everybody, even seeing the value and differentiation of our platform, still called it an ATS. We abandoned that process. Instead of spending time and money to build a new category, we spent the time and money to elevate the value of this category.”
Turns out AI category positioning works the same way.
Your AI startup wants to create a new category: “AI Decision Intelligence Platform.” You’re not just business intelligence—you’re using AI to make decisions, not just show data. Way more valuable. You spend 6 months: analyst briefings, thought leadership content, press positioning, category definition frameworks. Every demo, you educate buyers: “We’re not BI, we’re decision intelligence.”
Younger marketing leaders stick to the vision. They’ve invested in the category narrative, built content around it, hired a category creation consultant. Giving up feels like failure. They haven’t run enough category creation attempts to know when to quit.
You’ve seen this pattern before. In 2015, your SaaS company tried to create “Workflow Automation Platform” to avoid being “just another CRM.” After 9 months, every prospect still asked: “So you’re a CRM?” In 2018, you tried “Revenue Operations Platform” to differentiate from sales tools. Prospects kept searching for “sales software.”
You know the trap: fighting how customers naturally categorize you burns resources without changing behavior. So you check the data: What budget line items are prospects using to buy your product? BI tools. What do they search for to find you? Business intelligence AI. What do competitors call themselves? AI-powered BI.
The market has spoken. Buyers have budget for BI. They understand BI. They search for BI. Fighting that wastes money. So you pivot: abandon “Decision Intelligence Platform,” own “AI-Powered Business Intelligence.” Instead of creating a category, elevate the existing category. Position as the BI platform that actually makes AI useful, not just another dashboard.
The difference: you’re not changing what buyers call the solution—you’re changing what they expect from the category.
This judgment—knowing when to abandon category creation and elevate an existing category instead—comes from watching enough category creation attempts succeed and fail. Junior marketers think abandoning the vision means failure. You’ve learned that changing strategy based on market feedback is wisdom, not weakness. That pattern recognition comes from seeing both paths play out repeatedly.
Context
Barbra Gago was VP of Marketing at Greenhouse (applicant tracking), CMO at Miro (created “visual collaboration” category), and Head of Marketing at CultureAmp. At Greenhouse, they tried creating “recruiting optimization platform” category to avoid ATS stigma, but abandoned it when customers kept calling it ATS anyway.
Instead of fighting the market, they elevated the ATS category’s value. For experienced executives evaluating AI category strategy, this pattern recognition is critical—you’ve run enough category creation efforts to know when customer language and budget realities trump your vision.
That wisdom comes from seeing both category creation successes and failures over decades.