5 Hard-Won GTM Lessons from Viable’s Pivot to Enterprise AI
The hardest pivot isn’t changing your product – it’s abandoning your assumptions about who your customer should be. In a recent episode of Category Visionaries, Daniel Erickson, CEO of Viable, shared how his team’s journey from targeting early-stage startups to serving enterprises revealed crucial lessons about go-to-market strategy in the AI era.
- Your First Market Might Be a Mirage
Viable began as a tool to help startups measure product-market fit, inspired directly by Superhuman’s methodology. They attracted over 500 companies to sign up, but Daniel quickly discovered a fundamental flaw: “There’s actually not a whole lot of money to extract from pre Product Market Fit startups.”
The real opportunity emerged when they noticed large enterprises using their tool for entirely different purposes. This insight led to a complete pivot in their target market and value proposition. The lesson? Sometimes your most valuable market insights come from the customers you didn’t initially target.
- Category Creation Requires Patient Capital
Creating a new product category comes with what Daniel calls an education tax. “When you’re creating something new and you’re on sort of the forefront of what’s possible, you don’t have all of that sort of framework built out for you within your target customers,” he explains. “And so there’s a lot more education that has to happen during that sales cycle.”
This extended sales cycle means founders need investors who understand the category creation journey. As Daniel notes, “There are certain investors out there that are looking for category creators because category creators tend to be, if not winner take all, winner take most for these things.”
- Enterprise Data Problems Hide in Plain Sight
Viable’s breakthrough came from recognizing a pervasive but poorly addressed enterprise challenge: “80% of data that is collected by companies today is unstructured text.” This data lived in silos across different teams and tools, making it nearly impossible for product teams to extract meaningful insights without massive manual effort.
The problem wasn’t just the volume of data – it was that teams could only report on “the most recent thing or the loudest thing.” They couldn’t see the full picture across all customer feedback channels.
- AI Moats Require Deliberate Construction
While many founders assume data volume alone creates defensibility, Daniel reveals a more nuanced reality: “With any sort of AI startup, I believe that your biggest moat is always going to be your data moat, specifically around training data.”
Viable built this moat through two parallel approaches. First, they embedded feedback mechanisms directly into their product: “When you thumbs something up, it actually tells us that was a good answer, and so we can use that as training data going forward.” Second, they developed systematic approaches to generate high-quality training data internally.
- Enterprise GTM Evolution Follows a Natural Progression
Viable’s enterprise go-to-market strategy evolved through three distinct phases, each building on the previous:
- Initial deals through investor networks, which Daniel considers “probably the best way to sort of get your flywheel going”
- Targeted outreach based on existing customer profiles
- Content marketing at scale, achieving “about 50% month over month increase in website traffic just from our content initiatives”
This progression allowed them to learn from each phase while building credibility for the next.
The meta-lesson across all of these insights? The conventional wisdom about product-market fit can be misleading. Sometimes the best signal about your true market opportunity comes not from the customers you’re targeting, but from the ones who find unexpected value in what you’ve built.
For founders building enterprise AI companies today, the path isn’t just about technological innovation – it’s about recognizing when your initial assumptions about your market might be holding you back from a much bigger opportunity.