“Show, Don’t Tell”: How Zenlytic Built Trust in the Noisy AI Market

“Show, Don’t Tell”: How Zenlytic Built Trust in the Noisy AI Market

Trust is hard to build in the AI market, where companies routinely overpromise and underdeliver. In a recent episode of Category Visionaries, Zenlytic founder Ryan Janssen revealed their contrarian approach to standing out: focusing on practical problem-solving rather than AI capabilities.

Starting with the Problem

Unlike many AI companies that lead with technological capabilities, Zenlytic began by deeply understanding business challenges. Through their consulting work, they identified a critical issue: “Every company has more data than ever before, but nobody’s really using it to its full capacity,” Ryan explains.

This insight shaped their entire approach to market differentiation. Instead of joining the AI hype cycle, they focused on solving real business problems.

The Demonstration-First Philosophy

Zenlytic’s approach to building trust is remarkably straightforward: show the product in action at every opportunity. “Our objective is really to get to a demo in pretty much sort of every sales call,” Ryan shares. This philosophy extends beyond traditional sales situations to “meeting with an investor, meeting with a potential sort of new user… Or meeting with a potential new team member in a hiring process.”

Even their digital presence reflects this approach. “Our website is basically a giant demo with a website wrapped around it,” Ryan notes, emphasizing their commitment to letting potential customers experience the product firsthand.

Addressing AI Skepticism Head-On

The company’s focus on demonstrations directly addresses the skepticism many potential customers have about AI products. Ryan illustrates this skepticism with a personal anecdote about AI hallucinations, where ChatGPT invented an entire Goldman Sachs career for his wife who had never worked there.

Instead of dismissing these concerns, Zenlytic acknowledges them and shows how they’ve built safeguards against such issues. “When you’re doing data analytics and you’re building board reporting off of this, 90% of the time is absolutely terrible,” Ryan explains, highlighting why their approach to reliability is different.

Building Deep Technical Integration

Rather than being what Ryan calls a “thin wrapper” over existing AI APIs, Zenlytic focuses on deep technical integration. This approach helps them stand out from companies that simply repackage existing AI capabilities – many of which quickly become obsolete when those capabilities are added to foundation models.

The Mid-Market Focus

Zenlytic’s demonstration-based approach particularly resonates with mid-market companies. “We like the mid market sales cycles versus long enterprise sales cycles… the sweet spot for us is something like our customers mostly have revenues between sort of 15 and $500 million a year,” Ryan shares.

Results of the Trust-First Approach

The effectiveness of this strategy is evident in their growth. “We’re about six x on the year so far in terms of Arr,” Ryan notes. This success comes from prioritizing trust-building through practical demonstration over marketing hype.

Lessons for AI Founders

Zenlytic’s experience offers valuable insights for founders building AI products:

1. Focus on solving real problems rather than showcasing AI capabilities
2. Make product demonstrations central to all interactions
3. Address skepticism directly through practical examples
4. Build deep technical integration rather than surface-level AI features
5. Choose market segments where your approach to trust-building resonates

The key lesson is clear: in a market full of AI hype, showing real value through practical demonstration is more effective than telling people about your AI capabilities. As Ryan puts it, “The real way to differentiate is not to be an AI business at all. The real way to differentiate is to solve a problem that happens to be using AI.”

For founders navigating the crowded AI market, this approach offers a path to building lasting trust and credibility with customers. Instead of competing on AI capabilities, compete on your ability to solve real business problems effectively.