Cranium’s Playbook: How to Build Enterprise Trust When Selling Emerging Technology

Building trust with enterprise customers is challenging enough. But when you’re selling technology that didn’t exist a few years ago, in a category you’re helping create, it requires a completely different playbook. In a recent episode of Category Visionaries, Cranium CEO Jonathan Dambrot shares how they tackled this challenge in the AI security space.

Start with the Problem, Not the Solution

Before Cranium even existed, Jonathan spent years validating the market need. “Over maybe the course of a couple of years, I spoke to probably 100 CISOs, and I couldn’t get a positive answer to the question of how do you get visibility into the machine learning and AI systems,” he reveals. This extensive research period helped identify a genuine gap in the market rather than a solution looking for a problem.

Leverage Institutional Credibility

Unlike most startups, Cranium began life within KPMG, giving them unique access to enterprise-scale problems. “If you want to solve really big problems, there’s no better place than a big four,” Jonathan explains. “You see big problems at the largest scale and you get to get involved in them.” This incubation period allowed them to build their product with direct input from potential enterprise customers.

Build Trust Through Community

Rather than positioning themselves as the sole authority on AI security, Cranium took a collaborative approach. “We have been working really hard to make sure from a category perspective, we have good consensus,” Jonathan notes. They partnered with organizations like ISAACS and the Global Resilience Federation, bringing together industry leaders including Microsoft, Mitre, and CISA to develop practitioner guides.

Address Fear Through Transparency

The team recognized that fear could either be a barrier or a catalyst for adoption. “I’ve never seen more fear around any type of technology than AI,” Jonathan observes. Instead of downplaying these concerns, Cranium made transparency central to their product strategy, building tools that provide visibility and compliance capabilities.

Align with Regulatory Direction

Recognizing the importance of regulation in enterprise adoption, Cranium actively engaged with regulators at state and federal levels. This proactive approach helped them anticipate and shape compliance requirements while building credibility with enterprise customers concerned about regulatory risk.

Design for Enterprise Scale

The rapid pace of AI adoption meant their solution needed to scale quickly. Jonathan points out the dramatic shift: “We moved from basically a linear innovation environment where AI had penetration rates of about 20% to, like, an exponential AI penetration rate of 80% to 90% in a six month period.” Their enterprise-first approach meant building for this scale from day one.

Stay Ahead of Market Evolution

Enterprise trust isn’t a one-time achievement – it requires constantly staying ahead of market needs. “There were some things that we decided to do early on that I think were informed by the conversations we had with our design partners a couple of years ago that we’re modifying now,” Jonathan shares, emphasizing the importance of adaptability.

The key lesson from Cranium’s journey is that building enterprise trust in emerging technology requires more than just a solid product. It demands deep market understanding, strategic partnerships, and a commitment to solving problems at scale. As Jonathan puts it, “In most cases, if you build AI and ML systems appropriately and you do it the right way and it’s for good, and you’re solving big problems, we can do things that we could never have imagined in the past.”

For founders building in emerging technology categories, Cranium’s approach offers a valuable template: start with thorough problem validation, build credibility through partnerships and community engagement, and maintain transparency while staying adaptable to rapid market changes.