Beyond the Black Box: Stratyfy’s Framework for Selling AI to Skeptical Enterprise Buyers

Selling AI to enterprise buyers is challenging enough. Try doing it in financial services, where decisions impact millions of dollars and regulatory compliance is non-negotiable. In a recent episode of Category Visionaries, Laura Kornhauser revealed how Stratyfy turned skepticism about AI into a 400% customer growth story by fundamentally rethinking how to present machine learning to enterprise buyers.

Flipping the Traditional AI Pitch

Most AI companies lead with technological sophistication. Stratyfy took the opposite approach. “What really unlocked those initial opportunities for us was… the fact that we were able to deliver that technology in a way that was usable especially for our early customers,” Laura explains. This focus on practical implementation over technical prowess became their key differentiator.

Keeping Humans in the Loop

Rather than positioning AI as a replacement for human judgment, Stratyfy built their entire approach around augmenting human expertise. Laura emphasizes that “data alone is not going to give us all the answers. It gives us part of the answers.” This philosophy shaped both their technology and their sales approach.

The Transparency Framework

Their breakthrough came from making machine learning understandable to business users. As Laura describes it, their technology is “very understandable for the user, much more understandable, transparent, controllable than other machine learning approaches, which allows that user to impart their own wisdom or subject matter expertise into a model.”

This proved particularly powerful in areas like fraud detection, where “fraud experts can spot emerging trends or emerging threats faster than you have enough data for a machine to find it on its own.”

Building Trust Through Mission Alignment

Instead of just selling technology, Stratyfy aligned their solution with their customers’ mission. Laura explains, “Our mission is to enable greater financial inclusion while helping FIS better manage and mitigate risk. We see that as two sides to the same coin.” This dual focus on business outcomes and broader impact resonated with enterprise buyers.

The Long Game Strategy

Enterprise sales in risk-averse industries requires patience. “It wasn’t the first, 2nd or the third time that led to those especially first few customers. It was probably the fifth through 10th,” Laura reveals. Their approach focused on building credibility through multiple touchpoints: “Getting in front of people, getting back in front of them, having someone else who we know is a trusted contact of that person mention us again.”

Making Complex Technology Accessible

Their product development focused on making AI approachable. For example, their bias detection feature evolved because “We always saw fairness as a key performance indicator that should be evaluated right alongside expected financial performance or other fit data science metrics.” This approach made complex concepts tangible and measurable for business users.

The Team Behind the Technology

Success in selling complex technology requires the right team. Laura emphasizes they look for people who are “Exceptional from a skills standpoint, but also just exceptional people as well, which is so important, especially as you’re building a company like we’re building, that is mission focused and mission oriented.”

The Results: From Skepticism to Scale

This approach has led to remarkable growth, with Laura noting they’re “now working with four times as many” customers as the previous year. But perhaps more telling is how they’ve maintained quality while scaling, nearly doubling their team size while preserving their commitment to transparency and usability.

For founders selling complex technology to enterprise buyers, Stratyfy’s experience offers a valuable lesson: sometimes the key to adoption isn’t making your technology more sophisticated, but making it more understandable and trustworthy. By keeping humans in the loop, maintaining transparency, and focusing on practical implementation, you can turn technical skepticism into business opportunity.

The future of AI adoption in enterprise isn’t about replacing human judgment—it’s about enhancing it in ways that business users can understand, trust, and control. As Laura’s experience shows, the path to success lies not in building black boxes, but in opening them up for everyone to see.