Axion AI’s Trust-Building Playbook: How They Sold AI to Financial Institutions Before ChatGPT

In 2016, walking into a financial institution to pitch AI was like speaking a foreign language. “The first question we always got was very simple, what is AI?” shares Daniele Grassi in a recent Category Visionaries episode. Before ChatGPT mainstreamed AI, Axion AI had to overcome not just skepticism, but active disillusionment from previous AI hype cycles.

The Trust Challenge

Financial institutions had been burned before. “We had to recover the dissatisfaction and disillusionment that the industry had from previous hypes in AI,” Daniele explains. When Axion mentioned neural networks, they’d hear responses like “yeah, well, two neurons, four neurons” – a stark contrast to the hundreds of neurons and multiple layers in modern deep learning systems.

This skepticism was compounded by the industry’s risk aversion. “In the financial sector, the stakes are high. And in large institutions, sometimes it’s better for the people working in it not to make mistakes rather than go beyond expectations in terms of performance,” Daniele notes. Their solution? A three-pronged approach to building trust.

1. Technical Excellence as Foundation

The cornerstone of Axion’s strategy was unwavering technical rigor. “You cannot really sell smoke, okay? Because yeah, if you sell small and you ride the hype, then you may have short term success, but then if your reputation gets a hit, you’re done,” Daniele emphasizes. They refused to compromise on technical excellence, even if it meant slower initial growth.

2. Strategic Social Proof

Rather than fighting the credibility battle alone, Axion leveraged established institutions’ reputations. They joined ING’s acceleration program in Amsterdam, which led to investment and crucial introductions. UniCredit followed as their second major investor.

But their masterstroke was their advisory board strategy. “I think it is very important for a startup to get sort of sponsorship from relevant person in the industry they’re selling to,” Daniele explains. They brought on advisors like the president of Work Wand and former Goldman Sachs co-head of Quant, providing instant credibility in financial circles.

3. Focus Over Breadth

Instead of chasing every opportunity, Axion maintained laser focus on investment management, specifically in adding alpha to investment strategies. This specialization helped them build expertise that generalist AI companies couldn’t match. “When we find competitors or other companies like pitching, maybe the same prospect, they often are not as focused as us, not as technology and value focused as us, and not scientifically sound and with a long track record as us,” Daniele shares.

The Long Game

This approach required patience. “A very successful ex startup founder who became millionaire told me, yeah, you are getting into a ten year business. This will not be a short ride,” Daniele recalls. While others might have pivoted to easier markets, Axion committed to the long game in financial services.

Their strategy proved prescient. Today, the challenge isn’t explaining AI – it’s standing out in a crowded market. “The biggest problem from us in terms of competition are not other companies that say, are serious players in Syria, but more the, let’s say the smoke again, AI smoke that is out there because now every company says they are doing some kind of AI.”

For founders selling emerging technology to risk-averse enterprises, Axion’s playbook offers clear lessons: build technical excellence before scaling, leverage strategic partnerships for credibility, maintain focused expertise, and commit to the long game. While the timeline may be longer than typical startup trajectories, the approach creates defensible advantages that persist even after the technology becomes mainstream.