Caxy Innovation, Technology & Agility

What is the unique story that you feel your company has with AI? 

Caxy’s story with AI starts where most others fall apart — the execution layer. We don’t build AI for AI’s sake. We came up with a plan for AI to free up cash flow. We work with companies that were never built to be tech-first and we make AI real for them. 

AI isn’t just possible — it’s profitable. The unique twist is that we rarely lead with the tech. We start by uncovering operational bottlenecks and asking, “What’s the decision here? What’s expensive if you get it wrong?” That lens helps us zero in on places where AI can thrive. We build tools that run quietly in the background, but radically improve speed, accuracy or experience. It’s AI, but without the theater.

What was a monumental moment for your team when it comes to your work with AI? 

Our internal motto: AI that ships is better than AI that waits. A turning point came when we helped a company stuck in “pilot mode” — the kind where there’s always another committee review, another data quality fix, another quarter gone. We reframed the problem: instead of trying to solve everything, what’s the $10,000 a day mistake you’re still making because this isn’t live? What’s the ticking time bomb that’s threatening to blow up the business? That conversation changed everything. We deployed a lean, focused solution that solved the real issue in weeks, not quarters. That moment reshaped our entire approach.

What challenges did your team overcome in AI adoption? 

The challenge wasn’t getting buy-in on AI — it was getting clarity on what AI should do. PwC found that 94 percent of executives believe AI is critical to future success. But only 14 percent have implemented it at scale. That’s a 5-to-1 gap. Most clients came in thinking they needed a model. What they really needed was a system. We had to teach teams how to think about AI not as a department but as a layer — one that touches data, ops, UX and strategy. Internally, we built lightweight templates for how to run AI discovery, validate assumptions with fast prototypes and bake in user feedback early. That made AI less intimidating and more repeatable.