My first year at Snowfox has changed how I think about AI. From the outside, it looks like we’re moving incredibly fast. New models, new tools, constant headlines. But inside large organizations, reality looks very different. If you look at the classic technology hype cycle, it makes sense. We’ve gone through the phase of inflated expectations, and in 2025 most companies entered a period of scrutiny. What are the risks? How much effort does it take to implement?
Everyone has seen impressive demos, but very few examples of something that actually impacts the bottom line without requiring heavy change management. Roughly one quarter into 2026, something is clearly shifting. The acceptance that AI needs to be implemented is there. But most large companies still struggle to use AI in their daily work.
Policies, compliance, and uncertainty slow things down. At the same time, most people judge the state of AI based on the free tools they’ve tried, without realizing how much more advanced and practical paid, production-grade solutions have become. That gap creates confusion. AI feels both overhyped and underwhelming at the same time.
Today, I’m sitting on a product that has been built for almost 10 years, which in this space is a very long time. Our main challenge at Snowfox is not the technology, it’s convincing enterprises that implementation can actually be simple: fast time to value, no disruption, no new interfaces, minimal training. Because when something sounds too good to be true, most organizations assume it is. They’ve been trained to expect long projects, heavy integrations, and slow ROI.
The companies that move forward now are the ones willing to challenge that assumption. And they will have a significant competitive advantage. Not only through direct savings, but perhaps even more importantly by building internal confidence from actually having implemented AI.
