What you don’t see could cost you: Why open source matters in enterprise AI

What you don’t see could cost you: Why open source matters in enterprise AI The risks of the black box Red Hat AI Value across the ecosystem AI as a community effort Get started with AI Inference About the author Abigail Sisson More like this Blog post Blog post Original podcast Original podcast Keep exploring Browse by channel Automation Artificial intelligence Open hybrid cloud Security Edge computing Infrastructure Applications Virtualization Share In my previous article , I compared AI inference to the nervous system of an AI project — the critical, often unseen infrastructure that dictates the user experience. Whether it’s a chatbot or a complex application, the principle is the same: if the nervous system falters, everything else does too. As we know, however, a nervous system doesn't operate in isolation. It relies on the rest of the body, with countless other systems working in harmony. Enterprise AI is fundamentally the same. Individual models, isolated infrastructure components, fragmented orchestration, or disconnected applications cannot deliver meaningful value on their own. Real impact only comes when these elements connect to form a cohesive, high-performing whole. There are many paths to AI adoption. Some begin with closed, so-called “black-box” systems: pre-packaged platforms that make it simple to get up and running. They can be a great entry point, but they also come with trade-offs. When you cannot see how a model was trained, it can be difficult to explain its behavior, address bias, or verify accuracy in your context. In fact, a 2025 IBM survey found that 45% of business leaders cite data accuracy or bias as their biggest obstacle to adopting AI.