When Your CMS Hosts Four Websites and AI Becomes Your Co-Pilot

There's a peculiar moment that happens when you're knee-deep in Azure KeyVault certificate configurations at 3 PM on a Tuesday, managing authentication schemes for four separate websites running through a single ASP.NET Core application, when you realize: this is exactly the kind of complexity AI was built to help us navigate.

Modern enterprise CMS development isn't the "install WordPress and pick a theme" experience many imagine. Real-world platforms like Kentico Xperience power ecosystems—multiple brands, intricate authentication flows, Dynamics 365 integrations, WS-Federation SSO schemes that need perfect orchestration. The cognitive load is immense. You're not just building websites; you're architecting digital experiences that span organizational boundaries while maintaining security, performance, and developer sanity.

This is where AI tooling is fundamentally changing the game, not through flashy automation, but through something more subtle: context management at human scale.

Consider a scenario I encountered recently: troubleshooting a 500.30 error in a multi-site configuration while implementing Azure Identity Provider integration. Twenty years ago, this meant hours of documentation diving, Stack Overflow archaeology, and tribal knowledge phone calls. Ten years ago, it meant better documentation and more targeted searches. Today? AI-assisted development tools can hold the entire context—your authentication schemes, certificate deployment strategies, CQRS patterns with MediatR, Lucene search configurations—and help you reason through the problem space in natural language.

The transformation isn't that AI writes your code (though it can). It's that AI reduces the context-switching tax that makes complex architectures so mentally expensive.

When you're working with embedded Razor class libraries, managing four separate WS-Federation callbacks, coordinating Dynamics 365 marketing lists, and implementing sophisticated cache invalidation strategies—the traditional "figure it out" approach means holding an impossible amount of architectural knowledge in your head simultaneously. AI becomes a thought partner that remembers the details while you focus on the decisions.

But here's what genuinely intrigues me: platforms like Xperience by Kentico are themselves evolving to incorporate AI capabilities—content recommendations, personalization engines, intelligent search. We're approaching an inflection point where AI assists both the creation of the platform and the experience it delivers. The developer uses AI to navigate CQRS query handlers and cache dependency management, while the end user experiences AI-powered content discovery they never consciously notice.

The irony? The more sophisticated our CMS architectures become—multi-tenant, headless, composable—the more we need AI assistance just to maintain them effectively. We've built systems whose complexity exceeds comfortable human cognition. AI isn't replacing developers in this equation; it's making it possible for developers to keep building increasingly ambitious systems without drowning in their own technical debt.

Is this progress? Unquestionably. But it raises an interesting question: are we building complex systems because AI can help us manage them, or is AI emerging because our systems demanded it?

I suspect the answer is yes.