Building internal AI is different from building internal applications. It requires a new approach.
For years, the competitive landscape in software has been framed around the application layer. Product A vs. Product B. Dashboards stacked on dashboards. Functionality compared feature by feature.
That approach is outdated in an AI age.
AI requires a different strategy. The focus shifts away from applications and functionality and toward models. Each model produces outputs. The question is how those outputs can be aggregated and fed into a larger client AI.
In practice, this may look like:
- Model 1 and Model 2 developed in house
- Model 3 from a software provider
- Model 4 from a niche model provider
- Model 5 from a generalized AI
The value does not come from each model in isolation. It comes from connecting them so the client AI benefits from all of them together.
Historically, this was not possible. Applications competed with each other for attention, integration was painful, and everything stayed siloed.
Now, with advances in MCPs, OpenRouter, and more, aggregation is not only possible but far easier than before. This changes how companies should approach internal builds.
The applications mindset says: "We need another internal product or dashboard that does X, Y, and Z."
The AI mindset says: "We need many models that work together to simplify, each best at what it does, connected into a larger AI that learns and adapts."
This is a big and needed shift in the software and AI landscape.
Companies that stay locked into the application mindset will keep adding siloed products, limiting the overarching intelligence. Companies that move to a model first mindset will build internal AIs that are more powerful, flexible, and cost effective.
That is where the competitive landscape is headed and it is where internal organizational design needs to evolve to.
The sooner organizations make this shift, the sooner they unlock the real potential of AI.