Omni-Model

One of the things I think is becoming incredibly important in enterprise AI is being omni-model.

Not married to one model.

Not assuming the newest model should handle every task.

And definitely not paying premium-model prices for work that a smaller, cheaper model can do just as well.

If I’m extracting fields from a clean document, I may not need the most powerful model available.

If I’m dealing with messy reasoning, exceptions, or a high-risk decision, I probably want something stronger.

That’s where architecture starts to matter.

Route the work based on complexity.

Use the right model for the right job.

Keep the workflow, data, permissions, audit trail, and business logic independent from the model underneath it.

Because ROI in AI isn’t just about whether the answer is good.

It’s also about what it cost to get that answer, how fast you got it, and whether you can change models without rebuilding everything.

The model should be a component of the architecture.

Not the architecture itself... let me say this again with clarity... The model should not but the architecture itself!

That’s why I think being omni-model is going to matter more and more.

The goal isn’t to use the best model. It’s to use the best model for that specific piece of work.