Enterprise AI in 2026: build vs. buy
When an off-the-shelf model is enough, and when it costs you more than building your own.
Every enterprise AI conversation in 2026 starts the same way: should we build this ourselves, or buy something off the shelf? The honest answer is that most teams ask the question too early, before they know what "this" even is.
Buy when the problem is generic
If your use case is summarization, classification, or a chat interface over public knowledge, a hosted model plus good prompting will beat anything you build in-house, and it'll ship in weeks, not quarters.
- Drafting and rewriting internal documents
- Routing support tickets by intent
- Answering questions over public product documentation
Build when your data is the moat
The calculus flips when the value lives in your data: proprietary records, internal tooling, or workflows a vendor will never model. That's where a custom retrieval layer, evaluation harness, and guardrails earn their keep.
You don't build a model. You build the system around a model that makes it trustworthy in production.
The middle path most teams miss
Buy the model, build the system. Rent the intelligence, own the retrieval, evaluation, and guardrails. It's the fastest route to something you can actually put in front of customers, and the one we reach for most.
Tell us the goal, the constraints, and who signs off. We scope it against real delivery capacity and ship the first build in weeks.