“Should we hire an AI team or bring in a partner?” is one of the bigger early AI decisions — and the right answer depends mostly on one question: is AI your product or your tool? Here’s the framework, and where dgm fits. (dgm implements osFoundry, a separate company’s platform — we are not osFoundry.)
When in-house makes sense
Build an in-house AI team when AI is your core differentiator — it’s the product, or a moat — and you can hire and retain the talent to build and maintain it. If AI is the business, owning it deeply is worth the cost and effort.
When outsourcing or partnering wins
For most businesses — those adopting AI to run operations better, not to sell it — partnering is faster, cheaper, and lower-risk. The AI isn’t your moat; the outcomes are, and you can get those without building and staffing a team (see build vs buy).
The hidden costs of in-house
In-house looks cheaper than it is:
- Talent — scarce, expensive, hard to retain.
- Maintenance — constant upkeep as models and tools change.
- Opportunity cost — engineers on AI plumbing aren’t on your core product.
- Obsolescence — a long build can ship behind the frontier.
The middle path
There’s a third option that captures the best of both: partnering to implement a model-agnostic platform. You get AI custom-fit to your workflows (the control of in-house) without staffing a team or locking in (the speed and flexibility of outsourcing). And done right, outsourcing doesn’t mean losing control — a good partner keeps your data under your control, avoids lock-in, and trains your team to own the system.
How dgm fits
dgm is the partner option: it implements the model-agnostic osFoundry platform custom-fit to your workflows at fixed pricing ($399 assessment, $3,999/month), with no lock-in and training so your team can own it — control without the cost and burden of building in-house. If you’d rather explore the platform yourself first, go straight to osFoundry; if you want the middle path, that’s where dgm comes in.