Build, buy, or partner

When a business decides to do something with AI, the instinct is almost always to build. It feels like ownership, like control, like the serious choice. We understand the pull, but the evidence is hard to ignore. In its 2025 State of AI in Business study, MIT's NANDA initiative analysed around 300 deployments and found that AI tools bought from specialist vendors or built through partnerships succeeded about 67% of the time. Systems built entirely in-house succeeded roughly a third as often.

The question isn't whether your team is smart enough to build it. It's whether building it is where your advantage actually lives.

Why building feels right and usually isn't

Building promises control and owned IP, and sometimes that's exactly right. What it quietly commits you to is everything around the model: the data pipelines, the evaluations, the safety guardrails, the cost controls, and a standing duty to keep pace as models are deprecated and replaced. For most problems the value was never in the model at all; it lives in the workflow wrapped around it. You can buy the model. You can't buy your way out of owning that workflow forever once you've chosen to build it.

Buy when the problem is common

If your problem looks like everyone else's (transcription, support deflection, document extraction, meeting summaries), the honest truth is that someone has already built it better than a first attempt of yours will, and they maintain it for a living. Buying gets you to value in weeks rather than quarters, and the vendor carries the upgrade treadmill. The test is simple: if a capable competitor could buy the very same thing, building it yourself is spending scarce engineering time to arrive at parity.

Build when it's your edge

Build where the capability is genuinely yours: where it touches proprietary data, a workflow specific to how you operate, or the thing your customers actually pay you for. That's the ground off-the-shelf tools can't reach, and where ownership compounds rather than decays. The mistake is rarely building itself. It's building the commodity parts and buying the differentiated ones, which is precisely backwards.

Partner for the middle

Most real projects aren't build-or-buy at all; they're a blend. The pattern we see winning in 2026 is to buy the infrastructure layer (the models, the hosting, the plumbing) and build the intelligence layer that's specific to you on top of it. This is, candidly, where a studio like ours earns its place: a good partner brings the patterns from dozens of earlier builds, so you don't pay tuition on lessons someone has already learned. It's much of why partnering and buying outperform the lone in-house build, because someone has already made the expensive mistakes.

A three-question test

Before you commit, ask three things. Is this problem unique to us, or does every business in our space share it? Does solving it depend on data only we hold? Would a competitor solving it the same way erode our advantage? If the answers are mostly no, buy it and move on with a clear conscience. If they're mostly yes, build it, and unless AI is already a core strength in-house, build it with a partner rather than from a standing start.