Who you need to build an AI product

There is a common and expensive misconception that building an AI product requires a team of AI researchers. For the large majority of products it does not, and believing it does leads companies to hire the wrong people at great cost. Understanding who you actually need is one of the most useful decisions you will make.

Product engineers, not researchers

Because most products build on an existing foundation model rather than training one, as the build vs buy page explains, you do not need people who can invent models. You need people who can build an excellent product around one. That is product engineering: fitting the model into a real workflow, measuring its quality, handling its errors, designing for the cases where it is wrong, and releasing something reliable. These are different skills from research, and for a product company they are the ones that matter. Hiring researchers to do product engineering is both expensive and a poor fit for the actual work.

The hard work is reaching production

This follows directly from the theme of the whole guide. The hard part of an AI product is reaching production: the reliability, the evaluation, the error handling, the trust. That work is entirely product engineering. So the team you need is one that is strong at exactly that: senior engineers who have released real products, who think in terms of measured quality and safe failure, who know that the demo is the start and not the end. A brilliant researcher who has never had to make something reliable for real users is often less useful here than a senior product engineer who has.

Small and senior does better than large

The pattern we see across AI work is the same one that holds across software generally: a small senior team that improves the product in fast cycles does better than a large one. This is even more true in AI, where the ability to try an idea, measure it, and adjust quickly is the core cycle of the work, and a small senior team runs that cycle faster than a big one slowed by coordination. We made the general case in a small senior team builds more than a big one and the AI-specific case in execution speed as the main advantage. For most companies, the right choice is to put a few excellent people on a real problem and let them move fast, rather than set up a large AI organization.

What to look for

Whether you hire or partner, look for people who can point to real AI products they have released and made reliable, as well as impressive prototypes. Ask how they measure AI quality, how they handle model errors, how they decide when something is trustworthy enough to release. The questions to ask a partner page in our other guide applies here, with one addition for AI: the ability to talk concretely about reaching production is the clearest sign someone understands this work rather than just being excited about it.

Build the team or bring one in

You can build this capability in-house, which is the right long-term choice if AI is core to your company, or bring in a partner, which gets you a senior AI-capable team quickly and is often the faster way to a first product. The in-house vs outsourced development page covers that trade-off in general, and it holds for AI too. Our AI development work exists for exactly this: senior product engineers who have taken AI products to production before and can do it with you. However you assemble it, aim for execution talent that can release a trusted product, not a research organization, and you will match the team to where the real work actually is.