At some point, every company that wants to use AI seriously runs into the same fork in the road: build an in-house AI/ML team, or bring in outside expertise. Both paths can work. Both can also quietly drain a budget if the decision is made on instinct rather than on the actual numbers.
Here is what the numbers actually say, and a practical way to think through the decision.
The real cost of building in-house
Hiring for AI/ML roles is expensive well beyond the headline salary. In the US, the median annual salary for an AI engineer is around $145,080, and experienced professionals command $180k or more, often with equity and bonuses on top. On top of that, technical recruiters typically charge 15-30% fees, and that is before counting the internal time spent interviewing, the cost of a failed hire, and the ramp-up period before a new team member is actually productive.
Infrastructure adds another layer. On-premise AI infrastructure spend surged to $47.4 billion globally in the first half of 2024 alone, and roughly 72% of AI server spending now happens in the cloud rather than on owned hardware. That shift matters for planning: a cloud, pay-as-you-go model avoids a large upfront capital outlay, which is worth weighing against the instinct to buy and own everything from day one.
Speed is a cost too, even if it never shows up on an invoice
Building a capable in-house AI team from scratch typically takes 6-12 months: job postings, interviews, onboarding, and the time it takes a new team to actually understand your data and workflows. During that window, competitors who move faster are already shipping. The real cost of a slow build is rarely visible on a budget line. It shows up later, as lost market share and momentum that is hard to win back.
This is why many companies use outside specialists for the first phase of a project, specifically to compress that timeline, and only build internal capacity once the approach is proven. It is a similar logic to how AAI Labs typically works with clients: come in fast to validate an idea or ship a first working version, then hand over documentation and, where it makes sense, help the client's own team take it from there.
The talent market makes “just hire someone” harder than it sounds
Even companies with the budget to hire are competing in a tight market. There are roughly 750,000 open AI-related jobs in the US alone, which keeps wages high and candidates scarce. And hiring is only half the problem: machine learning has the second-highest turnover rate among industries, at 12.9%, and the median tenure for a machine learning professional is just 1.2 years. Building a team does not guarantee you keep it.
Most AI projects fail for reasons that have nothing to do with who wrote the code
This is the part worth sitting with before deciding anything: more than 80% of AI project failures come down to management gaps and data quality issues, not a lack of talent. Roughly 80% of a project's eventual success depends on solid data engineering, the unglamorous work of cleaning, structuring, and validating data before any model gets near it. A brilliant in-house hire cannot fix a project that was never set up to succeed, and neither can an outside vendor.
A practical way to decide
Building in-house tends to make sense when the capability is genuinely core to your product, you have enough ongoing work to keep a team busy long-term, and owning the IP and the institutional knowledge outweighs the slower ramp-up.
Bringing in outside expertise (for a defined project, a proof of concept, or to get a first version live quickly) tends to make sense when you need to validate an idea before committing to permanent headcount, the skills required are narrow or temporary, or speed to a working result matters more right now than owning every part of the process.
The two are not mutually exclusive. A common pattern is to start with outside help to prove the concept and ship a working system, then grow an internal team around it once the return is clear. Whichever path fits, the questions to ask before committing are the same: what does success look like, how will the data be maintained once the initial build is done, and who is accountable for the result six months from now?
If you are weighing this decision for a specific project and want a second, no-pressure opinion on the trade-offs, reach out.