More companies are trying to build AI capability in-house right now than at any point in the last decade. Fewer of them are succeeding at it than they expected to. The gap is not usually about ambition or budget. It is about a talent market and a technology cycle that are both moving faster than a traditional hiring plan can keep up with.
The talent shortage is real, and it is getting worse
Across Europe, the numbers are stark. Germany projects that 70% of AI positions will remain unfilled by 2027, with hiring delays already stretching past six months. In France, the share of companies reporting AI hiring challenges jumped from 19% in 2018 to 80% in 2023. In Ireland, 81% of employers say they simply cannot locate the AI talent they need.
This is not a temporary bottleneck. It reflects a structural mismatch between how fast demand for AI expertise is growing and how slowly the supply of qualified people can expand.
Building in-house is expensive, and it is often too slow
Assembling an internal AI team typically costs anywhere from €400,000 to over €1 million a year, once salaries, tooling, and management overhead are counted. That is a serious commitment for any organization, and it comes with a timing problem: AI capabilities themselves evolve on roughly six-month cycles. By the time a newly hired team is fully operational, onboarded, and productive, the technology landscape it was hired to work on may already look different.
Compensation trends make the equation harder still. Average pay for AI specialists rose from around $231,000 in August 2022 to $300,600 by March 2024. Even after paying that premium, retention is difficult, since in-demand specialists are constantly being offered more elsewhere.
Three forces are breaking the old model
1. The talent market has tightened past the point where hiring alone works
Salaries keep climbing, candidates keep leaving, and the pool of people with real production experience stays thin. Competing purely on compensation is not a strategy most companies can sustain.
2. AI capability moves faster than hiring cycles
Recruiting, onboarding, and ramping a specialist team can take months. Meanwhile, the underlying models, tools, and best practices they need to master keep shifting. Many teams spend their first year catching up rather than delivering.
3. Fixed teams do not match variable demand
AI work is rarely a steady drumbeat. Demand spikes during product development and drops during optimization or maintenance phases. A fixed, full-time team built for peak load sits underused during the quiet stretches, and every departure triggers a costly, slow replacement cycle.
The market is already unbundling AI talent
This pattern is not new. It is the same shift that broke Craigslist into specialized platforms like Airbnb and Indeed, and the same logic that pushed enterprises toward SaaS and cloud computing instead of running everything on owned infrastructure. Organizations are moving from owning AI talent outright to accessing AI capability as needed. Fractional and specialized AI teams have grown quickly as a result: the global population of fractional leaders roughly doubled, from about 60,000 to 120,000, in just two years.
When does it make sense to look outside your own walls?
None of this means internal AI talent has no place. Companies with a clear, sustained, high-volume need for AI work, and the budget to match, can still benefit from building a dedicated team. But for most organizations, especially those tackling a well-defined project, testing a new capability, or scaling up and down with demand, bringing in outside expertise for a defined phase of work is often the more realistic path. It converts a large, fixed cost into a variable one, matches capacity to the actual workload, and avoids the retention risk that comes with depending on one or two specialists who may not stay.
The practical question is not "build or buy" in the abstract. It is: does this project need a permanent team, or does it need the right expertise for the phase we are in right now? At AAI Labs, we work with companies in both situations, sometimes building a solution end-to-end with a client's team, sometimes stepping in for a defined project where speed and specialized experience matter more than adding permanent headcount.
If you are weighing that decision for your own AI roadmap, talk to our team. We can help you figure out what the work actually requires before you commit to a structure around it.