If your internal AI project has been "in progress" for the better part of a year, you are not alone, and it is probably not a technology problem. Most stalled AI builds trace back to the same root cause: organizational and hiring constraints that show up long before the first model gets trained.
The hiring timeline nobody budgets for
Hiring a senior ML engineer in Europe typically takes 4 to 6 months from job posting to start date. If the project needs a second engineer, expect another 3 to 4 months after that. Add it up, and a team that can actually ship is often 9 to 12 months away from the day the project was approved.
That timeline rarely makes it into the original project plan. Budgets and roadmaps get set assuming the team exists, when in practice the team is still being recruited. By the time hiring catches up, the business case that justified the project may have already shifted.
The skills gap inside teams that already exist
Even when a company has ML talent in-house, production AI requires a blend of ML engineering, data engineering, MLOps, and software engineering, and the right mix shifts depending on the stage of the project. It is common to see internal teams with two or three ML engineers who are strong at model development but weak at deployment infrastructure. That gap does not show up during the prototype phase. It shows up exactly when the project needs to move into production, which is often the point where stalled projects quietly die.
What the research actually says (and what it does not)
A widely cited MIT NANDA report found that vendor partnerships succeed roughly 67% of the time, compared to about 33% for internal builds. It is a striking number, and it gets repeated often. It is worth reading with some caution, though: the report was based on 52 organizational interviews and 153 survey responses, and its authors describe the findings as preliminary. It points at something real, but it should not be treated as a settled statistic.
A more useful reference point, if less dramatic: across more than 60 AI projects, roughly 70% reached production deployment. That is a self-selected sample (feasible projects get filtered in, and the clients involved had already committed to doing AI in the first place), so it is not a universal benchmark either. But it does suggest that the gap between success and failure has less to do with whether a team is internal or external, and more to do with how the project is set up from the start.
When external help actually makes sense
External teams tend to earn their keep on projects with a defined 3 to 12 month timeline, that require a mix of skills no single hire can cover, and where internal hiring would take longer than the project can afford to wait. In those situations, bringing in a team that already has the ML engineering, data engineering, and deployment skills assembled can be the difference between shipping this year and shipping next year.
Internal builds make more sense when AI is core to your product and the work will span years rather than months, or when the eventual headcount will justify the 6 to 12 month hiring runway. In that case, the patience is worth it, because you are building a capability you will need indefinitely, not a project you need delivered once.
Four things that predict success either way
Regardless of whether the team is internal, external, or a mix of both, a handful of factors show up again and again in projects that actually reach production:
A feasibility gate with real authority to kill the project if it is not going to work
An evaluation methodology defined before building starts, not after
A named owner for the production handoff, so the project does not stall at the finish line
Domain experts who are actually available to the team, not just listed as stakeholders
Get these four right, and the internal-versus-external question becomes a staffing decision rather than a make-or-break one.
If you are trying to work out whether your team can realistically deliver on the timeline you have, or whether outside help would close the gap faster, talk to our team. We have staffed and advised on AI projects across exactly these tradeoffs, and we would rather tell you honestly what your project needs than sell you a headcount you do not.