Every technology leader building an AI or machine learning capability today runs into the same wall: the people who can actually do the work are harder to find than the budget to pay them. According to ManpowerGroup’s 2026 Global Talent Shortage Survey, AI skills have overtaken traditional engineering and IT capabilities to become the hardest for employers to fill worldwide, with 72% of organizations reporting difficulty hiring the talent they need. For CTOs trying to stand up or scale an AI/ML team, that scarcity turns a straightforward hiring plan into a strategic decision about where the work gets done and who does it.
AI engineering team outsourcing has become the default answer for many of these organizations. The instinct is understandable: if senior AI/ML engineers are scarce and expensive to hire locally, extending the team through an external partner looks like the fastest path to shipping. But treating it as a copy of traditional software outsourcing misses what makes AI/ML work fundamentally different: the tight iteration loops between data scientists and engineers, the sensitivity of training data, and the fact that model decisions increasingly carry business and regulatory weight that a missed API deadline never did. Those differences are exactly why the nearshore model, rather than generic offshore outsourcing, tends to hold up better for AI/ML delivery specifically, and why the decision deserves more scrutiny than simply comparing hourly rates across regions.
Why AI/ML Outsourcing Isn’t Just Software Outsourcing With Extra Steps
Traditional software outsourcing has a well-understood shape: a spec goes out, code comes back, and quality is judged against defined acceptance criteria. AI/ML work rarely follows that pattern. Models are trained, evaluated, and retrained in short cycles that depend on constant collaboration between the people who understand the data, the people who understand the business problem, and the people who understand the infrastructure. A data scientist iterating on a fraud detection model, for example, needs to work through ambiguous, judgment-heavy questions with the client team in near real time, not through a handoff document.
That collaboration intensity is where offshore outsourcing, built around cost arbitrage and asynchronous handoffs, tends to break down for AI/ML specifically. It is also where the case for nearshore delivery gets stronger, not weaker, compared to traditional software projects. Teams working a few time zones apart, in a shared language and working culture, can run the kind of daily back-and-forth that model development actually requires, rather than losing a day every time a labeling decision or a metric threshold needs to be clarified.
Data Sensitivity Changes the Calculus
AI/ML projects also carry a data governance dimension that most software outsourcing decisions do not. Training data frequently includes customer records, transaction histories, or health information, and moving that data outside a familiar legal framework introduces risk that a generic delivery contract does not fully address. For companies operating in the EU, or serving EU customers, working with a partner already inside the GDPR framework removes an entire category of cross-border transfer questions that offshore engagements in other regions still have to solve for.
This is a genuine advantage of nearshoring from Portugal specifically, and it matters more for AI/ML engagements than for most other IT delivery models, because the underlying data itself, not just the code touching it, is what regulators care about. A well-structured nearshore IT services engagement builds data handling, access scoping, and audit trails into the delivery process from day one, rather than retrofitting compliance after a model is already in production.
The Build vs. Outsource Question Isn’t Binary
CTOs often frame AI/ML staffing as a build-versus-outsource decision, but the more useful framing is which parts of the capability need to stay in-house and which parts can be extended. McKinsey’s research on technology workforce design argues that leading organizations are moving toward a roughly 70/30 split between in-house and outsourced technology talent, keeping strategic ownership close while extending capacity through partners for execution-heavy work.
Applied to AI/ML, that split tends to look like this in practice: the problem framing, the model strategy, and the decisions about what the business is actually optimizing for stay with an internal team that understands the product and the risk tolerance. The data engineering pipelines, feature stores, MLOps tooling, model integration, and much of the day-to-day training and evaluation work are exactly the kind of well-scoped, collaboration-heavy execution that a nearshore team extension can absorb without the business losing control of its AI strategy. This is closer to a Team Extension arrangement than a full outsourced project, and the distinction matters: the nearshore engineers become part of the existing team’s rituals and decision-making, not a separate vendor delivering a black box.
What Gets Lost With Generic Offshore AI Outsourcing
The talent shortage itself has pushed many companies toward the cheapest available AI/ML capacity, wherever it happens to sit. That approach can work for narrowly scoped, well-specified tasks, but it tends to underperform for the ambiguous, exploratory work that defines most AI/ML projects in their early stages. IDC’s research on the broader IT skills gap found that skills shortages are already costing organizations through delayed products and missed revenue targets, and a mismatch between delivery model and project type is a common way that shortage turns into delay.
A generic offshore AI/ML placement optimized purely for hourly rate often means: less overlap for the daily iteration AI/ML work depends on, a support structure built for ticket-based software work rather than experimental model development, and thinner guarantees around how sensitive data is handled once it leaves the client’s own systems. None of these are disqualifying on their own, but stacked together they explain why so many AI/ML outsourcing engagements stall somewhere between the proof of concept and production, even when the underlying talent is technically capable.
Choosing the Right Model for Where You Are
The right delivery model depends heavily on where an organization sits in its AI/ML maturity. A company just beginning to formalize its AI capability usually benefits from a nearshore team extension embedded closely with a small internal core, so that institutional knowledge about the model and the data builds up on both sides together. A company with a mature internal AI function, by contrast, might use nearshore capacity more narrowly, for MLOps infrastructure or scaling data pipelines, while keeping model development fully in-house.
What stays constant across both scenarios is that AI/ML outsourcing works best when it is treated as a genuine extension of the team rather than a transaction. The organizations getting real value from external AI/ML capacity are the ones that invested in the IT delivery models decision itself, matching the engagement structure to the sensitivity and ambiguity of the work, instead of defaulting to whichever model happened to be cheapest or fastest to set up.
The Practical Takeaway for CTOs
AI engineering team outsourcing is not going away, and the talent shortage driving it is not closing anytime soon. What is changing is the recognition that AI/ML work has different collaboration, data governance, and oversight requirements than traditional software delivery, and that those requirements point toward nearshore models more often than they point toward pure offshore cost arbitrage. For CTOs evaluating how to scale an AI/ML capability without losing control of it, the question worth asking isn’t simply where the talent is cheapest. It’s where the talent can work closely enough, and inside a legal framework familiar enough, to actually move the model from experiment to production.
That is ultimately a question about partnership as much as it is about capacity. An AI/ML engagement that functions as a genuine extension of the internal team, sharing context, working hours, and accountability for outcomes, tends to compound in value over successive model iterations. One that functions as a transactional handoff rarely does, no matter how strong the individual engineers on the other end of it are. For organizations weighing their next move on AI/ML capacity, that distinction is usually a better starting point than the rate card.
