Most mid-sized employers in Canada and the US are not deciding whether to build a global capability centre. They are deciding something smaller and more urgent: who actually does the AI and data work, and where those people sit.
The offshore centre is one answer. It is rarely the first one a company of 200 to 2,000 people should reach for. But the questions behind it are the right ones, and they apply to any structure you choose.
Start with how permanent the work is
AI work comes in two kinds. One is a project: a forecasting model, a document-processing tool, a data clean-up that makes reporting trustworthy. The other is a standing function that builds, runs, monitors and improves systems for years.
Project work suits contract specialists. You get a defined scope, a defined end date and no long-term payroll commitment. Standing functions suit permanent hires, because the value sits in people who learn your data, your processes and your customers.
Most employers have a mix and treat it all as one. That is how you end up with a contractor who holds the only working knowledge of a system the business now depends on.
Four signs you need a permanent team
- AI or analytics requests now come from three or more departments, not one.
- The work touches proprietary data, pricing, formulas or customer records you will not hand to an outside party.
- Models in production need regular monitoring and retraining, not a one-time build.
- Leadership expects this capability to still matter in five years.
If two or more are true, plan for a core team you own. If none are, a contract or project model is likely cheaper and faster.
What a small core team looks like
AI programs rarely work with one isolated specialist. A person who builds models needs someone who can get clean data to them, someone who can put the output into a live system, and someone in the business who owns the result.
For a mid-sized employer, a workable starting core often covers:
- A data engineer who builds and maintains pipelines
- An analyst or data scientist who builds and tests models
- A business owner, often in operations or finance, who sets priorities and accepts the results
- Part-time access to security and legal review
That is three or four people, not a department. Add contract specialists around it for peaks, such as a machine learning engineer for a six-month build.
The talent supply problem is real
Machine learning engineers, MLOps specialists and data engineers are scarce in most local markets, and the best ones receive several offers. Statistics Canada and the US Bureau of Labor Statistics both show strong growth in computer and data occupations over the long term, so competition is not easing.
This is the main reason companies look at offshore centres. It is also a reason to widen your search closer to home first:
- Remote hiring across Canada or the US. A hybrid or fully remote data role opens a national pool.
- Cross-border hiring. Canadian talent is often a cost-effective option for US employers, and the reverse can work for Canadian firms that need a particular specialty.
- Upskilling analysts you already have. A strong analyst who knows your business can become a capable data engineer in a year with the right support.
When an offshore centre does make sense
A company-owned centre in another country starts to pay off when the volume of work is large and steady, you can staff local leadership there, and you are prepared to manage a second location properly. That usually means dozens of roles, not a handful.
Before committing, be honest about:
- Leadership. Someone senior has to own it on the ground and answer to an executive sponsor at home.
- Governance. Decide who approves models, who owns the data and how security reviews are handled before the first hire.
- Time zones. Overlap with your main team affects how quickly questions get answered.
- Knowledge transfer. Documentation and handover habits matter more when teams are far apart.
If any of these is unclear, the centre will not fix it. It will make it more visible.
The case against moving too fast
One GTA food manufacturer we worked with made over 20 hires in 18 months across production, skilled trades and office roles. The lesson carries over to technical teams: it worked because the roles and ownership were settled before hiring started, not because of the hiring channel. Structure first, then headcount.
The same applies to AI. A defined owner, a clear first use case and a realistic budget do more than any location decision.
What to do next
Write down the three AI or data tasks you most want done in the next 12 months. Mark each as a project or a standing function, name the business owner, and list the data it touches. That one page tells you whether you need contract help, a small permanent core or something larger.
A Traverse recruiter can help you scope those roles and find AI, data and operations people on a contract or permanent basis across Canada and the US.