Most mid-sized employers we talk to are past the question of whether to use AI. The harder question is how to get the people. One path is a permanent hire. Another is an outside team brought in for a defined project. A third, which is often the right one, is a mix.
The wrong choice is expensive in either direction. Hire a senior machine learning engineer into a company with no clean data and no one to set priorities, and you will lose that person within a year. Pay an outside team to build something no one inside understands, and you will be calling them back every time it breaks.
Start with what has to live inside your company
The cleanest test is ownership. Ask what will still need attention two years from now.
If the answer is a product that keeps changing, an internal analytics function that several departments will draw on, or models that touch your customers every day, you need people on payroll. Knowledge of your data, your customers and your quirks only builds up inside the company.
If the answer is a one-time build with a clear finish line, such as automating invoice matching, forecasting demand for one plant, or scoring inbound leads, an outside team can often get there faster than a hiring process can.
When a hire is the better call
A permanent hire fits when:
- The capability is ongoing, not a project with an end date
- You already have data and engineering people who can work with the new hire
- The gap is one specialty, not five
- Someone senior inside the business can set priorities and make decisions
- You are willing to pay for the role for the long haul
That last point matters. AI talent is scarce, and good candidates have options. They want a real problem, access to usable data and a manager who understands what they do. Employers who can offer those three things hire well. Employers who cannot tend to churn through people.
When an outside team is the better call
A project team fits when:
- The problem is defined and the deliverable is clear
- The work needs several skills at once: data engineering, modelling, security review, process redesign
- No one inside has done this before
- You need to decide which use cases are worth pursuing in the first place
- Speed matters more than building a permanent bench
Hiring five specialists to deliver one project is rarely sensible. By the time the last one starts, the first has finished their part and wants something new to work on.
Budget the whole capability, not one salary
Comparing a single salary to a single project fee misleads. Count:
- How many roles the work really needs, and at what seniority
- How many months you need each of them
- Recruiting time and the cost of a vacancy that stays open
- Tools, cloud costs and management time
- What your local market pays, which differs between Toronto, Calgary, Austin and the Midwest
One experienced hire into a team that is otherwise ready is usually cheaper than a retainer. A multi-discipline initiative usually is not cheaper to staff directly. Run both numbers over the same time period before deciding.
Six questions to settle before you commit
- What is the outcome? Write it as a business result, not a technology.
- What do we already have? Be honest about data quality, engineering depth and security expertise.
- How many skills does the work need? One missing skill points to a hire. Several point to a team.
- Who owns it afterward? If the answer is no one, fix that first.
- Who leads it internally? Someone needs authority to say yes, no and not yet.
- How will we know it worked? Agree on the measure before work starts.
Do not leave HR and operations out of this conversation. AI changes who does which tasks, how roles are written and what you pay for them. Employers that decide the technology first and the people question later often find the rollout stalls on the second.
The mixed model, in practice
A common pattern works well. An outside team delivers the first defined project while you hire one or two permanent people to work alongside them. Those hires learn the system as it is built, take over when the contract ends and carry on with what comes next.
The other pattern is the reverse. You already have a capable AI lead and a decent team, and you contract for a narrow skill, like governance review or MLOps, for a few months.
In both cases, put knowledge transfer into the contract. Documentation, working sessions and a named internal counterpart cost little and save a great deal.
What to do next
Before you open a requisition or request proposals, write one page: the outcome, the skills required, the expected duration and the person who will own the result. That page will usually tell you which route to take. If you want a second opinion on the role definition, the market rate or whether to hire or contract, a Traverse recruiter can help with this.