A CTO once told me he hired his first AI engineer before he even knew what problem they were trying to solve. “It felt like we were already behind,” he said. “So we hired fast and figured we’d sort the rest out later.”
He’s not alone, either. Companies across all industries are rushing to add AI to their stack. Some get it right, but…many don’t. The difference between success or not usually comes down to whether there was a plan or just a panic.
If you’re trying to grow your AI capabilities, the big question is: do you build an in-house team from scratch or bring in outside help?
This post is designed to help you think through that exact question. We’ll walk through what AI teams actually do, the common challenges, and how to grow the right team for your goals without wasting time, money, or energy.
The urgency: Why companies must tap into AI (like…right now)
From fraud detection in finance to customer insights in retail, AI is already reshaping how businesses operate. And if your team isn’t thinking about it yet, there’s a good chance your competitors are.
Adopting AI shouldn’t just be about jumping on a trend. When used well, AI solves some very real problems. It can help streamline operations, personalize experiences, and uncover patterns your team simply doesn’t have time to spot on their own.
That said, it’s not something you can bolt on at the last minute and expect results. Building the right kind of AI expertise for solutions takes time and planning. Those that wait too long often find themselves stuck playing catch-up, or worse, making rushed decisions and expensive mistakes just to check a box.
Whether you’re a startup with big ideas or a legacy company looking to modernize, the goal is the same: move forward with intention. The sooner you figure out what role AI should play in your business, the better off you’ll be.
The challenges: Why scaling AI teams is so hard
So you’ve decided AI needs to be part of your strategy. Now comes the tricky part: figuring out how to build the team that can actually make it happen.
This is where a lot of companies get stuck.
First, there’s the talent gap. Everyone wants experienced AI engineers, but AI isn’t super old yet and there aren’t nearly enough of them to go around. Especially the kind of engineer who can not only build a model but also explain what it’s doing and why it matters to the business.
Then there’s the speed of change. Tools, frameworks, and best practices in AI are evolving fast. What made sense a year ago might already be outdated, which makes it hard to know what skills to hire for or even what roles you need on the team.
It doesn’t help that “AI team” can mean a lot of different things. Are you looking for data scientists? Machine learning engineers? MLOps specialists? Product folks with AI know-how? All of the above?
And of course, there’s the budget. Good AI talent is expensive, especially if you're only looking in a few high-cost markets.
In short: everyone wants to scale AI, yet few have a clear plan. That’s what we’re here to fix.
Build vs. borrow: Key considerations for AI hiring and team design
When you’re scaling an AI function, one of the first big decisions is whether to build your own team from the ground up or bring in outside expertise. Unfortunately, there’s no universal answer. It really depends on what you’re trying to do, how fast you need to do it, and what kind of resources you have.
When to build
Building an internal team makes sense if AI is central to your long-term strategy. Maybe you’re developing proprietary models or weaving AI into your core product. In that case, having full-time team members who deeply understand your business, your data, and your goals can be a real advantage.
It also works well if you’ve got the time and budget to invest in hiring, onboarding, and (let’s be honest) competing for a very limited pool of talent. Upskilling your current team can help here too, especially if you already have strong engineers who just need support to level up in AI.
When to borrow
Sometimes, you don’t need to build a whole new team; you just need targeted help. Maybe you’re launching a proof of concept, exploring a niche use case, or looking for specific skills like NLP or computer vision.
This is where bringing in external talent can be a smart move. Contractors, consultants, or staff augmentation partners can help you access specialized skills (like natural language processing or computer vision) without the long hiring cycle.
Borrowing also gives you room to experiment. You can learn what works, adjust your strategy, and scale intentionally rather than reactively.
What to keep in mind either way
Whether you build, borrow, or blend both, the key is clarity. Define the problem(s). Outline the roles. Communicate across your existing teams. AI doesn’t work in a silo and neither will your people.
And don’t limit your search to one ZIP code. There’s excellent talent around the world. Expanding your hiring lens can open up possibilities you didn’t know you had.
How to do it right: Smart strategies for scaling AI teams
Regardless of whether you’re hiring in-house, bringing in external experts, or doing a mix of both, building an effective AI team takes more than just smart people. It takes structure, focus, and alignment.
Here are a few practical strategies to make it work:
1. Start with the problem, not the tech
It’s easy to get swept up in tools and models, but the smartest AI teams start by asking: What problem are we solving? Define the business need first, then decide how (or if) AI can help.
2. Hire for both skill and context
A strong résumé isn’t quite enough, partially because AI is so new and changes so quickly. You need folks who can work with your data, understand your customers, and navigate your industry’s unique constraints. Look for folks who are curious, collaborative, and business-minded, not just technically sharp.
3. Balance specialists and generalists
You’ll likely need both. Specialists (like ML engineers or data labeling experts) bring depth. Generalists help connect the dots and work across functions. The right mix depends on your goals and project stage.
4. Think cross-functional from the start
Say it louder; your AI team can’t work in a silo. Pull in product managers, data owners, legal, and even marketing early on. That collaboration will save you extra work and make your solutions more usable. Plus, you never know what other teams can benefit from your AI solution investment or be a valuable co-owner of the processes.
5. Don’t overlook upskilling
Sometimes the team you need is the one you already have. Investing in AI education for existing engineers or analysts can boost loyalty, speed up adoption, and reduce hiring pressure. Partnering with providers who offer structured, hands-on upskilling can make this easier.
6. Go global (but do it thoughtfully)
Expanding your hiring search to global markets can help you find strong AI talent without draining your budget. Teams in LATAM, Eastern Europe, and India often bring both deep technical skills and flexible collaboration styles. Just make sure you align on time zones, communication norms, and long-term goals.
Build smart. Scale strategically. And always keep your eyes on the why — not just the wow.
Final thoughts
If scaling an AI team feels overwhelming, you’re not alone! The space moves fast, the talent is scarce, and the stakes are high. It’s easy to make rushed decisions; or worse, to stall out before you start.
That’s where a trusted partner can help.
At PowerToFly, we connect companies with top-tier AI talent from around the world — engineers, data scientists, MLOps experts, and more. Whether you’re looking to hire full-time, augment your team, or upskill the talent you already have, we can help you move forward with clarity and confidence. We focus on both technical skill and values alignment, so your team isn’t just capable,it works well together, too.
We’ve helped companies hire globally, from LATAM to Eastern Europe to the U.S., and build teams that scale smart, not fast for the sake of it.
AI isn’t a one-off project. It’s a shift in how your team works and what’s possible. So start with what you need. Build with purpose. And when you’re ready to move forward, don’t go it alone.
We’re here when you need us. Click here to learn more about our AI talent services.
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