If you’ve already mapped your AI goals and sketched out the roles you need, you’re ahead of the curve. Most teams jump into hiring or training before they’ve taken a moment to define what success looks like.
But even with a clear strategy in place, one of the biggest questions still remains: Should you upskill the team we already have — or bring in new talent? It’s not just a simple matter of speed or budget, either. The right move depends on timing, team maturity, and the kind of outcomes you're chasing.
In this guide, we’ll break down:
- What you need to know before choosing to hire or upskill
- When upskilling works best — and how to do it well
- When hiring is the right move — and how to avoid common pitfalls
- Why the most effective teams blend both approaches
Growing your AI capacity takes more than action. It takes intention, timing, and a clear view of what your team actually needs.
Before you choose: Groundwork for smarter AI team decisions
Even with your AI goals mapped and your ideal roles outlined, there's one more critical step before choosing to hire or upskill: understanding the real shape of your current team.
This step goes beyond filling gaps on a chart. It’s about looking at momentum, context, and capabilities from the inside out. Here’s what to assess before making a move:
1. What's already working — and why?
Are parts of your AI initiative already gaining traction? If so, what made that possible?
- Was it a specific person’s skill set?
- A strong handoff between teams?
- A lightweight workflow that moved quickly?
Look for patterns of progress that tell you what to protect and what to scale — and hint at whether you need new roles or just more support for what’s already working.
2. Where is the friction really coming from?
Not every slowdown is a skills issue. Some are rooted in structure, unclear ownership, or simple lack of bandwidth.
- Are engineers burning out because they lack AI fluency — or because they’re also managing ops and product?
- Is model performance lagging due to poor data — or lack of MLOps expertise?
Getting specific about the source of friction helps avoid the wrong fix like hiring new employees when what you need is process clarity, or launching a training sprint that doesn't match real challenges.
3. What kind of growth pressure are you under?
Scaling looks different depending on what’s driving it.
- Are you racing to deliver a proof of concept before your competitors do?
- Supporting a long-term product evolution?
- Responding to new or changing customer demands?
Urgency, complexity, and visibility all affect whether it makes more sense to level up your current team or bring in external help.
When you pause to look at the full picture — not just the gaps, but the patterns and pressure points — your next move becomes a lot clearer.
When to upskill: Growing the talent you already have
The case for upskilling
Sometimes, the right person for the job is already on your team and they just need the right tools to level up.
Upskilling internal talent can:
- Accelerate project timelines (no onboarding lag!)
- Strengthen retention and morale with clear career growth paths
- Deepen cross-functional collaboration
- Build long-term internal capability
It’s also cost-effective. Instead of hiring from scratch every time a new skill is needed, you invest in a team that evolves with your strategy.
Just one caveat: upskilling only works if it’s targeted. A generic online course won’t cut it. Your training efforts should be tied to real business goals, with clear outcomes in mind.
Who to upskill? It depends on goals — and hidden strengths
Don’t limit yourself to obvious choices. The best candidates for AI training aren’t always engineers. Start by asking:
- What are our current goals for AI?
- What skills do we already have on the team?
- Where are the biggest gaps between those two points?
- Who’s shown adaptability, curiosity, and systems thinking?
- Who knows your business and is able to ask the right questions?
You might find your best AI advocates in data teams, backend engineers, or even product or ops roles. We’ve seen non-traditional candidates — including people from QA, customer support, or finance — grow into highly capable AI practitioners when given the chance. The key is to assess both technical potential and alignment with your roadmap — even if their current résumé doesn’t scream “AI.”
How to do It right
Successful upskilling is structured, supported, and real-world focused. A few best practices:
- Teach in context: Tie learning to live projects.
- Mix theory with hands-on work: Think workshops, peer reviews, sandbox experiments.
- Build AI literacy across roles: Help managers, analysts, and PMs understand AI enough to ask better questions — even if they don’t write code.
- Track outcomes: Measure not just participation, but actual capability gains.
Upskilling is a strategic move that deepens your bench, future-proofs your team, and builds a more resilient organization.
When to hire: Bringing in external AI expertise
The case for hiring
As great as upskilling is, there are still going to be times when training your existing team simply isn’t enough. Maybe your roadmap is aggressive. Maybe the skill set is too niche. Or maybe you need someone who’s seen the pitfalls before and can help you avoid them.
Hiring external AI talent can:
- Accelerate delivery on time-sensitive projects
- Bring in specialized expertise (e.g., NLP, computer vision, MLOps)
- Add fresh perspective from different industries or scaling phases
- Reduce risk by leveraging proven experience
It’s especially valuable when launching new initiatives, building infrastructure, or stepping into unfamiliar technical territory. Bringing in the right hire can reset momentum and set the tone for sustainable growth.
Roles that typically require external talent
While upskilling works well for many generalist or adjacent roles, others often require outside help — especially if you’re aiming to build competitive advantage through AI.
These might include:
- ML Engineers and AI Architects to build and deploy models
- AI Product Leaders who can bridge technical and business strategy
- MLOps Engineers who can make your workflows scalable and stable
- AI Ethicists to guide responsible use and compliance
If these roles are core to your product or roadmap, don’t wait to fill them — and you don’t have to settle for just anyone that’s available locally.
Smart hiring strategies that actually work
The biggest mistake companies make when hiring AI talent? Limiting their search to the usual suspects.
- Don’t just hire in your ZIP code. Expanding your search to global markets opens up access to high-quality talent — often at sustainable rates and faster timelines. Engineers in Latin America, Eastern Europe, and India bring deep expertise, strong collaboration skills, and overlapping time zones.
- Move intentionally, not reactively. Rushed hiring often leads to poor fit or unclear role scope. Define what success looks like in the role before you post a job.
- Use targeted support when it matters. Hiring for specialized or leadership roles can stall your roadmap if not done well. A trusted partner can speed things up without sacrificing alignment.
PowerToFly’s AI Talent services connect companies with top-tier engineers, AI leaders, and global collaborators who are ready to hit the ground running — whether you’re scaling fast or building for the long haul. Explore more →
Blend to win: How to balance upskilling and hiring for long-term success
Think in time horizons
Smart AI teams think beyond either-or decisions. They consider timing, team maturity, and where they want to grow next.
- Short-term needs — like launching a pilot or filling a technical gap — are often best met with external hires or staff augmentation.
- Long-term needs — like scaling internal capability or reducing hiring pressure — are ideal for structured upskilling.
This mix helps you stay agile while still building deep bench strength.
Build a resilient, cross-functional team
AI definitely isn’t built in a silo. It lives at the intersection of engineering, data, product, and business strategy. That means your team should be designed for collaboration, not just technical horsepower.
Consider models like:
- Pod-based teams for rapid experimentation
- Hub-and-spoke models for centralized AI capability across business units
- Embedded specialists for scaling within product teams
Use hiring to bring in missing expertise. Use upskilling to spread fluency across your org.
Culture is the glue
You can hire all the right people and still stall out if your team isn’t set up to learn, iterate, and collaborate.
Like any team, AI teams thrive on psychological safety. But they also require a shared language around models and data, and a culture that rewards testing, failing, and improving. Teams that pair technical excellence with open collaboration build trust in AI across the business.
Whether you’re building internal expertise or bringing in new talent, the goal is to create a team that can adapt, collaborate, and deliver. Not just fill seats. By choosing your approach intentionally, and adjusting as your needs evolve, you’ll build an AI function that scales with purpose — not just pressure.
Key takeaways
- Don’t skip the internal check-in. Before deciding how to scale, understand what’s already working — and why.
- Upskilling deepens capability. It works best when tied to clear goals and supported with structure.
- Hiring brings speed and specialization. Use it for roles that require hard-won experience or niche expertise.
- Mix your strategy to stay adaptable. Blending both approaches allows you to meet short-term goals while building long-term strength.
- Culture and clarity matter most. No team can thrive without collaboration, trust, and a shared understanding of how AI fits into the bigger picture.
Whether you're building from the inside out or scaling fast with global experts, PowerToFly can help you design AI teams that last. Explore upskilling, executive search, and global hiring solutions.
