You need to grow your AI team — but the path forward isn’t exactly obvious. Do you hire new specialists? Train the team you already have? Or a combination of both?
The right answer depends on much more than just urgency or budget. It starts with better questions: What are we trying to build? What skills are we missing? What roles are critical right now vs. six months from now?
When you’re talking about AI strategy, asking the right questions is half the work. Building your AI team is no exception. Whether you’re launching a new product or integrating smarter systems into your stack, your talent strategy will shape your outcomes from day one.
This article offers a strategic guide for tech leaders preparing to build (or expand) their AI teams. We’ll break down:
- Why talent strategy can’t be an afterthought
- What an AI team actually looks like (beyond job titles)
- How to map business goals to roles, not buzzwords
- Why team design — including diversity and collaboration — matters more than you think
So before you start hiring or upskilling, let’s make sure you’re building in the right direction.
Why AI talent strategy can’t be an afterthought
The stakes are high — and the talent pool is shallow
It’s no secret that AI is moving faster than most teams can keep up. 47% of C-suite leaders say their organizations are developing and releasing generative AI tools too slowly, citing skill gaps as a central barrier. From predictive analytics to generative tools, the pressure to “do something with AI” is everywhere. For many tech leaders, that pressure translates into a familiar scramble: hiring fast, training reactively, and hoping it all clicks…before the next product cycle.
But proper AI strategy isn’t exactly plug-and-play. The tools may be evolving rapidly, but building the team to support those tools takes time, clarity, and intention. The truth is, the market for AI talent is brutally competitive. AI job postings more than doubled in early 2025 (from 66,000 to nearly 139,000 by April), indicating a huge surge in demand. Salaries are rising, job titles are shifting, and skilled professionals — especially those with real-world deployment experience — are in short supply. Even when you find the right people, the hiring process can take months, only for your roadmap to change in the meantime.
And the cost of getting it wrong can mean anything from teams that stall out halfway through adoption to projects that never make it to production to engineers burned out from mismatched expectations or strategy decks collecting dust because no one has the bandwidth to execute.
That’s why talent strategy is foundational. Hiring decisions or upskilling efforts shouldn’t be reactive responses to immediate needs. They should be part of a well-defined, long-term plan that aligns with your AI goals.
So…what is an AI team, really?
Before you can build an AI team, you need to define what that means for your organization. Spoiler: it’s not just a group of prestigious computer scientists training LLMs in a lab.
An AI team can take many shapes depending on your goals. It might include:
- Machine Learning Engineers, who build and train models
- Data Scientists, who turn raw data into usable insights
- Data Engineers, who manage pipelines and infrastructure
- AI Product Managers, who connect business needs to technical solutions
- MLOps or DevOps Engineers, who ensure models actually run at scale
- Automation Specialists, who apply AI to real-world workflows
- Domain Experts trained to use, validate, or guide AI outputs
- And yes, occasionally, language model experts — but only when the use case truly calls for it
In reality, few companies need a full-blown AI research division. Most need lean, cross-functional teams that can solve business problems using AI as a tool — not an end in itself.
That means your AI team may be distributed across departments. It may evolve over time. And it may rely just as much on collaboration and communication as on modeling skills. So the question isn’t “Who should I hire for my AI team?”, it’s “What problem am I solving, and what kind of team do I need to solve it?”
Build, buy, or both?
Once you understand your goals, gaps, and team needs, the next big question is: Should we upskill the talent we have, or bring in new expertise? Well, it’s not a straightforward yes or no choice.
- Upskilling works well when your existing team has the foundation and the context — they just need support to grow.
- Hiring makes sense when you need speed, specialization, or new perspectives that don’t exist internally.
The smartest teams do both — building for the long term while staying agile in the short term.
Upskilling alone may not be fast enough. Hiring alone may not be sustainable. But when used together, they form a scalable, cost-effective strategy that adapts with your business. Now let's break down exactly when and how to use each approach — and how to make sure your AI team is built to last.
Start with strategy: map your AI needs to your business goals
Get clear on what you’re building — and why
Before you post a job or launch a training program, stop and ask: What are we actually trying to solve with AI? “We want to use AI” is not a strategy (sorry). A better version sounds more like: “We need to reduce manual review time in our underwriting process.”; or “We want to personalize user recommendations without sacrificing performance.”
These goals shape everything: the tools you choose, the roles you need, and whether you should build internal expertise or bring in external help.
Start by identifying your core AI use cases, success metrics, and cross-functional dependencies. Align with product, operations, and data teams early — not after you’ve staffed up.
Define the roles that support your vision
Once your goals are clear, translate them into the work that needs to be done. For example:
- Want to train models? You may need ML engineers.
- Need to make sense of your messy data? A data engineer and analyst might be enough.
- Trying to scale experiments across teams? Consider an MLOps engineer and an AI-fluent PM.
Not every role needs to be filled from the outside. Some can be upskilled from within — especially if the person already understands your systems and customers. The key is to be intentional. Build for the outcomes you want, not for the buzzwords you’re seeing on LinkedIn.
Diversity isn’t just good practice — it’s strategic advantage
When mapping out your AI talent strategy, don’t just focus on hard skills. Focus on who’s in the room. Teams with diverse backgrounds, lived experiences, and ways of thinking are consistently more innovative. In AI, that matters. Why?
Because diverse teams:
Ask better, broader questions
Catch edge cases and bias faster
Build more inclusive, real-world-ready solutions
Whether it’s gender diversity, cultural perspective, or domain expertise, the more varied your team, the more resilient and adaptive your AI systems will be. Diversity isn’t an HR box to check — it’s a design choice for building smarter systems.
Building an AI team goes beyond finding the smartest people. Designing the right structure, defining clear roles, and aligning every move to your business goals are the real challenges you need to go through first. Whether you’re starting from scratch or evolving an existing team, a strong talent strategy will set you up for lasting success. And the earlier you get intentional about who you need — and why — the faster your AI vision becomes reality.
Key takeaways
- Talent strategy comes first. Define your goals, challenges, and success metrics before hiring or training.
- An AI team isn’t one-size-fits-all. Design roles based on what you're solving, not job title trends.
- Diverse teams build better AI. Include a range of perspectives from day one — it's good strategy, not just good ethics.
- Hiring and upskilling are tools, not opposing choices. Use each intentionally to build a resilient, high-impact team.
- The smartest teams don’t rush. They map the work, then grow the team to meet it.
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.
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