Editorial
The market for artificial intelligence (AI) talent has reached fever pitch. Big tech companies are offering $400,000 packages to fresh graduates, while the rest of us scramble for whatever engineering expertise we can find to stay at the cutting edge of the market.
But after decades in technology leadership, including as CEO of an AI startup acquired by Apple and currently as CEO of a digital productivity company that serves 67% of the Fortune 500, I've found that the organizations building truly resilient AI capabilities aren't the ones winning bidding wars. They're the ones strategically developing the talent they already have.
The T-Shaped Engineer Advantage
While we need leaders with deep technical expertise, the real magic happens at Nitro when we build teams of what we call "T-shaped" engineers — professionals who combine deep expertise in one area with broad knowledge across multiple domains.
These aren't necessarily AI specialists. They're curious problem-solvers who ask "why" before rushing to implementation. They understand that technical skills alone aren't enough; effective AI teams balance enthusiasm and healthy skepticism about AI's capabilities and limitations.
The beauty of this approach? Engineers with strong problem-solving skills can quickly adapt to AI development with proper support and training. This internal development strategy has reduced our dependency on the limited external pool of AI talent while training people internally who are familiar with our company culture and values. AI will continue to evolve, and you need talent willing to adapt with it.
Building a Software Engineering Skill Mapping Framework
Here's how to identify AI-ready talent within your existing software engineering workforce.
Start with problem-solving aptitude. Look for team members who naturally break down complex challenges, regardless of their current technical domain. These individuals often make the best AI practitioners because they understand that AI is a tool, not a solution.
How fast they learn matters more than how much they know now. The AI landscape evolves rapidly, so your best candidates aren't necessarily those with the most credentials, but those who consistently learn quickly. Curiosity overrides credentials when building adaptive AI teams.
Context awareness separates good AI implementations from great ones. AI implementation requires understanding nuance, and employees who grasp the broader business context — regulatory requirements, industry-specific knowledge, customer needs — often contribute more to successful AI initiatives than pure technologists.
Don't underestimate collaborative capability. AI projects rarely succeed in isolation, so look for individuals who naturally bridge departments and translate technical concepts for non-technical stakeholders.
The Build vs. Buy Decision
Consider the following when evaluating whether to develop internal AI capabilities or hire external specialists:
- Building internal capabilities makes sense when you need AI solutions that are tightly integrated with existing systems. It's also the right choice when domain expertise is critical (like understanding contract nuances in document processing), when solving problems unique to your industry or organization or when long-term institutional knowledge matters more than cutting-edge technical skills.
- Buying external talent works best when you need immediate expertise for a specific, time-bound project. It's also appropriate when the technology is so specialized that training would be impractical, when you're establishing initial AI capabilities and need a catalyst or when market conditions favor acquisition (though in today's market, good luck with that).
Competing With Big Companies for Talent
Maybe we can't match Google's compensation packages, but we can offer something equally valuable: meaningful work on real problems. Here's our playbook.
Cultivating local talent pipelines has been one of our most successful strategies. We actively partner with local universities, not just “name” institutions. These partnerships create a steady stream of talent who might not have Stanford degrees but possess extraordinary potential.
Creating compelling career narratives helps us attract ambitious engineers. Young engineers want to see their impact, and at a smaller company, they're not just optimizing ad clicks — they're solving real business problems. When we show them how their code directly affects millions of users' daily workflows, it becomes a powerful recruiting tool and bonds them to the user.
We emphasize learning over hierarchy, integrate interns into core projects and provide direct mentorship from senior leaders. Creating clear career paths from intern to architect has helped us retain talent that might otherwise drift to larger companies. This starts while they are still in college.
AI Must Understand User Needs
Technical excellence means nothing without user understanding. The gap between technical capability and user behavior taught us that the best AI teams aren't just technically proficient but empathetic to user needs and understand what the user is trying to do.
We encourage all our engineers to understand the user at the end of every line of code they write. This means regular exposure to customer pain points, direct feedback loops and a culture that values user insight as much as technical innovation.
Implementation Roadmap
Start small. Identify three to five T-shaped engineers in your organization who show AI aptitude. Give them a real problem to solve—not a proof of concept, but something that will deliver immediate value. Support them with training, mentorship and most important, permission to fail and try again.
As they succeed, use their projects as case studies to identify the next group. Build internal champions who can evangelize both the technology and the approach. Within 12-18 months, you'll have a sustainable AI capability integrated with your customers' needs.
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The current AI talent shortage won't last forever. What will endure is the need for teams that can thoughtfully apply AI to solve real business problems. Organizations can build AI capabilities that provide a lasting competitive advantage by developing T-shaped engineers, building strong local talent pipelines and focusing on user needs.
The choice isn't between competing with Big Tech or giving up on AI. It's about being strategic and patient and recognizing that your existing team might already have everything you need. They just need the right framework and support to unlock their potential.
Editor's Note: Read more about the benefits of developing talent internally:
- How to Get Started With an Internal Talent Marketplace — Internal talent marketplaces can help alleviate skills shortages, as well as improve employee engagement. Here's how to navigate the landscape.
- Companies Don't Know Their Own Talent. AI Can Change That — Companies know less about their employees than LinkedIn does. That's a problem.
- 4 Ways to Develop a Diverse Internal Talent Pipeline — Senior leaders must recognize talent and cultivate a workplace where everyone can grow and learn.
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