Why AI Makes T-Shaped Talent Your Most Valuable Asset (And How to Hire for It)

About a year ago, in a piece titled “The Enduring (and Increasing) Value of T-Shaped Individuals and Organizations,” I explored the benefits of cultivating talent that goes both deep and wide.

As I explained then, “T-Shaped individuals and organizations are those who have deep expertise in one particular field (the vertical line of the T) as well as a broad understanding and ability to collaborate across multiple disciplines (the horizontal line of the T).”

Since this concept was first introduced in the 1990s, it’s proven to be a powerful conceptual model, fostering innovation and resilience in both individuals and organizations.

But I think it’s become even more important now, for reasons, that by the end of this piece, I think you’ll agree with.

Entering the Golden Era of Expertise

Fast forward twelve months from the time I wrote my original piece, and the landscape has shifted dramatically.

The rapid advancement and increasing capability of Large Language Models (LLMs) and other AI tools haven’t just validated the T-shaped model; they’ve supercharged it.

It’s becoming strikingly apparent that we’re entering a golden era for expertise—not necessarily expertise solely defined by decades in a single silo, but the ability to access, synthesize, and apply knowledge across domains.

The way I see it, this marks the democratization of expertise on an unprecedented scale.

What was once a challenging ideal—becoming genuinely T-shaped—is now more achievable than ever, even for those who’ve traditionally identified as deep specialists (I-shaped).

To use a building-industry metaphor, AI acts as a powerful “cognitive exoskeleton,” augmenting our abilities and bridging knowledge gaps that previously required extensive time or entirely separate hires.

As Sarah Gibbons and Evan Sunwall aptly put it in a recent Nielsen Norman Group article titled, “The Return of the UX Generalist,” “AI is broadening the scope of what any individual can accomplish, regardless of their specific expertise… Increasingly, individuals can accomplish tasks that previously required collaboration with several experts in different areas.”

Imagine a marketing manager using AI to analyze complex datasets for campaign insights without needing a dedicated data scientist on speed dial, or a product leader drafting initial technical specifications with AI assistance before looping in engineering.

This is the new reality, and it has profound implications for hiring.

From Skills Gap Filler to Capability Multiplier

This evolution fundamentally changes how organizations should approach talent acquisition and development.

Historically, hiring often focused on plugging specific, deep expertise gaps—finding that I-shaped specialist to fill a defined need.

While deep expertise remains crucial, AI introduces new possibilities:

• Augmenting Existing Talent: Instead of immediately hiring externally for a niche skill, could that work be assigned to a capable internal employee armed with a powerful AI copilot? This empowers current team members, fosters learning, and can be more cost-effective.
• Shifting Hiring Criteria: When hiring, the focus may shift. While specific skills are important, screening for an AI-First Mindset becomes paramount. This means looking for individuals who demonstrate:
  • Innate Curiosity: Are they eager to learn and explore new tools and methodologies?
  • Adaptability: Can they pivot and integrate new technologies into their workflow?
  • AI Fluency: Do they understand the potential and limitations of AI? Can they effectively prompt and critically evaluate AI outputs?
  • Problem-Solving: Do they see AI as a tool to tackle challenges, even those slightly outside their core domain?

A candidate possessing this mindset, even if initially lacking the depth previously thought necessary, might be a more valuable long-term asset.

Their ability to leverage AI allows them to rapidly acquire the functional expertise needed, effectively becoming the expert you require while also bringing broader capabilities.

Crafting Your New Hiring Rubric

As I indicated earlier, this necessitates evolving our hiring practices.

Relying solely on traditional credentials or ticking boxes for narrow specializations is no longer sufficient.

Organizations need a new rubric for evaluating talent in the age of AI.

While this rubric will be unique to your context, consider incorporating these elements:

• Behavioral Interview Questions: Move beyond standard questions. Ask:
  • “Describe a time you used a new technology or tool (like AI) to overcome a challenge or learn something outside your comfort zone.”
  • “How do you approach validating information or outputs generated by AI tools?”
  • “Walk me through how you might use AI to approach [a specific, relevant business problem].”
  • “How do you stay current with technological advancements in your field and beyond?”
• Practical Assessments: Consider assignments that require candidates to use AI tools (ethically and appropriately) to solve a problem, conduct research, or generate creative options. Assess both the outcome and their process.
• Portfolio Review: Look for evidence of cross-functional projects, adaptability, and perhaps even personal projects involving new technologies or AI experimentation.
• Prioritize ‘Learnability’: Weight a candidate’s demonstrated ability and eagerness to learn (their “learnability quotient”) as highly as their existing specific knowledge.

The idea is to see how a candidate can think on their feet with AI and create opportunities to see how they utilize AI to essentially supercharge what they can do.

Building an AI-Augmented, T-Shaped Organization

The rise of accessible AI doesn’t diminish the value of deep expertise, but it profoundly enhances the power of breadth, adaptability, and collaboration. It demands we look beyond traditional job descriptions and qualifications.

Leaders and marketers must now prioritize candidates who demonstrate strategic insight, cross-domain curiosity, critical thinking (especially with AI), and adaptability in navigating our increasingly complex technological and organizational landscapes.

This isn’t just about finding new talent; it’s also about empowering your existing teams. Invest in AI literacy, provide access to tools, and foster a culture of experimentation.

Building an organization fluent in both human and AI collaboration is key to thriving in this new era.

It requires developing an entirely new talent rubric – and the time to start defining yours is now.