10 Must-Have Traits for Staffing the Right AI Talent

Making Talent Decisions That Drive Business Results

Many executive leaders are seeking "unicorn" hires who have the perfect technical AI experience plus business niche expertise. Oh, and they are limiting their search to external talent. In my work advising boards and executives and based on lessons derived through my doctoral research on 46 enterprises successfully deploying AI, I’ve seen time and again that organizations finding success aren't hiring unicorns. They're hiring based on characteristics that predict value creation, not just technical credentials.

Whether you're evaluating a potential promotion or sourcing a new external hire, use this practical, outcome-focused guide. Each trait includes what to look for, how to evaluate it, and a clear next step to help you operationalize smarter AI hiring decisions.

1. Business Value Focus

Too many AI professionals focus on experimentation without connecting their work to business performance. The best talent goes beyond technical delivery to directly impact top-line or bottom-line results.

What to Look For

Candidates who consistently tie AI work to business performance metrics like revenue, cost efficiency, or customer outcomes.

How to Evaluate

• Ask for examples of past work directly tied to KPIs.

• Look for language focused on customer impact and business change.

Next Action

Add this to your interview script:

“Tell us about a time your work directly impacted a business objective. What changed?”

2. Cross-Functional Collaboration

AI success doesn't live in silos. Effective hires are those who collaborate across engineering, product, operations, and the C-suite—translating needs and aligning stakeholders.

What to Look For

Professionals who move comfortably across technical and business domains.

How to Evaluate

• Review cross-functional team experience.

• Ask for references that speak to influence across departments.

Next Action

Use a structured behavioral question:

“How do you gain alignment with stakeholders outside your domain?”

3. Continuous Learning and Curiosity

In AI, the pace of change is unforgiving. The best candidates are self-directed learners who stay current and apply what they learn in practical ways.

What to Look For

Candidates who stay current, self-initiate learning, and apply new knowledge.

How to Evaluate

• Ask what AI trend, tool, or method they’ve recently explored—and why.

• Review side projects, certifications, or independent learning efforts.

Next Action

Include this interview prompt:

“What’s something new you’ve learned in AI in the last 60 days? How could it help us?”

4. Adaptability and Resilience

Even the best models fail. The key differentiator is how someone responds to failure—do they pivot constructively or freeze under uncertainty?

What to Look For

A proven ability to pivot, learn from failure, and iterate in uncertain conditions.

How to Evaluate

• Ask for examples where things didn’t go to plan.

• Evaluate how they adapted and what they learned.

Next Action

Add a scenario question:

“You’ve deployed a model that underperforms. What’s your plan in the first week?”

5. AI Literacy or Deep Technical Expertise (Role Dependent)

Not every AI hire needs to write Python—but every one of them should understand how AI works in context. Match depth to the role’s needs.

What to Look For

For strategic roles: AI literacy in plain language.

For technical roles: demonstrable, relevant hands-on capability.

How to Evaluate

• Ask for real-world applications, not just academic theory.

• Use role-specific technical assignments if appropriate.

Next Action

Incorporate an AI fluency assessment:

“Can the candidate explain when to fine-tune a model—and when not to?”

6. Ethical and Responsible Mindset

Responsible AI isn’t philosophical. It’s risk management, brand protection, and long-term operational viability. The stakes are real: bias in algorithms can lead to regulatory exposure, reputational damage, and flawed business outcomes. The best AI professionals build with guardrails from day one that are designed to protect your business and drive both bottom-line efficiency and top-line growth.

What to Look For

An ability to anticipate and manage AI-specific risks—such as performance disparities, explainability gaps, and regulatory red flags—without slowing down delivery.

How to Evaluate

• Ask: “What risks do you consider before deploying a model?”

• Look for candidates who address bias detection, traceability, and proactive mitigation—not just post-deployment patchwork.

Next Action

Use a structured case prompt:

“You discover your model underperforms for a key customer segment. How do you investigate, manage the exposure, and maintain business continuity?”

7. Problem-Solving and Critical Thinking

AI talent must be more than technically capable. They need to be commercially relevant. The strongest candidates don’t just build impressive models; they solve the right business problems. They cut through ambiguity, focus on business impact, and make structured decisions that align with organizational priorities. They don’t chase “interesting” opportunities, and there are MANY of those, instead, they hunt down business outcomes with laser focus.

What to Look For

Structured, analytical thinkers who can scope poorly defined challenges, identify value levers, and drive toward execution without losing focus.

How to Evaluate

• Request a walkthrough of a complex or ambiguous project.

• Evaluate how they defined the problem, framed constraints, and prioritized tradeoffs to get results.

Next Action

Add a working session to the interview process:

“Here’s a messy business problem. Walk us through how you’d break it down and apply people, process, and technology to solve it—step by step.”

8. Communication and Simplicity

If your AI talent can’t explain their work to non-technical leaders, your strategy will stall. Prioritize those who make complexity actionable.

What to Look For

The ability to translate AI concepts into business-relevant language.

How to Evaluate

• Ask: “How would you explain what this model does to an executive audience?”

• Review past documentation or presentations.

Next Action

Include a “translation challenge” in the interview:

“Explain a model’s recommendation to both a software engineer and a sales leader.”

9. Human+AI Partnership Orientation

Great AI elevates human expertise. Seek candidates who design for augmentation, not automation, and focus on user impact.

What to Look For

A design approach that enhances, not replaces, human judgment.

How to Evaluate

• Ask how they gather end-user input.

• Look for evidence of feedback loops and user testing.

Next Action

Direct question to include:

“How do you ensure your AI solutions genuinely help the people using them?”

10. Diversity of Perspective

Diversity of perspective is a business advantage. It strengthens decision-making, reduces blind spots, and drives better commercial outcomes—especially in AI, where homogeneity often leads to incomplete data, flawed assumptions, and biased outputs. As Caroline Criado Perez illustrated in Invisible Women, the most dangerous problems are often invisible—because the data doesn’t reflect what’s missing. Cognitive diversity is one of the only reliable ways to surface those gaps before they become performance risks.

Top-performing AI teams don’t pursue diversity because it’s fashionable. They do it because it’s operationally smarter. It improves AI solutions, reduces risk, and results in more commercially viable solutions.

What to Look For

Candidates who bring differentiated thinking based on varied industries, functions, or lived experience. People who notice what others miss and aren’t afraid to question defaults.

How to Evaluate

• Ask for specific examples where their unique perspective revealed a risk, flaw, or hidden opportunity.

• Probe for how they engage with design assumptions and identify overlooked user needs or systemic gaps.

Next Action

Use this scenario-based prompt:

“Tell us about a time you had a different viewpoint than your team. What risk or opportunity did you see that others didn’t and what was the impact?”

11. Bonus Trait: Credibility and Influence

The smartest AI solutions won’t scale without someone who can align stakeholders, earn trust, and move the work forward. Your AI hire must have the credibility to influence across functions, win support from skeptical leaders, and carry momentum through organizational friction. This isn’t soft skill fluff; it’s a core business enabler. Without it, even the most technically sound AI solution will never scale.

What to Look For

Candidates who consistently earn trust, command attention in a room, and lead with clarity, even without formal authority.

How to Evaluate

• Ask: “Tell me about a time you had to influence a decision where you had no formal authority.”

• Probe references on their perceived influence, ability to lead through complexity, and how they show up with executives and peers.

  
  

Next Action

Include this scenario in your interviews:

“You’ve identified a valuable AI opportunity that will change a business process. Leadership is hesitant. How do you gain alignment and move it forward?”

Conclusion: Build AI Teams That Deliver Business Value

AI is now a core driver of innovation, competitive advantage, and operational efficiency. Yet many organizations still chase “unicorn” hires, focusing narrowly on technical credentials while missing the broader capabilities that truly drive success.

The most impactful AI teams are built intentionally, by leaders who prioritize the traits that consistently translate AI into measurable business results: a focus on commercial impact, cross-functional collaboration, adaptability, critical thinking, and the ability to simplify and scale AI solutions.

This framework serves as a practical tool for making smarter talent decisions. Use it to evaluate internal promotions, assess external candidates, and align your hiring process with the traits that predict value creation.

Ultimately, business success with AI doesn’t come from hiring the most impressive individual contributors. It comes from placing the right people in the right roles. People who turn AI into business outcomes that matter. The key to winning in today’s AI-driven economy is staffing for impact, not credentials.