Neda Nia drives Stibo Systems’ product vision, shaping strategy, innovation, and growth to create measurable value for customers.
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Concepts like “high agency,” “talent density,” “power ICs (individual contributors)” and “taste” in everyday work aren’t new, but AI is amplifying them. High-agency employees who are resourceful—regardless of their level of seniority—can contribute to meaningful work whether as ICs or leaders, creating high-density teams that apply taste and knowledge to deliver best-in-class results.
As leaders, we must aim to achieve all that by teaming great talent with one another.
To turn this vision into an organizational reality, we should rethink the traditional apprenticeship model. For decades, the path to seniority was linear: Start at the bottom, learn through repetition, move to oversight and eventually set the direction. But AI collapses the middle of that pipeline. When baseline execution is automated, the value of routine repetition plummets, while the value of high-level systems design and domain taste skyrockets.
The challenge is ensuring that junior talent develops deep foundational knowledge rather than blindly relying on automated outputs. The opportunity is that high-agency junior talent can skip low-leverage work entirely. They can step directly into roles where they learn high-level decision making side-by-side with senior leaders. By pairing early career adaptability with executive-level discernment, organizations build a self-reinforcing engine of intelligent talent density.
Let’s be specific about developing junior talent while embracing the deep expertise of experienced employees, finding ways to learn from each other and compound our strengths.
How AI Changed The Talent Game
For years, entry-level roles were built around execution: research, documentation, preparing presentations, writing code and analyzing data. AI now completes many of those tasks in minutes. That changes where junior talent creates value.
Today’s graduates are AI-native. AI has always been part of how they study, create and solve problems.
A junior colleague of mine on the product innovation team told me he never sat down and decided to “learn AI.” He just started. He’s been using chatbots since ChatGPT 3.5 was released. Everything came naturally as each new tool got better than the one before.
Another young colleague of mine, a data scientist, added that she left college with a strong handle on what AI is capable of doing well but also where it’s lacking. She’s used to spending less time deciding if AI should be used for a task or not. She also knows how to use less tokens.
That kind of fluency is an advantage. It’s also, tellingly, not the one they think matters most. In a KPMG survey of nearly 1,000 Gen Z interns, 88% said their generation is already ahead when it comes to using AI effectively—yet they identified critical thinking, not AI skill, as the trait most likely to set them apart.
Problem Solving, Not Task Completion
The way I see it, the opportunity lies in redesigning junior roles around solving business problems, using AI to complete the tasks.
Give junior employees a real business challenge, the necessary context, access to AI and the autonomy to explore different solutions. Ask them to explain their reasoning, defend their recommendations and challenge AI’s conclusions. That’s how judgment develops.
AI is an exceptional assistant, but it doesn’t understand business context, customer relationships or organizational trade-offs. Clear guardrails around validation, accountability and critical thinking allow people to learn while taking ownership.
A colleague who started in this field 30 years ago put it simply: Back then, you were just expected to go learn it yourself. Junior employees are still doing the same work of building themselves, just with better tools.
Keeping The Human In The Loop
Another of my young colleagues on the product innovation team talked to me about the importance of “being critical” of AI. He uses it to write code, and he loves how it often helps him optimize in ways he hadn’t considered. But before diving in, he sits down with his manager or another more experienced colleague to talk it through. He said that the planning phase is when he really learns to understand the broader context.
It’s also important to create a safe work culture where it’s acceptable to use AI at work, as long as employees can explain and defend their reasoning. Many recent graduates come from universities where significant effort is spent detecting AI-generated work, which can make them hesitant to use AI professionally even when it’s beneficial. Let them know it’s okay to experiment.
Reverse Mentorship
Zen philosophy has a concept called shoshin, or “beginner’s mind.” It describes approaching problems with curiosity instead of assumptions.
One of my young co-workers said it can be difficult to get more senior colleagues, who are comfortable in their established workflows and ways of doing things, to sit down and look at “something cool” he’d discovered—often a new AI tool. He said that once they take the time to talk to him about what the new tools can do, they come away inspired.
Experienced leaders bring domain expertise, pattern recognition and strategic judgment. Junior employees often bring fluency with new ideas and an outside perspective. And the exchange goes both ways.
Questions such as “Why is it done this way?” encourage reflection and reevaluation. They prevent a mindset of “Just because that’s how we’ve always done it.”
Remembering The ‘Why’
As another one of my junior co-workers pointed out, the real world works differently than school. AI allows him to experiment with a feedback loop, testing things out and playing with what works and what doesn’t. He gets a better sense of what he calls the “why”—a deeper understanding of how something works in real life and real time.
Every generation enters the workforce with a different advantage. This generation happens to have AI. Fluency was never going to be the differentiator, and the sharpest junior hires already know it.
What gets measured next is how quickly that fluency turns into judgment, and how much real responsibility a company is willing to hand over once it does. That’s the real return on hiring AI-native talent: the judgment they build once the tasks are done.
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