Agentic AI refers to artificial intelligence systems that can perform tasks on behalf of humans and make independent decisions without direct oversight. These systems can reason based on context, memory, and available data, generate detailed plans, and autonomously execute the steps required to complete a task. Their growing capabilities mark a shift from passive tools to active collaborators.

While some speculate that agentic AI will displace many junior-level roles—and there may well be a certain level of recalibration—the reality is more nuanced. Rather than diminishing the importance of early-career workers, this shift makes them more critical than ever for one simple reason. The generation now entering the workforce has “grown up” alongside generative AI. They are more comfortable with its pace and equipped to shape its future. They are “AI natives.”

At the same time, as someone famously said, ‘there’s no compression algorithm for experience, and experience and sound judgement are not intrinsically an attribute of generative AI, which at best is 4 years old and still undergoing rapid evolution. Which begs the question: Who’s going to provide experienced supervision to a potentially limitless number of AI agents entering the workforce?

Understanding how we nurture a generation of AI natives—and equip them with the right skills and tools to be leaders and not passive observers of this transformation—will be critical to defining the future of work, and society at large. Their instincts, creativity, and adaptability will determine how successfully we integrate AI into our organizations not just as a tool but as a partner. The challenge ahead is beyond technological; it is cultural, educational, and distinctively human.

The new AI paradigm

Here’s the first thing we need to come to terms: This is a new game with new athletes who are likely more proficient than previous players ever will be.

Think of it this way: If you’re asked to learn the piano later in life, you might be enthusiastic and dedicated, but the odds of becoming a prodigy are slim. Similarly, think about someone who learned to use a computer well into adulthood. Even decades later, their typing, mouse usage, or navigation of user interfaces often reveals their late start.

The same dynamic is now unfolding with AI tools. A generational divide is emerging—not because more seasoned professionals lack intelligence or drive, but because they didn’t grow up with these tools. For those who aren’t AI natives, adapting to an AI-first or AI-hybrid workforce may prove more difficult than we anticipate. However, that’s where most of the institutional knowledge and experience lies. 

Several technological shifts have created similar knowledge vacuums: the introduction of computers, the internet, mobile, cloud technologies, and others. In each case, it took a decade or more before fluency became a baseline requirement for certain roles. Those who couldn’t adapt either transitioned into roles that didn’t require those skills or exited the workforce altogether. What’s different now is the speed. The AI shift is happening in years, not decades. Workers who lack proficiency in leveraging AI tools will fall behind, and those who have learned to harness it to elevate their work will advance.

As with every major technological shift, a new generation of leaders is emerging, particularly entrepreneurs whose native fluency with AI is reshaping the landscape. Consider the CEOs of companies like Devin, Windsurf, and Scale AI—all AI natives. Could one of them be the next Bill Gates or Michael Dell? It’s possible. Our responsibility as a society and as leaders is therefore twofold: to maximize the potential of this new generation of AI natives, and to ensure the rest of the workforce focuses on accelerating the “path to seniority” for our junior athletes. 

Investing in AI natives

Our priority must be to invest in junior talent who will redefine the industries we work in. While the exact contours of this transformation are difficult to predict, its scale is easy to imagine if we accept that AI is the most profound technological disruption of our time. In a world where technology evolves at sonic speed, our focus must be on ensuring that human adaptation keeps pace. Simply put, we need to train our best athletes for this new arena and equip them with the essential skills to manage and lead this change in an accelerated way.

With the arrival of agentic AI, the ability to spin up AI coworkers on demand will soon be a baseline capability. This shift will require even the most junior employees and individual contributors to master three foundational management skills: Describing a task clearly, delegating it effectively to an AI agent, and supervising the results. Supervision is especially critical in a world where agent technology is still maturing. The failure mode here is not technological, it’s organizational. Delegating work to an agent without the ability to supervise it is a recipe for disaster, which is why we must instill a new sense of quality control and agency in our people.

As an example, AI systems today are highly sensitive to how questions are posed. The prompt or “context” is processed by the AI’s attention layers, which determine the relative importance of each word or token. A slight miscommunication can be amplified, distorting the output. In the case of autonomous agents, hallucinations don’t just lead to bad answers, they can trigger incorrect or even dangerous actions. Until we are confident these tools will not act irrationally, we must keep humans in the loop. Therefore, rethinking the concept of agency is essential.

Agency, in this broader sense, includes the tasks delegated to an AI agent, how those tasks are executed, and how the agent communicates with humans, data sources, and other agents. New communication protocols like MCP and A2A are emerging to standardize these interactions. But the human role remains central.

As junior employees take on the responsibilities of “managers,” the traditional boundaries between business and engineering are dissolving. Much like how product managers and engineers have converged, today’s professionals must be fluent in both domains. To be a great engineer now means also being a great product manager: understanding the customer, defining the roadmap, identifying risks and biases, and designing compensating controls. This is the mindset we must cultivate in our AI-native workforce. They will be expected to manage their AI agents not just by issuing commands, but by understanding their capabilities and limitations, and by anticipating risks before they become problems. Supervision is key, which requires experience, and experience requires time—which, at this pace of change, is a scarce commodity. 

Cultural transformation

The rise of artificial intelligence is not merely a technological evolution—it is a cultural transformation that is reshaping the very fabric of organizations. Its impact reaches far beyond productivity gains, challenging how we structure teams, define roles, and manage performance in a hybrid workforce of humans and AI agents.

We are entering an era where developers no longer write code alone, and knowledge workers can summarize complex documents in seconds. But these are surface-level changes. At a deeper level, we must reconfigure the foundational elements of our businesses: how we collaborate, how we lead, and how we grow. This transformation is not solely technical; it is also largely psychological and managerial. As AI agents become embedded in daily workflows, human employees will experience a shift in identity, agency, and expectations. Leaders must therefore rethink management science itself. We must design new models for onboarding, training, and career development—not just for people, but for AI agents as well.

Much like humans, AI agents will require “career paths” and governance frameworks, and mapping out what role they will play, how they can be best utilized and where they should be deployed will become a part of the management process. We must also prepare our human teams to work alongside virtual colleagues who are more efficient, scalable, and can work 24 hours a day 7 days per week. And, unless we turn them off, they will never quit or retire. 

To navigate this shift, we must equip employees with the skills to manage AI responsibly. This includes the ability to communicate, delegate, and supervise. In a world where anyone can spin up a number of virtual coworkers, with the main constraint being cost, the concept of individual contributor is shifting into one of the player-coach. 

Supervision is key to this evolution. We must ensure that the one who delegates has the ability to check the quality of the work being created by an AI. Imagine an airline that, because of the introduction of the autopilot with auto-land and auto-take-off features, decides to fill some of the flights with only junior pilots. Would we sense the same level of safety and quality control? Only if we felt the junior pilots were properly equipped to supervise. 

Ultimately, cultural transformation in a period of such sharp technological advancement is about more than adopting new tools. It is about forming a new generation of leaders and accelerating their path to experience, equipping them with managerial skills from the outset and leveraging their innate familiarity and proficiency with this new technology.

Today, technology change is ahead of human change. It’s easier to change software and AIs than it is to rewire the human brain, to break old habits and create new skills. Non-AI natives—most of us—have possibly the most challenging task of all: to pass the baton to a new generation of humans entering the workforce and equip them with the skills necessary to manage something that the current generation does not fully understand. All this, without the luxury of time. 

The opinions expressed in Fortune.com commentary pieces are solely the views of their authors and do not necessarily reflect the opinions and beliefs of Fortune.

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