Tomas Chamorro-Premuzic

For most of the past decade, discussions about AI and jobs have focused on the impact of entry-level roles. Most notably, we worry about call-center agents being replaced by chatbots, analysts displaced by algorithms, or junior coders assisted into obsolescence by agentic AI and synthetic coworkers.

Yet this framing misses a more subtle shift: AI is not only changing the bottom of the org chart, but also reshaping the top. Though the change is quieter and more structural or configural, senior leadership, executive, and C-suite roles are being redefined just as profoundly. For example, there’s little risk that the CFO role disappears, but also a very small probability that the attributes, skills, and behaviors that made CFOs successful or effective in the past continue to make them successful or effective in the future. The implications of this are profound, forcing organizations to hire and promote senior leaders less for what they have done in the past, and more for what they could do in the future.

AI Is Both a Tool and a Leadership Challenge

To understand the broad impact of AI on leadership, it is useful to shift from seeing AI as a tool (or range of tools) to a leadership challenge—if not the defining leadership challenge of our times. The key question is not so much about the leaders who can help organizations with the technical or tactical aspects of AI (including implementing it and driving adoption), but rather about the range of skills, values, and behaviors leaders need to display in order to navigate the AI age, calling for a new phase or age of leadership altogether.

This should not be surprising. Every major technological wave has eventually altered leadership itself. The railroads produced the professional manager. Electricity produced the modern corporation. The internet created platform CEOs who in turn created digital ecosystems rather than brick-and-mortar factories.

AI is doing something similar, changing what leaders must know, what they must do, and even what leadership roles must exist (or not) to address the functional and psychological coordination needs of modern organizations and work in the human-AI age.

As I’ve argued, AI affects leadership in three broad ways. First, AI is forcing leaders to decide where to automate, how to augment human skills, how to govern data, how to redesign work, and how to capture value from AI rather than simply deploying it—not to mention considering the broad societal and ethical consequences of becoming an AI-organization. Strategy used to be about markets and competitors. Now it is also about algorithms and agents. Firms that fail to make good calls about AI risk becoming irrelevant, much as companies that ignored the internet in the 1990s disappeared.

Second, AI is commoditizing expertise. For much of the 20th century, leaders advanced because they knew more than others, which they typically showed through Ivy League MBAs, past experience, and a track record of results on conventional KPIs. The CFO understood finance. The COO understood operations. The CEO accumulated decades of experience.

Today, much of that expertise is available on demand. Models can analyze financial scenarios, optimize supply chains, and synthesize market research faster than any individual. When hard skills and experience become easier to replicate, the differentiating qualities of leaders shift. Empathy, curiosity, learning ability, integrity, and self-awareness become more important. These traits are harder to automate and more valuable in coordinating humans with machines. The best leaders of the AI era will not be those who know the most, but those who learn the fastest and judge most wisely.

Third, AI changes organizations themselves. It alters culture by increasing the need for transparency, adaptability and speed. It alters structure by flattening hierarchies and expanding spans of control. It alters coordination by enabling real-time decision-making. If work is reorganized around data and algorithms, leadership roles must be reorganized as well.

Most importantly, if AI penetration continues to increase even incrementally (not exponentially), AI will soon become the new Wi-Fi, smartphone, or electricity: a fundamental necessity and universal feature, rather than an opportunity for differentiation. In this world, culture will probably become the biggest competitive advantage of organizations.

Evolution of the C-Suite

Nowhere is this clearer than in the C-suite. Even before AI, these roles were evolving. Fifty years ago, many firms had chief administrative officers or chief production officers. Some had chief planning officers during the strategy boom of the 1970s. Today those titles are rare. In their place we have already seen titles such as chief digital officers, chief customer officers, chief risk officers, chief sustainability officers, chief diversity officers, chief data officers, chief AI officers, chief ethics officers, and chief transformation officers go from niche or rare or gimmicky to the new normal. Each title reflects a new priority, much as geological layers reveal past climates.

Some of these roles are rising because new capabilities are needed. The chief AI officer exists because AI strategy is now inseparable from business strategy. The chief data officer reflects the recognition that data quality determines model quality. The chief ethics or trust officer emerges from the reputational and regulatory risks of AI. We also see hybrid roles such as chief product and technology officer, chief people and culture officer, or chief growth officer. The boundaries between functions blur as organizations become more integrated.

To illustrate this with our data, we analyzed a representative sample of more than 5,000 open executive roles at Russell Reynolds, reflecting broader global demand for senior talent. Over the past decade, the findings point to a clear pattern: Technology has been a primary force reshaping the composition of the C-suite. The shifts between 2019 and 2025 are particularly revealing (see chart):

As the above chart highlights, there have also been some clear downwards trends, notably chief digital officers (now increasingly subsumed into the broader executive tech roles at the top of the list), and chief diversity officer (a reflection of changes in political attitudes on the subject, as well as a trend for firms to absorb ESG and DEI into their core strategy, or simply ditch these initiatives).

Same Same, but Different

Perhaps the most interesting shift is not in titles but in the content of C-suite roles. To cite an obvious example, CEOs today spend far more time on technology and talent than a decade ago. The COO is expected to understand automation and analytics, as much as logistics or operations. The CFO must interpret data science and scenario modeling rather than just accounting. The CHRO increasingly oversees workforce analytics, AI-enabled performance management and coaching, agentic AI for talent, and skills architecture—to the point that the recently relabeled “People and Culture” function could now be relabeled “People, Machines, and Culture.” The job descriptions look similar, the role titles remain the same, but the skill constellations underneath them have changed because those same leaders are now adding value in a different way, and some of their previous talents and skills have now been delegated to AI.

For example, if we look at the CFO role (the most common C-suite role after CEO in our dataset), we see a clear trend of AI and tech-related competencies becoming a common or normative feature in job descriptions by 2025, while being mostly absent in 2019. Here are the main attributes that signal this change. These competencies reflect the increasing importance of data-driven decision-making, digital transformation, and technological fluency for modern CFOs.

Trending up

• Data analytics and interpretation
• Artificial intelligence and machine learning
• Cloud computing and security
• Emerging technologies and trends
• Digital marketing and e-commerce

Trending down

• Technical accounting
• Auditing
• Financial regulations
• Language skills
• Cultural awareness

This suggests that the CFO profile is now much more intertwined with other functions (especially tech and marketing) and that the attributes that used to make executives credible as a CFO are now table stakes. Instead, what makes CFOs valuable now is how well they can augment their judgment and expertise with data and AI. In essence, more CEOs are moving from reporting to predicting, from control to influence, and from technical expert to data-driven strategist.

Likewise, the CHRO of the future may look quite different from the CHRO of today, and especially yesterday. If the CFO has moved from reporting to predicting, the CHRO is moving from administering people to architecting human–machine systems.

Trending up

• Workforce analytics and people data science
• AI-enabled talent assessment, selection, and development
• Skills architecture and dynamic workforce planning
• Human–AI collaboration design (who does what: human vs machine)
• AI-driven performance management and continuous feedback systems
• Organizational network analysis and productivity diagnostics
• Behavioral science and experimentation (nudges, A/B testing for talent)
• Ethical governance of AI in talent decisions (bias, fairness, transparency)

Trending down (or becoming table stakes)

• HR operations and policy administration
• Employee relations and compliance
• Compensation and benefits design
• Traditional performance management cycles
• Training delivery and program ownership
• Cultural stewardship as a largely qualitative, intuition-driven exercise

Note: these skills are not disappearing, but they are increasingly being automated, standardized, or outsourced, and are therefore less differentiating.

This shift suggests that the CHRO profile is no longer primarily about protecting employees or managing processes, but about optimizing the interface between people, data, and machines. In the same way that finance has become inseparable from analytics, HR is becoming inseparable from technology and science. The most effective CHROs will not be those who best understand HR, but those who best understand talent as a system, combining psychology, data, and AI to drive performance. In essence, CHROs are moving from supporting the business to engineering it, from measuring engagement to predicting performance, and from managing people to orchestrating human and artificial intelligence at scale.

The Evolution of Boards: From AI Adoption to Agentic Governance

If the evolution of the C-suite reflects a shift from expertise to judgment augmented by AI, the evolution of boards reflects something even more profound: a shift from oversight to augmented decision-making systems. This highlights a maturity curve of governance itself, moving from passive adaptation to active redefinition of what a “board” even is. Consider the below pyramid as an illustration of board evolution vis-à-vis how boards of directors are being impacted by AI, and what that may look like in the near future.

At the base of the pyramid sits what we might call the Luddite phase, where boards continue to operate as they always have, treating AI as peripheral or irrelevant. These boards rely on traditional governance rituals, static reporting, and human-only deliberation. In practice, this is not stability, but slow obsolescence: As management teams become more AI-enabled, these boards risk becoming increasingly disconnected from how value is actually created.

The next stage, which quickly becomes table stakes, is where boards begin to use generative AI as a basic hygiene factor. Directors use AI to summarize materials, stress-test assumptions, and prepare for meetings. This is the governance equivalent of email or spreadsheets: not differentiating, but necessary. By 2027, as our chart suggests, this level of adoption is likely to be expected rather than noteworthy.

From there, we move into incremental progress, where boards become explicitly AI-ready. This is not just about using tools, but about changing how governance works. Boards start integrating AI into core processes: scenario planning, risk modeling, CEO evaluation, and capital allocation decisions. Crucially, directors begin to rely on AI not just for efficiency, but for augmenting judgment, creating a hybrid model of human–machine deliberation.

The real inflection point comes with the disruptive phase: agents as board members. Here, AI moves from tool to actor. Agentic systems participate in board processes, contributing analyses, generating alternative strategies, and in some cases acting as independent “voices” in decision-making. These agents may not have formal voting rights initially, but they begin to shape outcomes in meaningful ways. At this stage, governance becomes multi-intelligence, blending human experience with machine cognition.

Beyond this lies a more radical, and somewhat uncomfortable, scenario: the dystopian endpoint, where human boards are largely displaced. In this world, governance is delegated almost entirely to AI systems optimized for efficiency, risk minimization, and performance. While this may be theoretically appealing from a purely economic standpoint, it raises profound concerns about accountability, ethics, and legitimacy. Who is responsible when decisions are made by systems no one fully understands?

Finally, at the apex of our pyramid sit the unknown unknowns. This is a recognition that the most transformative changes are, by definition, those we cannot yet anticipate. Just as few predicted the platform economy or the rise of generative AI a decade ago, the future of governance will likely include forms we cannot yet conceptualize. This is less a category than a reminder: We can only see what is here and are limited by our own experience and intellectual constraints. Likewise, some of these trends, including the more future-oriented scenario of AI agents representing the majority or entirety of the board, are already a present reality today, though mostly in smaller, younger, AI-native companies. In that sense, as science fiction author William Gibson famously noted, “the future is already here, just not evenly distributed.”

Looking Ahead

If the trajectory of boards is any indication, the more interesting question is not whether AI will populate the C-suite, but how much of the C-suite will remain populated by humans.

At one extreme, it is plausible to imagine a future in which certain executive roles are partially or even fully automated. Not in the sense of humanoid CEOs replacing people, but in the sense that key elements of decision-making, forecasting, and coordination would be delegated to algorithmic systems or agentic AI. We already see early signals of this in areas like pricing, capital allocation, hiring, and marketing, where models often outperform humans in consistency, speed, and scale. In that world, the C-suite does not disappear, but it becomes thinner, more fluid, and more hybrid, with humans increasingly acting as curators, editors, and arbiters of machine-generated insights.

More realistically, the near-term future will resemble the middle of our board pyramid: hybrid leadership architectures. Here, AI does not replace executives but becomes embedded in their roles. A CFO is inseparable from predictive models. A CHRO operates through talent intelligence platforms. A COO relies on real-time optimization engines. Leadership becomes less about owning decisions and more about orchestrating systems that produce decisions. The key risk is not replacement, but over-delegation, where executives outsource judgment to systems they do not fully understand.

This shift may also give rise to new roles, though many will probably be transitional—an attempt by organizations to catch up with the agile, fluid, and unpredictable impact of AI on work, talent, and leadership. We are likely to see new titles emerging, such as:

• Chief AI governance officer, overseeing model risk, ethics, and compliance
• Chief augmentation officer, redesigning work around human–AI collaboration
• Chief resilience officer, integrating cyber, geopolitical, and operational risk
• Chief platform or ecosystem officer, managing human and machine contributors across boundaries
• Chief humanist officer or humanist-in-residence, tasked with safeguarding meaning, culture, and human experience in increasingly machine-mediated organizations

Admittedly, the last role in the above list may sound indulgent or even whimsical, but it addresses a very real gap. As organizations become more efficient, optimized, and data-driven, they also risk becoming sterile, transactional, and psychologically disengaging. Someone needs to ensure that work remains not just productive, but meaningful; not just efficient, but humane. In a world where machines optimize for performance, humans must still curate purpose, identity, and trust.

At the same time, the structure of leadership itself will evolve. The traditional, fixed C-suite may give way to more modular and networked leadership systems, where teams assemble dynamically around problems rather than reporting lines. As AI improves information flows, decision rights will migrate closer to where expertise resides, reducing the need for hierarchical escalation. Senior leaders will spend less time approving and more time interpreting, coaching, and challenging both humans and algorithms.

Of course, there is a more cynical interpretation. Organizations have always been good at renaming problems rather than solving them. Adding a chief AI oficer does not guarantee an AI strategy, just as adding a chief innovation officer rarely produces innovation. The real transformation lies not in titles, but in capabilities, incentives, and culture. Still, titles are a useful signal. They reveal what organizations believe matters. Today, the emphasis is on data, technology, ethics, and talent. Tomorrow, it may shift again, perhaps toward human sustainability, cognitive well-being, or cultural coherence.

The broader lesson is this: leadership is becoming less about individual capability and more about system design. The executives who will thrive are not those who compete with AI, but those who configure environments in which humans and intelligent machines outperform either alone.

Source: HBR