When Artificial Intelligence Rewrites Leadership from the Top
There is a narrative that organizations repeat with comfort: artificial intelligence will displace mid-level analysts, customer service agents, junior programmers. It is a narrative that unsettles just enough to seem honest, but not so much as to threaten those who tell it. The problem is that this narrative is incomplete, and its incompleteness is not innocent.
What is happening at the top of organizations is structurally more significant than what happens at the base, precisely because it is more silent. There are no headlines about the CFO displaced by an algorithm. There are no union protests over the automation of the executive committee. Nevertheless, data from more than 5,000 open executive positions analyzed by Russell Reynolds Associates between 2019 and 2025 documents a real displacement: not of people, but of the attributes that make those people valuable. That distinction matters more than it appears.
The question that no board of directors should avoid is not whether AI will reach their ranks. It already has. The question is whether the organization has the clarity to understand what is changing and the discipline to act accordingly before the market decides for it.
The End of Leadership as a Warehouse of Knowledge
For most of the twentieth century, executive power was built on a simple premise: whoever knows the most, leads. The CFO accumulated decades of financial knowledge that no subordinate could easily replicate. The COO understood the chain of operations because he or she had lived it. The CEO reached the top with a track record of results that functioned as a certificate of suitability. Credentials — the MBA from an elite institution, the passage through the right companies, the seniority in the function — were not vanity: they were real signals of a scarce asset.
That asset is no longer scarce. Current artificial intelligence models can analyze financial scenarios, optimize supply chains, and synthesize market research with a speed and consistency that no individual can match. This does not mean the CFO is irrelevant. It means that the portion of his or her value that came from knowing more than others has been transferred to systems that do not collect bonuses or ask for promotions.
What remains — what the systems cannot easily replicate — is the capacity to judge well under ambiguity, to sustain trust in moments of pressure, to design the systems within which both humans and machines operate. The analysis of executive profiles by Russell Reynolds documents this shift with precision: in 2025, competencies related to artificial intelligence, data analytics, cloud computing, and emerging technologies appear as normative characteristics in CFO profiles. In 2019, they were practically nonexistent in those same documents. Job descriptions did not change their title. They changed their demands.
This displacement has a strategic consequence that few organizations are processing with sufficient seriousness: the criteria for hiring and promoting executive roles must be reoriented from track record toward learning capacity. Hiring a CFO because he or she mastered accounting close cycles for twenty years may be precisely the mistake an organization cannot afford at this moment. That competency is already automated or on its way to becoming so. What is not automated is that CFO's capacity to build judgment about the systems that now produce the analyses, to detect when the model is wrong, to decide when to trust the machine and when to ignore it.
The Anatomy of Change in Two Roles
The CFO and the CHRO are the two most documented cases in the available analysis, and the most revealing because they represent two functional extremes: the first associated with quantitative precision, the second with the management of the intangible human dimension. That both are converging toward the same direction — greater fluency with data, greater capacity for systems design, lesser dependence on manual processes — is not a coincidence. It is a signal that the transformation respects no functional boundaries.
For the CFO, the change can be described as a displacement from reporting to prediction. The traditional role of the financial director was to safeguard the accuracy of the past: close the books, ensure regulatory compliance, present results to the board. Those functions do not disappear, but they are automated, standardized, and delegated to platforms that execute them with fewer errors and at lower cost. What is not automated is the capacity to interpret the scenario models that now inform capital decisions, to design the parameters within which artificial intelligence operates in the financial function, to maintain accountability for results even when the analysis is produced by a system that no one in the room fully understands.
For the CHRO, the transformation is even more radical because it affects the narrative that has defined the function for decades. People management was built on a humanist premise: administering the employee lifecycle, managing relationships, maintaining culture. That narrative remains valid, but it has ceased to be sufficient. The executive profile organizations are now seeking includes the capacity to design the architecture of interaction between humans and machines at work, to use behavioral analytics in talent decisions, and to ethically govern the artificial intelligence systems that evaluate, select, and develop people. The CHRO who does not understand how a turnover prediction model works has the same problem as the CFO who does not understand a balance sheet: he or she is operating blindly within their own function.
What these two cases reveal is a pattern that applies to the entire executive committee: the differential value of an executive no longer resides in what he or she knows how to do, but in the quality of judgment with which he or she orchestrates systems that do what he or she used to know how to do. That is a profound redefinition, and most organizations have not yet internalized it in their evaluation, succession, or compensation processes.
Boards of Directors Facing a Governance Gap
If the transformation of the executive committee is silent, the situation on boards of directors is more concerning. An analysis by the National Association of Corporate Directors reports that only 14% of boards discuss artificial intelligence at every meeting. Even more relevant: 45% have not included the topic in their agendas at all. These numbers are not simply an indicator of technological lag. They are an indicator of a governance gap with concrete financial and reputational consequences.
Boards exist to oversee strategy, manage risk, and ensure accountability. When artificial intelligence is already informing pricing decisions, capital allocation, hiring, and product development at the companies those boards oversee, the inability to discuss the topic with rigor is equivalent to supervising without reading the financial statements. The analogy is not hyperbolic: artificial intelligence models are producing outputs that affect the economics of the company, and most boards do not have the mechanisms to audit them.
The analysis in the original HBR article proposes a maturity curve for boards that runs from the phase of inertia — where AI is treated as something peripheral — to hybrid governance architectures where artificial intelligence systems actively participate in processes of strategic analysis, without displacing human responsibility over decisions. This curve is not speculative: some companies, particularly those with higher technological intensity, already operate with mechanisms where agentic systems contribute analysis in strategic planning and risk assessment processes.
What makes this progress difficult for traditional boards is not the technology: it is the composition. The profiles that have historically dominated boards — deep sector experience, financial credentials, institutional networks — do not, for the most part, include the capacity to evaluate the quality of an artificial intelligence model, identify its biases, or judge when the recommendation of a system should be rejected. Adding a director with a technology profile partially solves the problem, but it does not resolve it at its root. The governance of artificial intelligence is not a specialized technical competency that is delegated to a single director: it is a collective capacity that must be distributed across the board.
Organizations that are advancing this work well are not waiting to be compelled by regulation or forced by a reputational incident. They are redefining the selection criteria for new directors, building reporting mechanisms that make the use of artificial intelligence in operations visible, and establishing specific oversight committees with clear mandates. They are still a minority.
What the Map of Titles Does Not Show
The proliferation of new executive titles — chief artificial intelligence officer, chief data officer, chief ethics officer, chief transformation officer — carries the risk of becoming the kind of cosmetic reorganization that organizations master when they face pressure to appear agile without actually being so. The history of the chief diversity officer role, which according to Russell Reynolds data is in decline because its agenda was absorbed — or simply abandoned — into the broader structure, is a warning about the distance between a title and a real commitment.
The title of chief artificial intelligence officer does not guarantee an artificial intelligence strategy, in the same way that the title of chief innovation officer did not produce innovation in most of the companies that adopted it. What determines whether the change is real is whether the role has resources, decision-making authority, and the capacity to generate concrete trade-offs in other areas of the budget and executive agenda. A chief artificial intelligence officer without the capacity to tell the CFO that the financial model being used has a confirmation bias, or to tell the CHRO that the performance evaluation system being implemented penalizes profiles that should not be penalized, is a decorative position.
The most honest signal that an organization is processing this change seriously is not in the titles it creates: it is in the titles it modifies and in the competencies it demands from those who are already at the top. When the CEO evaluation process begins to include criteria on the quality of his or her judgment in decisions informed by algorithms, when the CFO succession process explicitly weighs the capacity to design financial analytics systems, when the board of directors has mechanisms to audit the use of artificial intelligence in operational decisions, that is an organization that is taking the change seriously.
What most organizations are doing is more comfortable: adding a new position to the organizational chart, including "artificial intelligence" in job descriptions, declaring that digital transformation is a strategic priority. Those actions are not useless, but they are insufficient if they are not accompanied by the more difficult decision: accepting that the criteria defining what makes a person valuable at the top have changed, and acting accordingly even if that means reevaluating those who are already there.
The Leadership That Is Coming Does Not Resemble the One That Works Today
The organizations that will emerge best positioned from this period will not be those with the most sophisticated artificial intelligence models, nor those that have created the most new executive titles. They will be those that have resolved, ahead of others, the most difficult governance problem: how to maintain human accountability over decisions that are increasingly generated, informed, or executed by systems that the humans in charge do not completely understand.
That problem does not have a technological solution. It has a leadership solution. And the leadership it requires is not the kind that knows the most, but the kind that judges best under conditions of growing uncertainty, the kind that designs the systems within which both people and machines operate, the kind that accepts responsibility for results even when it did not make the decision directly.
Organizations that continue to hire and promote executives primarily on the basis of what they did in the past are building the executive committee of the previous cycle. The cycle that is beginning demands something different: less certainty about functional domain, more capacity to move well in territory where the rules are still being written. That capacity is not certified by a track record. It is inferred with judgment, evaluated with rigor, and developed before it is needed — not after its absence has already cost something.
