AI Is Exposing Why Organizations Promote The Wrong Leaders

Initially published on Forbes May 25, 2026

Organizations are redesigning work for AI while still promoting managers for a pre-AI world. The criteria that built their leadership pipelines may now be working against them.

The traits that helped managers succeed in a pre-AI workplace — confidence, visibility and decisiveness — were optimized for environments where leaders were expected to project certainty, move fast, command attention and personally drive execution.

But those same traits may now be blocking the kind of human agency AI transformation depends on. Confidence without humility can become a failure to question AI outputs or distinguish fluency from accuracy. Decisiveness without reflection can scale flawed decisions across systems before anyone catches the error. Visibility and executive presence matter less when leadership increasingly happens inside workflows, systems and decision design.

In AI-enabled environments, these gaps no longer affect isolated teams or moments. They become embedded into how organizations operate at scale. This is why so many AI transformation efforts are struggling to translate AI investments into measurable business outcomes.

To realize the expected AI gains, leadership models need to catch up with what the technology requires.

Why AI Is Exposing The Wrong Leadership Traits

Most organizations still identify leadership potential through a familiar set of signals: who speaks up in the room, who presents ideas with confidence, who is visible to senior stakeholders, who appears decisive under pressure. These traits create the impression of leadership. They are easy to observe, easy to reward and easy to scale through promotion systems.

New research from Hogan Assessments suggests these are not necessarily the traits leadership now requires. Drawing on responses from 9,794 employees across 25 countries, the study examined what people actually value in a leader from the perspective of those most affected by it: the people doing the work. The result is striking. The study found zero overlap between the competencies executives score highest on and the competencies employees say they most want from leaders.

Executives score highest on inspiring others, competing with others, presenting to others, taking initiative and driving innovation.

Employees want effective communication, sound decision-making, accountability, integrity and leadership ability.

These two lists represent fundamentally different ideas about the kind of leadership organizations now require.

The Leadership Skills Organizations Actually Need

What employees want from leaders matters not simply because employees prefer it, but because these are the qualities that enable organizations to transform successfully in AI-enabled environments.

Trust affects whether people adopt and challenge AI systems appropriately. Judgment affects whether people challenge assumptions, validate outputs and explore alternatives rather than simply taking AI recommendations at face value. Clarity affects execution across distributed systems where humans and algorithms increasingly work together.

In other words, the qualities employees are asking for are increasingly the same qualities organizations need in order to transform effectively with AI.

AI increasingly asks managers to make decisions based on outputs they may not fully understand, manage human-AI collaboration and exercise judgment in conditions of genuine uncertainty. They must define when human oversight overrides automated systems and build trust in tools most employees cannot fully see inside.

The Hogan data make clear that employees already understand what this moment requires. Across 25 countries and multiple industries, what workers want from leaders is strikingly consistent. Clear communication. Sound judgment. Accountability. Integrity. These are not aspirational preferences. In an environment where people are being asked to rely on AI outputs, follow decisions shaped by algorithms and work in systems where accountability is less visible, these qualities are functional requirements. They determine whether the system works.

How AI Is Changing Leadership And Management

The shift AI is creating in leadership and management is not incremental. In AI-enabled organizations, work is moving from execution to orchestration. Decisions are continuous and distributed. Authority is increasingly shared with algorithms. Teams are no longer simply groups of people producing outcomes together. They are combinations of humans and systems, and managing that combination requires a different cognitive and interpersonal profile than the one most leadership development and promotion processes were designed to surface.

Therefore, the leadership skills organizations now need are not the same traits many promotion systems were designed to reward.

Specifically, AI environments reward judgment over confidence. Managers increasingly make decisions based on AI-generated outputs, incomplete information and systems they do not fully understand. What matters is not decisiveness alone, but the ability to interrogate outputs, challenge assumptions and apply judgment before flawed decisions scale across systems.

It also rewards systems thinking over visibility. Traditional leadership rewards being seen, heard and recognized. AI leadership increasingly requires designing workflows, defining decision boundaries and aligning human and machine contributions. This work is largely invisible. It does not show up in meetings. It shows up in outcomes over time.

And AI shifts management away from individual performance and toward system performance. Value increasingly comes from how well humans and AI work together, not how strong any individual contributor is. Managers create impact by enabling others, integrating AI into workflows and building environments where people feel safe questioning outputs and exercising judgment.

In AI-mediated environments, trust becomes operational, especially as employees are increasingly asked to work alongside systems shaping decisions they cannot fully see or verify. People are being asked to rely on outputs they cannot fully verify, follow decisions shaped by processes they cannot see and work in systems where accountability is less visible. Inspiration is no longer enough. Clarity, consistency and psychological safety are what make the system function.

How Organizations Need To Rethink Leadership In The AI Era

None of this changes on its own. Promotion systems reward what they were designed to reward. If organizations continue selecting for visibility and self-presentation, they will continue elevating profiles that are poorly matched to the demands of AI-enabled work.

Changing this starts with redefining what potential looks like. The question shifts from who stands out to who enables others to perform. Who creates clarity under uncertainty. Who exercises sound judgment when the inputs are ambiguous. Who can design the conditions in which people and intelligent systems work effectively together.

It requires changing what gets measured. Development programs that add AI literacy to existing leadership frameworks miss the deeper point. The issue is not whether leaders understand the technology. It is whether they can operate effectively in environments where they no longer control all the variables, where their role is to create conditions for performance rather than deliver it directly and where judgment matters more than execution speed.

Organizations that fail to make this shift may continue investing heavily in AI while seeing limited adoption, weak trust and disappointing returns.

The Hogan research frames leadership not as a position but as the ability to build and maintain a high-performing team. That definition has always been more accurate than the one organizations actually use. In the AI era, the cost of the gap between those definitions is no longer theoretical.

Organizations are trying to transform with leaders they would not choose if they were designing for the world they are now entering.