AI adoption outpaces leadership change, report says

The Conference Board has published research on how artificial intelligence is reshaping leadership inside organisations. The report finds AI adoption is outpacing changes to leadership structures.

Based on a survey of 775 international leaders and 20 interviews, the study points to a widening gap between the spread of AI in day-to-day work and managers' readiness to oversee it. Nearly two-thirds of respondents said their organisations already use AI across several functions or have deeply integrated it into daily operations.

Even so, 37% said ownership of AI-related leadership decisions remains inconsistent or unclear. The report says this is putting pressure on leaders, who must review larger volumes of AI-generated work while maintaining quality, judgement and staff development.

Governance appeared stronger in organisations where AI is more established. Among respondents at organisations where AI is highly integrated, 85% reported clear ownership or formal governance, compared with 26% at organisations still exploring the technology.

Management pressure

The findings suggest AI's impact will be felt unevenly across management layers. Only 4% of leaders expect a reduction in C-suite roles, while 38% anticipate fewer middle managers and 36% expect reductions in frontline managers and project leaders.

That points to sharper disruption in the management layers between senior executives and frontline staff. The report also warned of new bottlenecks, fatigue and the risk of what it described as "work slop" as leaders assess growing volumes of machine-assisted output.

"AI is fundamentally transforming what it means to be an effective leader," said Diana Scott, US Human Capital Centre leader at The Conference Board.

"Success will depend less on managing tasks and more on guiding teams through change, building trust, and rethinking how work gets done. Organisations that focus only on AI skills risk overlooking the broader leadership capabilities needed to turn the technology into meaningful business value," Scott said.

Leadership models

The research also found limited support for conventional top-down leadership in an AI-enabled workplace. Just 5% of respondents said a traditional leader-centred model is best suited to the AI era.

Instead, 36% favoured a hybrid leadership model in which leadership input and responsibilities are shared. Another 30% backed distributed leadership, where responsibilities are spread across levels, functions and roles, while 24% preferred shared leadership, where individuals take the lead according to the expertise required.

The figures indicate that many leaders expect decision-making to move beyond formal hierarchies as AI changes workflows and accelerates execution. The report says companies should examine not only where authority sits but also how accountability is maintained when work is shared more widely.

"AI is changing how work gets done faster than organizations are changing how they're structured," said Erka Amursi, principal researcher, Human Capital, at The Conference Board.

"Rather than simply eliminating management layers, companies should rethink how decisions are made, responsibilities are shared, and performance is measured to ensure their organizational structures support new ways of working," Amursi said.

Skills gap

On leadership skills, 52% of respondents ranked critical thinking and decision-making as the most important enduring capability in the AI era. Among competencies expanded by AI, continuous AI literacy and knowing when human judgement should override AI tied for first place at 40% each.

Another 33% identified redesigning work for human-AI collaboration as an important competency. That suggests organisations are beginning to treat job design and process redesign as management tasks rather than purely technical projects.

The study also found stronger demand for practical training than for broad awareness programmes. Some 72% of leaders said they favour hands-on practice with AI tools over more traditional development approaches.

Recommendations for Chief Human Resources Officers included clarifying AI accountability and decision rights, balancing AI speed with human judgement, testing more flexible leadership models and integrating AI into workforce planning. The report also called for more focus on human skills including systems thinking, risk awareness, coaching, empathy and the ability to provide context.

It added that AI development should be tailored to each leadership level, with role-specific learning tied to the actual work leaders do rather than generic training.