What does leadership in the age of AI look like in practice?

The AI age is being built on extraordinary computing power, giving machines unprecedented capacity to process information, recognize patterns, generate analysis and take action. As more analysis and execution are delegated to machines, leadership shifts towards the decisions that cannot be delegated.

This emerging model of AI-era leadership places greater value on making sound judgments, reading the context, questioning machine-generated conclusions and taking responsibility for the decisions that follow.

AI can identify options, model scenarios and recommend actions. Leaders must assess those recommendations against real-world consequences. This is where leadership for the era of AI begins. Decision-makers must be willing to interrupt a convincing answer, ask whose interests it serves, and take responsibility for the people who will live with the result.

For that reason, we think AI literacy can no longer sit only with technologists. Policy-makers, executives and leaders across sectors need enough understanding of AI to question its outputs and recognize its limitations.

Equally important is preparing the next generation to work confidently with AI while remaining firmly in command of the decisions it informs.

This is the responsibility of AI-era leadership: to stand between a powerful system and the people affected by its decisions.

AI systems are moving beyond retrieving information or generating content. Increasingly, they can reason across complex inputs, recommend actions and execute tasks across digital environments.

Stanford University’s 2026 AI Index reports that AI agents advanced significantly in their ability to complete tasks in 2025. On OSWorld, which tests agents on real computer tasks across operating systems, accuracy rose from roughly 12% to 66.3%, bringing the leading agent within six percentage points of human performance. Yet even at that level, agents still fail roughly one in three attempts on structured benchmarks.

Greater AI capability raises the stakes for leaders. As AI increasingly supports and executes decisions, leaders must establish clear boundaries for where human judgement, accountability and oversight remain essential. This means defining when human review is required, ensuring appropriate governance over AI-driven actions, and assessing consequences that systems may not fully capture. The goal is to ensure that greater autonomy is matched by clear accountability, responsible governance and meaningful human oversight.

This shift is already changing what organizations value in their people and leaders. The World Economic Forum’s Future of Jobs Report 2025, drawing on more than 1,000 employers representing over 14 million workers across 55 economies, expects 39% of workers’ core skills to change by 2030.

The report also says leadership and social influence, resilience, flexibility and agility and AI, and big data have seen the most substantial increase in importance, with 22, 17 and 17 percentage-point rises, respectively, in the share of respondents identifying them as core skills compared to the 2023 edition of the report.

For government organizations, this shift carries particular significance. In the United Arab Emirates, where the government has set a target to transition 50% of government sectors, services and operations to agentic AI within two years, leaders will increasingly work alongside systems capable of analysis, execution and decision-making.

This places an even greater premium on human judgement. We believe that as AI becomes better at execution and solving defined problems, the human premium will increasingly shift towards defining the problem itself, asking better questions and recognizing challenges before they become crises

This brings us to the next generation of leaders; a question particularly relevant to the World Economic Forum’s Annual Meeting of Global Future Leaders in Dubai.

The leaders gathering there will spend much of their careers working alongside AI systems far more capable than those available today. For them, the leadership challenge will increasingly be one of judgement: deciding how much authority to give these systems, and where human judgment must remain decisive.

So, what does this model of leadership look like in practice? Cognizant and Pearson research identifies four defining capabilities for an AI-era workforce: an AI-native mindset; problem-finding; soft skills and human centricity; and intellectual agility. It brings them together under one broader principle: adaptability.

The UAE offers a practical example through the National Experts Program’s AI track (NEP-AI). This brings together 32 Emirati experts from different sectors for an eight-month journey designed to build deeper AI understanding and connect it directly to their fields, leadership responsibilities and national priorities.

The programme combines intensive learning with mentorship from senior government and industry leaders, sector-specific capstone projects and international study visits to leading AI ecosystems.

Naturally, the focus differs by sector. A policy-maker may focus on regulation, accountability and public trust; an infrastructure expert on resilience and safety. For a business leader, the same technology may raise questions of productivity, investment and competitiveness.

Yet across these different sectors, the underlying leadership challenge is the same. AI will give the next generation of leaders faster analysis and a wider range of options. Their responsibility is to consider questions the system cannot settle: who bears the risk, whose interests are missing and who answers for the outcome.

Ultimately, leadership programmes should prepare people to make those calls. The measure of an AI-era leader will be the decisions they have the courage to question and the consequences they are willing to own.