A considerable part of a project manager's week goes into updating plans, checking progress, preparing reports, and keeping stakeholders informed. These tasks are necessary, but collecting and organizing information can take more time than working out what it actually means.
I think planning and reporting are where AI's impact on project management is most immediately visible. Producing an initial work plan, extracting actions from meeting notes, or turning updates from several sources into a report is becoming easier. The more interesting question is which skills matter when those tasks take less time.
Give an AI system a project scope and it can suggest work packages, dependencies, and a tentative schedule. That can be a useful starting point. Whether the plan can actually be delivered is another matter.
A task list rarely tells you how many other projects a team member is supporting, when a critical decision can realistically be made, or whether a supplier will deliver as promised. Assessing a plan requires an understanding of the organization, its resources, and its constraints.
Planning skills are not disappearing. What changes is where the project manager spends time: less on producing the first draft, more on questioning assumptions, identifying dependencies, and agreeing on a workable plan.
Reporting is changing in much the same way. AI can summarize progress updates, group unfinished tasks, and assemble a status report. But a well-formatted report does not necessarily mean a project is in good shape.
Most tasks might be complete while the few that remain are blocking the launch. Or the schedule might look fine even though an important decision has been pending for weeks. Recognizing these situations takes more than repeating the data. It requires interpreting the relationships behind it.
To me, the opportunity is to spend less time assembling reports and more time thinking about what they reveal. That only works if the underlying information is current and trustworthy.
If we are talking about a new skill, it should mean more than writing effective prompts. Project managers need to understand what data an AI system uses, where it may fail, and which assumptions shape its recommendations.
Generating a risk register may be easier, but spotting the critical risk missing from that list still matters. Likewise, a delay prediction based on historical data is not automatically a reliable assessment of today's circumstances.
There is a distinction between technical expertise and critical judgment. Not every project manager needs to build AI models. They do need to understand the limits of the systems they use and avoid treating generated output as a decision in itself.
A 2026 study by Costanza Mariani, Mauro Mancini, and Kirsi Aaltonen approaches this question from another angle. The researchers asked 35 project and program professionals to prioritize AI literacy alongside established project management competencies.
They identified four distinct perspectives: analytical judgment and decision-making; relationships and team coordination; traditional project control and execution; and AI literacy with data-oriented skills.
One interesting finding is that the AI-focused perspective was not the most prominent among participants. Time and cost management received positive rankings across the perspectives, while no AI-related statement emerged as a universal point of agreement.
That does not mean AI skills are unimportant. It suggests that professionals do not yet prioritize them in the same way alongside their existing responsibilities. The study examines viewpoints in a specific group; it should not be read as a ranking that represents every project manager.
Better planning and reporting tools do not remove the human decisions that move a project forward. Resolving competing priorities, clarifying an ambiguous expectation, or understanding why a team is struggling cannot be solved by producing more detailed data alone.
I do not see communication, negotiation, change management, and leadership as alternatives to AI. If routine work takes less time, there may be more room for these skills. But that is not automatic. How the saved time is used depends on the organization and the choices of the project manager.
AI is not replacing the project manager's entire skill set. It is changing how some of those skills are applied. As drafting plans gets easier, judging whether they are realistic matters more. As reporting gets faster, recognizing the issues behind the numbers matters more.
I would therefore put more emphasis on data literacy and critical evaluation than on mastering any single AI tool. Those capabilities are more useful when combined with sound planning, risk management, and communication skills.
A project manager's value should be judged less by the number of reports produced and more by the problems spotted early and the decisions those reports helped inform.
Mariani, C., Mancini, M., & Aaltonen, K. (2026). Mind the gap: Project managers, AI literacy, and the future of project management competences. Project Leadership and Society, 7, 100219. https://doi.org/10.1016/j.plas.2026.100219
Cover photo by Vitaly Gariev on Unsplash.