AI as a collaboration system
AI is changing the way project managers work. But for many professionals in the social impact sector, the real question is not simply how to use new tools. It is how to use them responsibly, strategically, and in ways that strengthen human judgment.
That was one of my biggest takeaways from Humentum’s AI for Project Managers: A Practical Masterclass. Across six live sessions, the course brought together project management practice, Project DPro thinking, and hands-on AI experimentation to help participants explore how AI can support the full project lifecycle.
I was especially interested in what this means for the way organizations make decisions, interpret evidence, communicate progress, and stay accountable to the people and communities they serve.
What stood out most was that the course did not present AI as a shortcut or a replacement for professional expertise. Instead, it framed AI as a collaboration system that can help professionals think more clearly, structure information, test assumptions, and make better decisions, while keeping ethics, accountability, and context at the center.
What was most different about this course compared to other AI trainings?
The biggest difference was that the course was not about “AI hacks.” It was about project leadership.
From the beginning, the facilitator was clear that AI should not be treated as a decision-maker, a context owner, or a substitute for professional accountability. AI can help draft, summarize, structure, and surface options, but humans still decide, explain, commit, and remain accountable.
How did the course use Project DPro?
Many Project Managers in the humanitarian and development sector might be familiar with Project DPro (Project Management for Development Professionals), a project management methodology designed by and for the sector. Here, Project DPro gave the course a practical structure. Instead of discussing AI in the abstract, each session connected AI use to a specific phase of the project lifecycle, from identification and definition to setup, planning, implementation, closure, sustainability, and learning.
How did the course address governance, planning, and implementation?
The course made clear that AI can support project management across the lifecycle, but only if professionals remain disciplined.
In the project setup session, we looked at governance, roles, and responsibilities, early risk identification, and decision pathways. AI can help draft governance options, identify missing roles, and stress-test assumptions. But it cannot assign authority or override institutional reality.
That was one of the most useful reminders from the course. Governance has to reflect real capacity, real decision rights, and real stakeholder expectations. AI can help us think through complexity, but it cannot remove the need for human judgment.
What is your biggest takeaway?
For me, the biggest takeaway was that responsible AI use is not a side issue. It is part of professional competence.
AI can help us work faster, but speed without judgment can cause harm. This is especially important in mission-driven organizations, where project information often involves communities, partners, donors, and sensitive realities.
The course helped me see AI not as a replacement for project leadership, but as a way to strengthen it.
Ready to strengthen your project leadership in the AI era?
Sign up for Humentum’s AI for Project Managers: A Practical Masterclass.
As a strategic communications and partnership leader, much of Alexandra Anda’s work sits close to project delivery: translating evidence into stories, supporting donor-facing reporting, coordinating across teams, and helping organizations explain complex work clearly and responsibly.
Alex has 15+ years of experience driving audience growth, shaping high-stakes narratives, and strengthening stakeholder engagement across global development, media, and mission-driven organizations.