Benefits of Adopting Generative AI for Project Management
Recap of “The Immediate Benefits of Adopting Generative AI for Project Management” | June 25, 2024 | https://www.projectmanagement.com/videos/983099/the-immediate-benefits-of-adopting-generative-ai-for-project-management
Featuring: Riam Chazbek - PMI Lebanon Chapter, Deeksha Singh - PMI South Africa Chapter, Rupal Bhandari, Edivandro Conforto - Ph.D., and Deeksha Singh Singh
This PMI session focused on how generative AI is already being used in project management, what separates effective adopters from casual users, and the role organizations play in enabling meaningful adoption. Rather than centering on future speculation, the discussion emphasized current, practical benefits and real-world behavior from project professionals.
The session was anchored by PMI’s latest research on generative AI adoption in project management and featured perspectives from practitioners working in PMOs, consulting, and large enterprises across different regions.
PMI research focus and structure
PMI’s thought leadership team framed the discussion around findings from one of its first major research efforts focused specifically on generative AI adoption within project management, rather than AI in general.
The research examined four core questions:
How high adopters of generative AI are embracing change
What performance and productivity benefits they are experiencing
How organizations are enabling adoption
What differentiates organizations that actively support adoption from those that do not
From this research, PMI identified two clear groups:
Trailblazers: project professionals using generative AI extensively across projects and tasks
Explorers: those experimenting cautiously or using AI only occasionally
Trailblazers were not just using AI in more projects - they were using it more deeply within individual projects, across multiple activities.
Benefits observed among high adopters
The research showed consistent advantages for trailblazers compared to explorers, particularly in:
Productivity and efficiency
Problem-solving capability
Creativity and brainstorming
Collaboration across technical and non-technical teams
More notably, trailblazers reported better outcomes in traditional project performance areas:
Cost
Schedule
Scope
Quality
The takeaway was clear: broader and deeper experimentation with generative AI correlates with stronger project results.
How generative AI is being applied in practice
PMI introduced a simple adoption framework described as the three modes of AI use:
Automating low-value, repetitive work
Assisting with analysis, drafting, and communication
Augmenting decision-making, judgment, and complex thinking
Early adoption often starts with simple tasks such as summarizing meetings, drafting reports, translating content, or creating schedules. More advanced use cases include decision support, risk analysis, scenario exploration, and resource planning.
A key message was that effective adoption does not stop at simple automation. Trailblazers actively experiment across different types of tasks, not just different projects, and refine their own use cases based on what improves their work.
Characteristics of trailblazers
Panelists described trailblazers less in terms of technical expertise and more in terms of behavior and mindset. Common characteristics included:
Curiosity and willingness to try new approaches
Persistence through early frustration
Comfort with learning through failure
Openness in sharing both successes and mistakes
Strong ethical awareness and empathy
Trailblazers were described as people who actively push boundaries while remaining grounded in real-world project needs.
Organizational enablement matters
A major theme throughout the session was that individual effort alone is not enough. Trailblazers are far more likely to exist in organizations that actively enable AI adoption.
Organizations that support adoption tend to have:
A clear AI strategy aligned to business goals
Governance, policies, and guardrails
Defined expectations for how AI should be used
Training, education, and access to tools
PMI’s research found that trailblazers are significantly more likely to work in organizations with these elements in place. Without them, even motivated project managers face barriers to effective adoption.
Panelists emphasized that PMOs and project leaders can play a key role by integrating AI tools with existing systems, creating learning communities, sharing use cases, and helping leadership understand where AI adds value.
Culture, safety, and learning environments
Beyond formal structures, the panel highlighted the importance of culture. Effective adoption depends on environments where:
Experimentation is encouraged
Failure is treated as learning
Knowledge sharing is normalized
Employees feel safe asking questions
Communities of practice, show-and-tell sessions, and peer learning were repeatedly cited as practical ways organizations can accelerate adoption without waiting for perfect solutions.
Tools, learning, and getting started
While common tools like ChatGPT and Copilot were frequently mentioned, the discussion emphasized that tools matter less than how they are used. Learning paths varied, but most panelists cited a mix of:
Curiosity-driven experimentation
Social learning through peers and communities
Trusted sources like PMI research and tools
The consistent advice was simple: stop waiting for complete certainty and start practicing.
Closing message
The session closed with a strong call to action. Generative AI is not optional or temporary. Organizations and professionals who invest early, experiment often, and learn collaboratively will move ahead. Those who delay risk being overtaken - not by AI itself, but by peers who know how to use it effectively.
Personal Reflection
What stood out to me most was how strongly the discussion tied success with generative AI to behavior rather than technology. Curiosity, resilience, and a willingness to experiment mattered far more than picking the “right” tool.
I also found the emphasis on organizational responsibility validating. Individual project managers can only go so far without clear strategy, governance, and psychological safety. Adoption accelerates when experimentation is supported, rewarded, and shared openly.
Overall, this session reinforced that generative AI is already reshaping project work in practical ways. The real differentiator is not access to AI, but how intentionally it’s used - and whether project professionals are willing to practice, learn, and adapt in real time.