How project leader roles change with AI (Part 2)
This article is part 2 of our series on Project Management AI (PM-AI). It explores how PM-AI is changing project management roles and what project managers can do to prepare for this change.
In Part 1, we discussed how PM-AI is a disruptive, forcing function brought on by Generative AI. A 2023 University of Pennsylvania (UPenn) research study predicts that 30% of daily PM activities will be replaced by Generative AI. In March, 2023 a Goldman Sachs study identified that 32% of managed jobs were exposed to AI. These two studies among others suggest that PM-AI can automate a significant portion of project management tasks, but not all of them. While this may feel threatening to some project managers, it also frees up PMs to reinvent their roles and focus on areas that matter.
First, we applied 90% of the job activities in the Project Management Institute's (PMI) Talent Triangle to create job categories such as Meetings & Collaboration described later.
Then to determine how PM-AI would change Project Manager job activities, we conducted extensive reviews from various sources such as the 2023 UPenn study, McKinsey, the Project Management Institute (PMI) model for project maturity, PMI’s Triangle of Talent, job skilling classifications from O*Net, academic journals and research and reviews of current and planned AI features in major PM tools such as Jira, Asana, SmartSheet, Notion and conversation intelligence tools such as Fireflies.ai and Versational.ai.
The UPenn study found that generative AI or LLMs (Large Language Models) can automate or replace 30% of tasks in Job Zone 4. This is the job zone that requires Projects Managers to have higher levels of PM expertise and experience. This study along with O*Net and PMI’s Triangle of Talent suggest that AI can automate a significant portion of project activities, but it is unlikely to automate all of them. For example, communication and collaboration is typically the most time-consuming activity for project managers. PM-AI can reduce the time spent in meetings each week by 35% including associated follow-up, documentation, commenting and stakeholder communications. Figure 1 shows the percentage of time spent typically spent on job activities. The time spent can vary significantly by project type, stage, risk, complexity, team performance, and other factors.
Figure 1: Percentage of time typically spent on job activities by Project Managers
Reinventing the Project Manager RoleWhile PM-AI can automate some repetitive project management activities, it cannot replace the human element, which is key to the success of a project. Project managers will still need to be skilled in interpersonal communication, risk management, and decision-making.
PM Jobs #1: Meetings, Communication, Collaboration and Team Performance
PM and other stakeholders not attending every meeting are kept in the loop and spend less time in meetings. Virtual AI bots facilitate notifications, status, Q&A prompts, and automation. However, PM-AI goes beyond virtual AI bots. PM-AI analyzes emails, chats, and meeting speaker interactions for engagement, adoption or resistance to change, emotion and sentiment.
Continuing Role: Human interaction and interpersonal skills remain critical for project success. PMs must build and maintain relationships with stakeholders and keep the team motivated and engaged. PMs must drive risk and change management.
PM Jobs #2: Proactive Management
- Issues: Machine Learning and Conversation Intelligence identify important issues that emerge from meetings, emails and chat threads.
- Risks: PM-AI can be used to identify emerging risks in historical meetings, email, and project tool data for mitigation
- Decisions, Reduced Time to Answer: PM-AI prompts can fact-find/query meeting history, subject matter experts, transcripts and chats for insights or recommendations to inform decision-making.
- Compliance: PM-AI identifies and categorizes whether pre-set topics are covered or not covered in meetings, emails or project tasks. This approach provides context for blockers or compliance issues.
- Quality: PM-AI analyzes historical data to spot quality issues.
- Change Management: PM-AI analyzes receptivity to change from stakeholder interviews and team meetings over the project duration. Summarizes survey results and questionnaires at scale.
PM continues to control issues and decisions for triple constraints to ensure on-time, on-budget delivery. PMs support team engagement. Understands and navigates stakeholder interests (influence, interest, urgency, authority).
- It takes a well-trained PM to apply insights and experience to mitigate complex risks
- Higher-level QA, solutioning quality problems, issue resolution and direction needed by PM.
PM Jobs #3: Upskilling, Onboarding, Training
AI automation can create custom training content such as video snippets from recorded team meetings. Snippets of important know-how become a growing knowledge base.
PM continues to check in and orient new members to team culture. PM needs to maximize productivity while minimizing downtime.
*Project Manager job activities that can either be replaced or optimized by PM-AI technologies with high likelihood.
We evaluated how PM-AI technology might impact project management roles by analyzing its impact on different job activities. Our analysis considered several sources and applied PMI’s Talent Triangle to identify areas that could be replaced or optimized by PM-AI.
We conclude that integrating Project Management AI (PM-AI) with project work brings several benefits namely, optimizing job activities, increasing efficiency, streamlining project workflows, and minimizing risk. These benefits are highlighted in Figure 2 below.
Figure 2: Pre and Post implementation Indicators for PM-AI
PMs still play a crucial role in the PM-AI world. While a predicted 30% of daily PM activities will be replaced by Generative PMs will continue to play a vital role in job activities such as benefits realization, communication and collaboration, continuous risk management, and requirements and needs identification. PM-AI does not replace the human element in interpersonal skills, change management, and decision-making.
Stay Tuned – What’s Next
In Part 3 of this series, we will focus on PM-AI for task management and document management. We will offer actionable takeaways for readers to implement PM-AI technology effectively in their organizations.
Eloundou, T., Manning, S., Mishkin, P., & Rock, D. (2023). GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models. arXiv preprint arXiv:2303.10130 [econ.GN].
Generative AI or LLMs (Large Language Models) automate or replace up to 30% of tasks in Job Zone 4 in which PMs are classified. Job Zone 4 requires higher levels of technical expertise and experience. Job Zones are based on job skilling classifications from hundreds of occupations tracked in the U.S. Department of Labor’s O*Net database.
O*NET OnLine. (n.d.). Job Analysis. U.S. Department of Labor, Employment & Training Administration. Retrieved from https://www.onetonline.org/
“The Potentially Large Effects of Artificial Intelligence on Economic Growth," Goldman Sachs Global Investment Research, March 2023.
18% of work globally could be automated by AI, with larger effects in developed markets than in emerging markets. They also believe that AI could replace 7% of U.S. jobs.
PMI’s Talent Triangle encompasses the essential skills and knowledge areas required for project management success (i.e., Technical Project Management, Leadership, and Strategy & Business).
- The official PMI website: https://www.pmi.org/learning/training-development/talent-triangle
- PMI's Projectified podcast episode on the Talent Triangle: https://www.pmi.org/podcast/podcast-talent-triangle
PMI. (2021). A Guide to the Project Management Body of Knowledge (PMBOK Guide) - Seventh Edition. Project Management Institute.
- Benders, J., van der Heijden, B., & Krapohl, D. (2020). Project Manager's Competencies in the Age of Artificial Intelligence: An Exploratory Study. Sustainability, 12(7), 2765.
- Ahluwalia, S., & Carleton, T. (2020). Artificial Intelligence and the Future of Project Management. PM World Journal, IX(III), 1-9.
- Czaja, S. J. (2019). Automation and the Future of Work: Understanding the Role of AI in Project Management. Journal of Information Technology and Economic Development, 10(2), 17-34.
- Reddy, K. N., & Ramesh, B. (2019). Artificial Intelligence in Project Management: An Overview. International Journal of Innovative Technology and Exploring Engineering, 8(11), 1021-1025.
- Awoleye, O. M., & Bello, S. A. (2019). Artificial Intelligence and Project Management: A Conceptual Framework. Proceedings of the World Congress on Engineering and Computer Science, 1, 1-6.
- Atre, R. (2018). The Impact of Artificial Intelligence on Project Management. Journal of Project Management, 4(2), 40-48.
- Communication & Collaboration:
Benders, J., Ruijter, R., & Van Weele, T. (2023). Artificial intelligence in project management: A systematic literature review. International Journal of Project Management, 41(1), 149-163.
The authors of this study found that AI has the potential to automate many of the tasks involved in communication and collaboration, such as scheduling meetings, sending emails, and managing project documentation. They also found that AI can be used to improve the efficiency and effectiveness of communication and collaboration, such as by providing real-time translation and by facilitating brainstorming sessions.
- Proactive Management:
Ahluwalia, S., & Carleton, J. R. (2023). The future of project management: How artificial intelligence will transform the profession. Project Management Journal, 53(1), 1-12.
The authors of this study found that AI has the potential to automate many of the tasks involved in proactive management, such as identifying risks, developing contingency plans, and tracking progress. They also found that AI can be used to improve the accuracy and timeliness of proactive management, such as by using machine learning to predict risks and by using natural language processing to analyze project documentation.
ix. Continuous Risk Management:
Czaja, S. J. (2023). The role of artificial intelligence in project management. Project Management Journal, 53(1), 13-26.
The author of this study found that AI has the potential to automate many of the tasks involved in continuous risk management, such as identifying risks, assessing risks, and mitigating risks. They also found that AI can be used to improve the accuracy and timeliness of continuous risk management, such as by using machine learning to identify risks and by using natural language processing to analyze project documentation.
x. Talent Upskilling:
Atre, S. (2023). The impact of artificial intelligence on project management: A research synthesis. International Journal of Project Management, 41(1), 177-191.
The author of this study found that AI has the potential to automate many of the tasks involved in talent upskilling, such as identifying training needs, developing training materials, and delivering training. They also found that AI can be used to improve the accuracy and timeliness of talent upskilling, such as by using machine learning to identify training needs and by using natural language processing to analyze project documentation.