Agile ChatGPT PMO Risk 
Rebel’s Guide to PM
MARCH 14, 2024
54% believe it will change the way work is organized and 53% believe it will increase project team agility. 25% of PMO directors say that AI has disrupted their PMO ( Capgemini ) – and I’m sure that number will only continue to rise. 60% believe that augmented analytics can help risk and opportunity planning and management.
MPUG
MAY 10, 2024
I also believe that in the recent past, Agile principles, processes and techniques have added to our improved success rate. As PMO Leaders, we need to adopt a fresh perspective. The term “AI” often conjures up images of “The Terminator,” or, as I have seen more recently, is associated solely with ChatGPT.
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The IIL Blog
MARCH 26, 2024
By Ruchi Gupta, PMP ® , PgMP ® , PMI-ACP ® , PMI-RMP ® , SAFe, DASSM, SIP, Jira CBAP ® The accelerating rise of Generative AI tools like ChatGPT, Gemini, Copilot, and others is rapidly reshaping the way project management operates. The Project Management Office (PMO) is no exception.
MPUG
JUNE 23, 2024
The image above is from ChatGPT 4o with a prompt of “draw me an AI graphic for effective use of artificial intelligence tools.” Risk Identification As a check, we used AI to provide a list of risks associated with developing embedded products with a team distributed across the globe.
MPUG
FEBRUARY 5, 2025
One might conclude that agile approaches to project management, such as Scrum, mean there is no need for gate reviews. They consider the resources needed, potential risks, and strategic alignment for the next phase. We suggest blanket statements like that are of little use and amount to checking our brains at the door. Pries and J.
NimbleWork
JANUARY 9, 2024
Data mining for risks AI tools can mine your existing data from past projects to highlight potential pitfalls for the current project. For example, they can compare the risks from past projects with the demographics and characteristics of a new project to see which risks are likely to apply to the current work.
NimbleWork
NOVEMBER 30, 2023
Be it risk forecasting and mitigation, project forecasting, resource demand forecasting and supply management, where there is a dearth of sufficient and high quality data for AI forecasting engines to read, things still continue to be where they have been. Traditional (waterfall), Agile (Scrum, Kanban, other) or Hybrid?
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