AI Product Manager (B2B) for PMI| Project Management Institute (PMI)Middletown, DE, United States
Hi PMI Community! I'm Kerry Brooks, a Product Manager at PMI, and we're looking to use your feedback to identify opportunities and gaps as we build a risk sentiment tool for project managers.
Risk management is widely considered one of the most important aspects of a project manager's role.
Effectively identifying, assessing, and mitigating risks can significantly impact the success of a project. It involves anticipating potential issues, analyzing their potential impact on the project's objectives, and developing strategies to minimize or eliminate these risks.
Imagine you have an AI-assisted risk application that can help you. How do you think it could be your secret weapon in handling risks?
From predicting hiccups to offering solutions, share your wildest AI-assisted risk management dreams! Saving Changes...
Sergio Luis ConteHelping to create solutions for everyone| Worldwide based OrganizationsBuenos Aires, Argentina
Mar 16, 2024 4:06 PM
Replying to Jari Anttila
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Thank you Sergio. We have the tools, but I was more referring the way of gathering the data to find out the current project sentiment and possible hidden risks. As you referred, we already analyze the sentiment of customer feedbacks. Now, how do do that for our internal project team. One way could be to collect feedback similarly as we do with customers, but getting answers can be challenging. To really dig the silent risks, we would need to access the raw data like meeting transcripts or Teams channel discussions.
For us, "client/customer is the next on the process chain" so we collect feedback from all customers in the same way. In the other side, for example, you can use meetings recording (video and transcripts) to collect feedback BUT IF AND ONLY IF you put in place policies and rules about that and each time you will start the recording you notice to everybody about that. You can collect feedback from emails and from comments in project site (yammer, ms team, whatever you use). There are lot of way to collect feedback. The generative AI architecture published by Google in 2015 has democratized the information and is just a matter to imagine it. BUT because of that I wrote in my first comment that one thing is mostly forgotten is that when organizations start using generative AI almost a new business unit has to be created (I am using almost for not being category) where you have to put there not only technical people but other roles like lawyers, diversity and inclusion specialist, linguistic, etc, etc. The cost is higher not because the tools, the tools are for free, because the associated structured you need to create not matter you use it internally. Saving Changes...
Risk management is inherently domain knowledge specific, so the application or model would need access to industry specific data for it to be useful. LLM's greatest strengths today is in classifying massive amount of information, and then let domain experts make decisions faster than before. Saving Changes...
Hi Sergio - thanks for your response. We are looking to test a tool that helps us get to the unknown-unknowns. Our hypothesis is if we can synthesize project team sentiment to get to those "underlying uneasy feelings and issues" about how the project is progressing, and provide mitigation steps to manage them early, we may be able to help our project leaders keep the project on-track.
Personally, I think AI is useful in all stages of risk management, from planning risk management, identifying risks, categorizing risks, conducting qualitative and quantitative risk analyses, planning risk responses, implementing risk responses, monitoring risks, etc.
Risk management should be automated so that project managers can focus on the project objectives. Saving Changes...
Michael BrowningDirector, Cybersecurity| Vanderbilt UniversityNashville, United States
Thank you, this was a very interesting read! Saving Changes...
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