Learning & Innovation Research Manager| Project Management Institute (PMI)Spain
Are you utilizing any specific checklists or protocols within your projects or company to assess your readiness for working with Generative AI data? I'm curious to know what strategies or tools you've implemented to prepare for integrating Gen AI into your workflows. Please share your approaches in the comments below! Saving Changes...
Georganna BellManagement and Programs Analyst| VA Office of Acquisitions and LogisticsWashington, DC, United States
I totally agree! The GenAI readiness checklist will serve as an excellent roadmap for navigating any potential AI/ML initiatives going forward in VA. Saving Changes...
Hi all:
I am learning new ways to utilize my PM tools by using AI. I am quite impressed with PMI Infinity and have started utilizing it. I look forward to any feedback from experienced user and am open to any suggestions for me starting a new project, with new team.
When implementing a Gen AI checklist, I believe that preparing the enterprise is time well invested. Considering the diversity of all teams, the different levels of exposure they might have, and what they might expect using AI begs alignment for the implementation steps to be successful.
Best,
Kristen Saving Changes...
Ayumi DurdenProject and Data Manager| Change ImpactNC, United States
Hey Everyone, I think to ensure successful integration, it’s essential that checklists and protocols are built on diverse data sources. Relying on homogenous datasets can lead to biased outcomes that exclude or misrepresent certain communities. By incorporating a wide range of perspectives and lived experiences into our processes, we strengthen both equity and effectiveness. Inclusive data leads to more ethical, accurate, and sustainable integration.
Hope this was somewhat insightful. Good luck to everyone in their Gen AI learning journies! Saving Changes...
Anonymous
Hi Claudia,
Answering your question, I do not have any experience using a specific checklist or protocol with Gen AI, but the focus is on this way in order to start using AI for project management. Saving Changes...
one of the most productive sides of Gen AI for me is to bridge the gap between the technical and the project management side. Now creating a BDD or breaking down a techno project into simpler task is easy and get completed within seconds.
As a PM we can foresee the future of the final MVP before the project starts. Saving Changes...
one of the most productive sides of Gen AI for me is to bridge the gap between the technical and the project management side. Now creating a BDD or breaking down a techno project into simpler task is easy and get completed within seconds.
As a PM we can foresee the future of the final MVP before the project starts. Saving Changes...
Dipti SharmaProject Manager | eNest Sas Nagar (Mohali), Pb, India
As of now my company relies on customized chatgpt utilizing Langchain and RAG for project management related documentation, even our HR team is utilizing LLM for screening of candidates. I am eager to learn more and implement in our company. Saving Changes...
Data Landscape for PMs in AI Course - Module - Role of Data in Gen AI Systems - Can someone please help me explain the difference between 2 points. Data Quality - It says to use relevant data i.e. specific to similar industry projects will drive tailored results. However, under Data Variety it says to add multiple project data from different industries/domains to get diverse perspective? Don't these contradict? Thoughts?
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2 replies by Divya Rathod and Nikher Verma
Jul 03, 2025 9:15 PM
Nikher Verma
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Yes, you are right, but it's the words that are confusing—let's say you are working on sentiment analysis using GEN AI, and the goal is to provide insights into elections.
1. Data Quality—It says to use relevant data. For data quality, we will focus on data from India or any specific country that we are studying, because each country has its unique issues. Therefore, data from Caribbean or European countries won't be helpful to provide insights into Indian polls.
2. Data Variety—It suggests adding data from multiple projects across different industries or domains. For data variety, we will use not only official press conferences and political campaigns but also content from TikTok, Instagram, Facebook, and X, including Gen Z slang like “no cap,” “sus,” “vibe check,” or “rizz,” as well as entries from Urban Dictionary or Gen Z slang glossaries. For example, say “This policy slaps” instead of “This policy is beneficial,” and don’t forget to include the data from the local language. This will help the system to interact with people from all backgrounds.
Hope this helps.
Jul 05, 2025 7:35 AM
Divya Rathod
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Okay I get it now, it's contextual to respective project objective.
Data Landscape for PMs in AI Course - Module - Role of Data in Gen AI Systems - Can someone please help me explain the difference between 2 points. Data Quality - It says to use relevant data i.e. specific to similar industry projects will drive tailored results. However, under Data Variety it says to add multiple project data from different industries/domains to get diverse perspective? Don't these contradict? Thoughts?
Yes, you are right, but it's the words that are confusing—let's say you are working on sentiment analysis using GEN AI, and the goal is to provide insights into elections.
1. Data Quality—It says to use relevant data. For data quality, we will focus on data from India or any specific country that we are studying, because each country has its unique issues. Therefore, data from Caribbean or European countries won't be helpful to provide insights into Indian polls.
2. Data Variety—It suggests adding data from multiple projects across different industries or domains. For data variety, we will use not only official press conferences and political campaigns but also content from TikTok, Instagram, Facebook, and X, including Gen Z slang like “no cap,” “sus,” “vibe check,” or “rizz,” as well as entries from Urban Dictionary or Gen Z slang glossaries. For example, say “This policy slaps” instead of “This policy is beneficial,” and don’t forget to include the data from the local language. This will help the system to interact with people from all backgrounds.
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