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...
Honestly not in my company today. But taking these courses is going to have me thinking of the future and how we can prepare data now to utilize GenAI in the future. Saving Changes...
Thank you for sharing Zohalb, there is a great amount of information in your post. Could you share any specific cases when you have implemented AI in your customers? It would help us see a real case. Thanks a lot
I am also interested in real cases of AI implementation in the construction industry. There are too many expectations and ideas, but I am afraid of very few applications on a routine basis yet. Saving Changes...
Data quality has been a perennial subject and my research 15 years ago found that discussions and practice focused at that time on how to prepare the data (i.e. structure it, clean it up, etc.) before it was used as an input. What was missing at that time, were techniques that provided a "smell test" on data quality from the source: i.e. data integrity problems that emanate from malice, negligence, ignorance or bias of the primary or secondary generator of that data, which you've touched upon here when talking about consistency, relevance and accuracy. But the problem goes beyond raw data and into QC of analyzed data and information. To tackle this problem, I went back to quality control techniques used by journalists in verifying and validating their sources. Yet there still was no "checklist" I found, so I researched and created one myself and then published a 5 point protocol for filtering raw information (primary and secondary) to perform this "smell test" in my 2008 book dealing with energy industry information called, Energy, Risk and Competitive Advantage, the Information Imperative. I called the framework or protocol, "Triple CAT" (Consistency, Context, Competence, Accuracy and Transparency). It has been many years since I worked on this research, but I'm ready to pick it up again in this new GenAI environment to address the "Garbage in/Garbage Out" problem. Saving Changes...
I would like to bring the Topic of usning GEN AI in our company as a whole to look at lessons learned from past projects to ensure enhancement of effecients cost effective ways of completiing projects to generate more revenue. Also use it to enhance our proposals and manage projects more effectively. I am PM on site and not sure how to bring this topic up to Leadership as I would like to be the person that heads it up. Any suggestions. Saving Changes...
Collecting all feedback from past projects
Creating a list of frequently asked questions.
Assess risks based on past project perfomance. Saving Changes...
Not really, I hope with more information I can put this AI models on going in the the company, for the moment only making the questions to start the application of this new concepts. Saving Changes...
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