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...
Learning & Innovation Research Manager| Project Management Institute (PMI)Spain
Feb 28, 2024 11:33 AM
Replying to Saguna Khare
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Due to confidentiality concerns in Healthcare industry we haven't started using AI models yet/
Good point Saguna. Have you thought about developing models with a no-code, low-code approach? It could give you the privacy required internally. Saving Changes...
Anonymous
Hello Claudia,
Given the nature of the work we do in our company and the level of personal information involved, we haven’t yet begun using this. I am hoping we would begin in the upcoming months Saving Changes...
Michael ShostSenior Security Portfolio Leader| Group 1001 SolutionsBrewster, NY, USA, United States
I have been developing an initiative where I am creating AI-driven "Brains" for Enterprise Initiative Management, encompassing Project Brain, Program Brain, and Portfolio Brain models.
These AI Initiative Brains are designed to leverage the power of Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) to transform how projects, programs, and portfolios are initiated, planned, executed, monitored, controlled, and successfully delivered.
The use of my AI Brains is redefining the project management landscape by:
Enhancing decision-making with predictive analytics and advanced decision-making capabilities.
Streamlining project initiation, risk assessment, stakeholder management, and performance monitoring through AI-driven processes.
I am creating tailored models for the main types of initiatives.
Based on the tailored models, I am having the Initiative brains produce all required deliverables.
By progressively elaborating the info in the model as the initiative's lifecycle proceeds, the model assumes and maintain a proactive stance in putting the insights from the model's predictive intelligence to work optimally as driven by the AI model, trained on my customized LLM of PMO, Portfolio, Program, & Project, management knowledge.
Ensuring seamless integration with existing PMO tools and systems, enhancing interoperability and utility.
The models will integrate with best-in-class tools (EG, MS Project, Jira, and even PPM tools eventually)
Offering a scalable, flexible architecture that supports real-time data processing and iterative improvement.
As my work progresses I am enhancing the UI so that we can use NLP to interact with the models.
Granted it is a work in progress but initial results are more than encouraging, they are mind blowing! Saving Changes...
Manoel GalvaoProject Manager| CompuNet, Inc.Rexburg, Idaho, United States
What would be considered good measures to protect my clients data while using GenAI in PM Saving Changes...
Hi Claudia,
In my experience, the following is what we could call "good practices", which is in line with what Markus and Zohaib pointed out:
• A first basic concern is related to data privacy and security. Whether with GDPR, CCPA and, more recently, with the EU AI Act (https://www.europarl.europa.eu/topics/en/a...-on-artificial- intelligence).
• Data governance is paramount from the point of view of ethical considerations, and I already see AI committees and audits emerging to help in this regard (in addition to the purely technical perspectives of data processing). Actually, in some organizations, the data wrangling process is a project itself!
• We must be sure we have the necessary technological infrastructure for Gen AI and, even more so, the necessary skills internally to deal with this technology. As you know, it's not that easy to recruit good people in this area
• A good practice, as in any initiative that involves cultural aspects, is to do a pilot project and start small with what generates the most ROI and least effort. To do so, you need to analyze what you want to achieve. One of the main causes of failures in AI projects is precisely exaggerated expectations (mainly from the executive leadership).
• Establishing feedback loops with users is equally fundamental because, fortunately or unfortunately, data changes constantly even after the model is launched.
• Last but not least, maintain a continuous learning environment for everyone involved. I am referring to workshops, seminars, and other initiatives that align the opportunities and limitations of technology depending on the maturity and capacity of the organization in question.
As training provider and ATP, I just would like to comment that we realize the importance and benefits of using AI in project management. For sure with good help from these PMI courses.
So we have prepared a specific very practical course about how to benefit from AI in PM. Then we are also introducing modules in basically all the other courses (fundamentals, human skills, risk mgmt etc)..
The goal is to help all our course participants to understand how powerful AI is and help them to turn Ai into their new project assistant.
So in this sense we are introducing AI into our workflows as a training provider, but I must say not only that.
As any company we can of course also benefit from AI in almost everything we do, from reading and writing documents, presentations, emails etc, to simply staying up to date about what is going on.
So thank you PMI for these very inspiring and well prepared courses. Saving Changes...
Considering the project outcomes and other relevant factors that determine the benefit realization from projects - what kind of assessments have been carried so far or any studies to highlight after using AI tools as to what has been the success rate from various domains / industries whether it was structured data or unstructured data and the data quality ascertained in terms of accuracy - relevance and lastly consistency ? The moot point is the yard stick for measurements of AI tools - what can be the endgame ? Saving Changes...
Anonymous
We do actually do this and are starting to use more of a checklist format to ensure customer readiness for our healthcare implementations. Checklists help to define criteria, help to measure parameters needed and ensure accuracy so we do not miss any key steps along the way. These can be very helpful for GPT information and insights. Saving Changes...
Hi Claudia,
Starting the AI journey and hence are exploring synergies presently, plan to list down all potential synergies, qualify, classify and rate / rank them and then pick the top most for further detailing. Saving Changes...
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