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...
LATASHA DELANEYSr. Organizational Change Manager| BAE Systems Inc.Hampton, Va, United States
Nov 30, 2023 12:17 PM
Replying to Rami Kaibni
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Hi Claudia, thank you. As a mater of fact, I did a post last week on my LinkedIn as to how we can utilize AI in the construction industry because AI can add lots of value if properly utilized on Construction Projects. Some of those benefits include:
1) Predictive Analytics: Using AI algorithms to forecast timelines, material requirements, and potential risks, optimizing planning and scheduling.
2) Computer Vision and Drones: AI-powered drones equipped with cameras to monitor construction sites, track progress, and identify safety hazards.
3) Generative Design: Create and optimize designs based on project requirements, site conditions, and material constraints, enhancing efficiency and reducing waste.
4) Quality Control: AI-powered systems to inspect materials, identify defects, and ensure compliance with building codes and standards.
5) Autonomous Equipment: Integrating AI into construction machinery for autonomous operation, improving efficiency and safety on site.
6) Supply Chain Management: Using AI to optimize supply chain logistics, predicting material needs, and streamlining procurement processes.
7) Smart Project Management: Leveraging AI-driven platforms for better project management, collaboration, and decision-making driven by data insights.
This is fascinating. I work in Supply Chain management and I can already identify ways in which AI can help with reporting metrics, identify high risk areas based on those metrics, and provide recommendations on how to eliminate or mitigate those risks. Saving Changes...
JP FernandezProgram Manager| SLBHouston, Tx, United States
Dear Claudia, thank you very much for your question.
In our industry, Technology for Oil & Gas and New Energy, we've spent decades capturing, securing and making sense of data from every measurement and scenario. We've managed to get a lot of value from structured data and have worked hard to keep it secure and integrate it into many workflows and tools. We've focused on keeping data quality high.
Despite of the progress made, we know there's still a lot to discover, especially with less structured data. We're starting with tried-and-tested methods to keep data safe while exploring what Generative AI can offer, with discrete but tangible results already.
We're taking clear steps, building on years of data and innovation. These steps are giving us the confidence and helping us improve our data, workflows, and overall value chain. Stay tuned :)
Saving Changes...
NAVANEETHA KRISHNAN BALRAJManager Project Management Office| Saxon InfotechNew Market, MD, United States
Nov 29, 2023 8:14 PM
Replying to Rami Kaibni
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Claudia, this is a great question. However, given the nature of what we do as consultants, we haven't yet started preparing for this but would be very interested to see what other professionals and organizations are doing!
We have not implemented GenAI in our organization yet. But this course definitely provides me the opportunity to prepare for implementing GenAI soon. Saving Changes...
Andrew Abel AndersonSenior Project Consultant| Barba, LLCValencia and Alexandria, Virginia , Spain
Hello, all. I am jumping into the AI discussion. I am working on pulling together a AI project competition, and would like to get some ideas from you all on what would be a 1, 2 and 3 way to judge the competition. Plan is to provide those that submit projects with AI to take the PMI courses first, do a work shop and then frame the competition requirements. I am pulling all this together over the several several months. Any feedback would be appreciated. Saving Changes...
Anonymous
Thoughtful and deliberate approach. Saving Changes...
I am just beginning my journey into AI so I don't have anything to share. However I am learning a lot from this thread so I feel better prepared! Saving Changes...
Has anyone from this community of practice implemented any specific GenAI use case for Portfolio Management. It would be really a great insights on the discovery of the use case, deployment strategy and execution.
I am starting to delve into GenAi, i am persuing to use either ChatPPT or Gemini to create a model to help us to develop scope statements based on different sources of data in order to have a clear definition on wbs/backlogs to minimize risks when starting new projects. Saving Changes...
Anonymous
Good insights to implement the GenAI in our project management work! Saving Changes...
Here's how you can adapt the strategies and tools for assessing readiness and integrating Generative AI into workflows specifically within the Health sector domain:
Data Quality Assessment Checklist:
Develop a checklist tailored to healthcare data, assessing factors like relevance to medical research or patient care, accuracy, completeness, consistency, and adherence to medical standards.
Tools: Utilize healthcare-specific data quality assessment tools or platforms that can analyze medical records, clinical trial data, or electronic health records (EHRs) for quality metrics.
Ethical and Legal Protocols:
Establish protocols to ensure compliance with healthcare regulations and ethical standards, such as HIPAA for patient data privacy and protection.
Tools: Implement healthcare-specific data governance and compliance software that can manage consent management, data anonymization, and audit trails for compliance purposes.
Security Measures:
Enhance security measures to safeguard sensitive patient information from breaches or unauthorized access, ensuring compliance with healthcare data security standards.
Tools: Deploy healthcare-focused cybersecurity solutions, such as intrusion detection systems, endpoint protection, and encryption technologies tailored to safeguard medical data.
Model Validation and Testing:
Develop rigorous validation and testing procedures specific to healthcare applications, ensuring that Generative AI models meet regulatory requirements and produce clinically valid outcomes.
Tools: Utilize healthcare-specific validation datasets, medical imaging validation frameworks, and simulation environments for testing AI algorithms in healthcare settings.
Performance Monitoring:
Implement real-time performance monitoring systems to track the accuracy, reliability, and clinical relevance of Generative AI models deployed in healthcare workflows.
Tools: Integrate healthcare analytics platforms with monitoring dashboards that capture key performance indicators (KPIs) relevant to patient outcomes, diagnostic accuracy, and treatment effectiveness.
Training and Skill Development:
Invest in training programs focused on healthcare analytics, AI ethics, and clinical validation processes to equip healthcare professionals with the skills needed to work with Generative AI technologies.
Tools: Offer healthcare-specific training modules, workshops, and certifications in collaboration with medical institutions or professional organizations.
Collaboration and Communication:
Foster collaboration between data scientists, healthcare professionals, and regulatory experts to ensure alignment with clinical needs and regulatory requirements.
Tools: Facilitate communication through secure collaboration platforms that enable interdisciplinary teams to share insights, discuss findings, and address regulatory concerns.
Continuous Improvement:
Establish mechanisms for continuous quality improvement and feedback loops to iteratively refine Generative AI models and enhance their clinical utility over time.
Tools: Implement feedback mechanisms within healthcare workflows, leverage patient data feedback systems, and use AI-driven analytics to identify opportunities for improvement in clinical decision-making processes.
By applying these strategies and leveraging specialized tools tailored to the healthcare sector, organizations can assess their readiness and effectively integrate Generative AI into healthcare workflows while ensuring patient safety, data privacy, and compliance with regulatory standards.
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