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Ready, Set, Gen AI! Share Your Checklists and Protocols for Successful Integration

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Claudia Alcelay
PMI Team Member
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!
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Teresia Thuku Nairobi, 30, Kenya
Not yet incorporated any, i have been using GPT to come up with draft templates which i modify offline. This course shall aid in coming up with proper plan.
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Carol Baker Indianapolis, In, United States
We don't have protocols in place yet other than items related to academic integrity, but a small group has been tasked over the next 2 months with testing Microsoft Copilot to evaluate its advantages and value as a productivity tool for us. Our goal is to ID value, impact, challenges, gather feedback towards shaping a campus-wide plan, etc. Also, we hope to begin establishing consistency and standards for use.
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Kokou Amouzou CIO| Health Insurance Firm Lome, M, Togo
Our social security organization is not yet preparing a checklis to assess AI implementation readiness in the company. But I will be happy to see how it is from other social security organization, thanks Claudia
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KOFFI AZOUMAH LOME, M, Togo
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!
actually in my company nothing is set for integrating gen AI. but since this is the future, we are sharing the development an interest of integrating gen AI with coworkers
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Gbadebo Babsalaam Geoscience Project Manager Calgary, Alberta, Canada
Hi Claudia,

In the energy/extractive industry, some companies have initiated several strategic approaches and implemented specific checklists and protocols to assess their readiness for integrating Generative AI (Gen AI) into their workflows. These measures are designed to leverage Gen AI’s capabilities effectively, particularly in predicting recoverable hydrocarbons from oil and gas reserves based on available data. Here are some key strategies already adopted:

1. Data Quality Assessment:
Research shows that high-quality data is crucial for accurate predictions in the oil and gas industry. Companies like ExxonMobil and Shell have invested in advanced data management systems to enhance data integrity.
Implementing standards such as PPDM (Professional Petroleum Data Management) can further ensure data quality and consistency.

2. Model Training and Validation:
Industry leaders use sophisticated machine learning frameworks like TensorFlow and PyTorch for model training and validation. For example, BP has utilized these frameworks to enhance their predictive models for hydrocarbon recovery.
Incorporating real-time data from IoT sensors and edge computing can significantly improve model accuracy and timeliness.

3. Integration with Existing Systems:
Effective integration often involves using middleware solutions and APIs to connect Gen AI models with existing ERP systems like SAP or Oracle. Chevron has successfully implemented such integrations to streamline their operations.
Leveraging cloud platforms such as AWS or Azure can facilitate scalable and flexible integration solutions.

4. Collaborative Approach:
Companies like Schlumberger emphasize cross-functional collaboration by establishing integrated project teams that include domain experts and data scientists. This holistic approach ensures comprehensive model development.
Utilizing collaborative platforms like Microsoft Teams or Slack can enhance communication and coordination among team members.

5. Continuous Monitoring and Optimization:
Continuous monitoring tools such as Apache Kafka for real-time data streaming and Splunk for log analysis can help in tracking model performance and identifying issues promptly.
Adopting a DevOps approach for continuous integration and continuous deployment (CI/CD) can ensure that Gen AI models are regularly updated and optimized based on the latest data and insights.
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Cdr BK Raghunath (Retd) Traction SSM Group Manager| Alstom Bangalore, Karnataka, India
Hi Claudia, we haven't yet started preparing for this but are aware that this is definitely the future. The small steps that we have taken are using tools for making minutes of meeting.
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Nelson Blandino Rizo Implementation Manager| Ericsson San Salvador, El Salvador
interesting article and excellent proposals to carry out the adoption of the Gen AI in various projects

Thanks to all
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Nelson Blandino Rizo Implementation Manager| Ericsson San Salvador, El Salvador
interesting article and excellent proposals to carry out the adoption of the Gen AI in various projects

Thanks to all
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Girish Raghuraman Director| PriyaagEnterpriseInc Whitby, Ontario, Canada
Very useful insights to get one thinking of how we need to look at integrating AI into organizations!
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Girish Raghuraman Director| PriyaagEnterpriseInc Whitby, Ontario, Canada
Dec 16, 2023 5:17 PM
Replying to Ogechukwu Nwachukwu
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Hi Claudia, I have some documentation that provides guidelines/framework for managing AI/ ML projects. This platform does not have an attached feature. if you share me your email, then I can send it to your mail box.
Hi Ogechukwu

Would you be able to share the checklist that you would have. My email is [email protected]. Thanks, really appreciate you offering to share your experience
Thanks, Girish
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