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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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Greg Sillak Principal Consultant| Acumen PMO Calgary, Alberta, Canada
I can see how GenAI would be a valuable tool in scenario planning during front-end concept development of large energy projects. The multitude of inputs and related assumptions can easily lead to the wrong option being chosen. e.g.; reservoir productivity, well locations, gathering facility location, pipelines and utility locations. It would great to test out a GenAI application along with traditional concept development. There is potential to speed up the concept development timing and better understand outcomes of each scenario.
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Roksana Jahan Tumpa University Lecturer| Central Queensland University Sydney, Australia
Great discussion! I love the short videos. Would be really helpful to for project professionals to enhance project outcomes.
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Donna Pierre Programme Officer| World Meteorological Organization Avenue Soret, Geneva, Switzerland
Dec 05, 2023 1:56 AM
Replying to Zohaib Qadir
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Dear Claudia

Here's a checklist to help guide the integration of AI successfully:

Define Clear Objectives:

Clearly outline the objectives you want to achieve with AI integration.
Align AI goals with overall business and project objectives.
Understand Stakeholder Needs:

Identify and involve key stakeholders in the AI integration process.
Understand their needs, concerns, and expectations related to AI.
Assess Readiness and Capacity:

Evaluate the organization's readiness for AI adoption.
Assess the available technical infrastructure and the capacity for handling AI technologies.
Data Governance and Quality:

Establish robust data governance policies.
Ensure data quality and integrity for accurate AI model training.
Security and Compliance:

Address security concerns related to AI systems.
Ensure compliance with relevant regulations and standards.
Talent Acquisition and Training:

Identify the need for new skills and talents.
Invest in training programs for existing staff to adapt to AI technologies.
Start with a Pilot Project:

Initiate AI integration with a small, manageable pilot project.
Use the pilot project to identify challenges and refine the integration strategy.
Choose Appropriate AI Models:

Select AI models that align with project goals.
Consider factors such as machine learning algorithms, deep learning, or natural language processing based on project requirements.
Ethical Considerations:

Establish ethical guidelines for AI use.
Address biases and fairness concerns in AI algorithms.
Monitoring and Evaluation:

Implement robust monitoring mechanisms for AI performance.
Regularly evaluate the impact of AI on project objectives.
User Training and Acceptance:

Provide adequate training to end-users interacting with AI systems.
Foster a culture of acceptance and collaboration between AI and human teams.
Scalability and Future Planning:

Design AI integration with scalability in mind.
Develop a roadmap for future AI enhancements and technologies.
Continuous Improvement:

Regularly update AI models to improve accuracy and efficiency.
Stay informed about advancements in AI technologies.
Communication Plan:

Develop a communication plan to keep stakeholders informed.
Clearly communicate the benefits and impacts of AI integration.
Contingency Planning:

Develop contingency plans for potential AI failures or issues.
Establish protocols for addressing unexpected challenges.
This is a great and detailed outline Zohaib. Many thanks for sharing. The use of AI in Project Management has only touched the surface so far at my organization, but in this Data Landscape of GENAI for PM course, the understanding of how this can be considered is certainly a big take away for me. Your areas identified will definitely help and allow us to not feel like we are starting at absolute ground zero.
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Pascal GODJO Energy, Sustainability & HVAC Manager : Data Center and Building| SEATEC Groupe
Very interesting questions and feedback. My interrogations on construction industry are treated
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Preethi Gopalakrishnan Senior Manager, People Consulting| EY Global Delivery Services Kochi, KL, India
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!
Thanks Claudia and Rami, I am still a student (MBA/MPM) and thanks for the opportunity to share my 2 cents. Considering that GenAI works best with unstructured data, a good way to create an internal process to develop the strategy to include GenAI would be to conduct a Design Thinking workshop. This way ideas can be brainstormed, proto-typed and assessed as to what fits our organisation best. There's no one-size-fits-all protocol I suppose, but this robust approach could help and is agile too.
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Anonymous
No specific checklists at this time. In healthcare, access to data is regulated differently across the globe so I am eager to learn more in this area.
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Alexanderx Babs-Jonahx Surulere, Lagos, Nigeria
Thank you Claudia. We Provide PMO as a Service as such we focus on adapting GenAI to Project Management Governance. This helps provide insights into decisions made by Sponsors through the life of a project or program. It's still early beginning but the insight is extremely useful as our clients are realising quick wins from guided decision making.
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Marcos Garcia Salas Eden Prairie, MN, United States

I aim to help but want to clarify - I'm not working on a specific project/compnay at the the moment and don't have my own projects or company workflows to share. However, I can suggest some key considerations for organizations preparing to work with generative AI:


1. Data governance frameworks to classify sensitive data and establish handling protocols for AI training/usage.
2. Security assessment checklists covering data encryption, access controls, and monitoring of AI system interactions.
3. Quality control processes to validate AI outputs, including human review workflows and accuracy metrics.
4. Change management protocols to train staff and document new AI-integrated processes.
avatar
Marcos Garcia Salas Eden Prairie, MN, United States

I aim to help but want to clarify - I'm not working on a specific project/compnay at the the moment and don't have my own projects or company workflows to share. However, I can suggest some key considerations for organizations preparing to work with generative AI:


1. Data governance frameworks to classify sensitive data and establish handling protocols for AI training/usage.
2. Security assessment checklists covering data encryption, access controls, and monitoring of AI system interactions.
3. Quality control processes to validate AI outputs, including human review workflows and accuracy metrics.
4. Change management protocols to train staff and document new AI-integrated processes.
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Judith Lewis Caldwell, Tx, United States
I am not currently working in an area utilizing GenAI; but, I am interested in learning how others are implementing these tools in their projects.
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