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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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Shannon Hrudka Exeter, Ontario, Canada
Hi Claudia,

We have not yet implemented GenAI formally within the processes of our construction company, but are encouraged to trial and test LLM's such as ChatGPT to get a sense of the accuracy of results, and hence the potential benefits. So far the results are very encouraging, but I realize now (based on this course) that our company data sets are currently not organized well enough and will need a lot of massaging before being able to trust the results. Thank you for offering this course to help guide us in the right direction! Shannon
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Riaz Mohammed Project Management Unit Head| Al Kuhaimi Metal Industries Dammam, Saudi Arabia
Hi Claudia
I am working as a Project Management dept. head for a Steel products manufacturing organization.
Our project management tools are very basic,for eg:MS access, MS excel, etc.
This course was a good learning experience for me to understand the huge potential of GenAI tools and techniques in the field of Project Management.
This course has motivated me to dwell further into AI models with an aim to enhance the Project Management.
Surely ,we will look for the opprtunities to implement GenAI models to collect, structure and alayze data.
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Ethan K Senior Staff Industrial Engineer| Semiconductor Melaka, Melaka, Malaysia
Yes, we’ve started integrating readiness checks through our internal Jira board to track project.
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Gary Gallimore Nepean, ONTARIO, Canada
Our company is slowly rolling incorporating genAI into our processes. But we are spending a significant amount of time ensuring that everyone understands the importance of ethics and security around genAI.
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Gary Gallimore Nepean, ONTARIO, Canada
Our company is slowly rolling incorporating genAI into our processes. But we are spending a significant amount of time ensuring that everyone understands the importance of ethics and security around genAI.
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Denathayalan Ramasamy Chief Technology Officer| Atal Incubation Centre -CIIC Chennai, Tamilnadu, India
Dec 05, 2023 1:56 AM
Replying to Zohaib Qadir
...
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.
Good tips :) I try to implement it my new projects
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China Kierson Richmond, VA, USA

We’re beginning to explore frameworks to assess our readiness for integrating Generative AI into our workflows. While we don’t yet have a formal AI-specific checklist in place, we’re leveraging our existing project management and data governance structures as a foundation.



Our current focus is on three key areas:



Data Organization and Accessibility – Ensuring our data sources are structured, compliant, and accessible for potential AI applications.



Policy Alignment and Ethical Considerations – Reviewing our privacy, compliance, and data security practices to ensure they can extend to AI-driven tools.



Operational Readiness – Evaluating internal workflows, team training needs, and resource allocation to support the adoption of AI-based solutions responsibly.



We’re also monitoring emerging industry frameworks around AI governance and model evaluation, which will likely inform the formal checklist or protocol we develop in the near future.

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Anonymous

Due to strict privacy protocols, only Copilot is permitted for generative AI tasks. This ensures data security but limits access to broader AI capabilities. The key impacts include:
Reduced flexibility in AI-assisted workflows.
Limited automation for specialised or advanced tasks.
Slower innovation due to restricted experimentation.
Increased reliance on manual processes or alternative tools.

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Anonymous

Due to strict privacy protocols, only Copilot is permitted for generative AI tasks. This ensures data security but limits access to broader AI capabilities. The key impacts include:



Reduced flexibility in AI-assisted workflows.
Limited automation for specialized or advanced tasks.
Slower innovation due to restricted experimentation.
Increased reliance on manual processes or alternative tools.

Organisational Checklist for Successful Integration of Copilot includes:



Governance & Compliance - Align with privacy regulations and internal policies.



Access Control - Limit usage to authorized staff; monitor activity.



Training & Awareness - Educate users on ethical and secure AI use.



Use Case Validation - Approve specific tasks suitable for Copilot.



Performance Monitoring - Track output quality and gather user feedback.



Fallback Protocols - Maintain manual or alternative workflows for unsupported tasks.



Data Handling - Avoid inputting sensitive or confidential information.

Nov 29, 2023 8:14 PM
Replying to Rami Kaibni
...
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 haven't started but we are also observing to see the progress of other professionals and organizations at time.
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