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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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Shakeel Anwar Bhatti Abu Dhabi, , United Arab Emirates

In my case, we’ve started with a structured readiness checklist that focuses on three areas:

1- Data Governance & Security – ensuring sensitive information is classified properly and not exposed to external AI systems.
2- Use Case Validation – identifying where GenAI adds real value (automation, summarization, knowledge retrieval) versus areas that require strict human oversight.
3- Change Management & Training – preparing teams with guidelines on responsible use, ethical considerations, and accuracy checks.

Rather than rushing in, we’re treating AI integration like any other strategic project—starting small with pilots, documenting lessons learned, and gradually scaling once governance and trust are in place.

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Muhammad Imran Project, Program Manager Lahore, Pakistan
Lessons Learned and Resource Allocation data is uploaded to MS Copilot and used in the org in planning for new projects.
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Michael Paice Principle Project Manager| SMEC Logan, Queensland, Australia
Currently our organization is only recently beginning to look into adapting and creating LLM to automate some tasks.
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Asif Khan Product, Program, Customer and Executive Mgmt| IBM Ashburn, Va, United States
Nov 30, 2023 10:16 AM
Replying to Claudia Alcelay
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Hello Rami, your approach as a consultant could provide us with great cases to build upon a standardized approach to Gen AI data readiness. Although not into this topic yet, if some ideas come to your mind where you think Gen AI could play a role in your profession, please share. :-)
I liked the answers, from my past experience with consulting / engagements

1 customer engagement for half or full day workshop to define scope bound use case/s
2 define approach and teaming
3 model selection and data collection
4 data Sanitization including labeling
5 model training with data
6 pilot with live users and feedback
7 update based on user feedback and second round of training
8 launch of pilot for further training and testing
9 final handover of the solution
10 project closing and sustainment
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Lisa Cantavespre Al, United States
Hi Claudia, At this time, my organization is small, we are still exploring which tasks would realize the greatest benefit from AI without increasing risks as AI learns.
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Veronica Ford Project Manager| Verger Development Solutions Houston, Tx, United States
These are great points all. I'm learning at my own company, that there is a great amount of resistance as we attempt to embark on the AI journey. The readiness checklist offered by Zohaib Qadir is one I plan to use with upper management to help launch into the AI world.
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Veronica Ford Project Manager| Verger Development Solutions Houston, Tx, United States
These are great points all. I'm learning at my own company, that there is a great amount of resistance as we attempt to embark on the AI journey. The readiness checklist offered by Zohaib Qadir is one I plan to use with upper management to help launch into the AI world.
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Deanna Minick United States
Moving my Project Delivery team to a unified implementation framework with consistent milestones and tollgates and uniform vocabulary will help GenAI be more effecting when analyzing project results, potential risks, mitigation success and lessons learned. Getting the team out of spreadsheets was my single biggest goal and it will pay off greatly when adding GenAI to their arsenal.
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Bandar Bahlul Management| Logistics Management Riyadh, 1, Saudi Arabia

Thank you for your interest in how we’re preparing for Generative AI integration.



Yes, we are actively utilizing structured checklists and protocols to assess our readiness and ensure a smooth transition. These include:


A readiness checklist covering data quality, security, and team training.
Protocols for prompt design, output validation, and ethical use.
Internal workshops to educate teams on Gen AI capabilities and limitations.
Pilot projects to test use cases in project planning and reporting.

We believe that a strategic and responsible approach is key to unlocking the full potential of Gen AI in our workflows.

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Olanike Aladeojebi AI Project Manager/Data Engineer| Publica AI Lagos, Lagos, Nigeria
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.

Valuable insights.



Thanks Markus

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