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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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Oladeinde Amosun Senior Project Manager| ESW IT Business Advisors Calgary, Alberta, Canada
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.
I fully support Zohaib’s checklist components, as they closely align with our company’s AI adoption and change management protocols for digital transformation.
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Anonymous
done
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Anonymous
done
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shantilal zanwar Cypress, Texas, United States
We do evaluate data quality before feeding it to Copilot and Agents.
There is no check list per se, but standards for data quality.
For unstructured data, no Organization details can be fed on specific incidents, hence strict monitoring of what goes in to RAG agents is in effect.
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Shradha Khadka Falls Church, VA, 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 just come up with AI policy and are in the process of taking trainings. Very new to AI and absolutely NO to generative AI. We might work with BOX AI that keeps us within our platform.
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Elman Joel Garcia Diaz Asunción, 11, Paraguay

Hello Claudia,



In our workflow, we’ve adopted a structured approach to integrating Generative AI tools through a set of internal checklists and governance protocols. We begin with an AI Readiness Assessment, evaluating data quality, security compliance, and alignment with business objectives. Then, we define a Responsible AI Protocol, ensuring transparency, traceability, and ethical use across all stages of the project lifecycle. From a project management perspective, we’ve embedded AI checkpoints into our QA and risk management processes, verifying model outputs and maintaining human oversight. For tools, we leverage OpenAI and Azure AI services integrated with our internal knowledge base, supported by version-controlled prompts and documentation to ensure repeatability and auditability of AI-assisted outputs. This framework has helped us maintain consistency, reduce manual effort, and ensure that AI adds measurable value while staying compliant and responsible.





 
 

 

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David Enrique Velez Barreto Full-time Student| University of Puerto Rico, Mayaguez Campus Mayaguez, Puerto Rico
As a full-time student and aspiring Project Manager, integrating Generative AI has profoundly reshaped the way I approach learning, planning, and project execution. Rather than viewing it as a passing trend, I see Gen AI as a strategic enabler — a tool that enhances efficiency, creativity, and analytical depth across every phase of project development. Through its integration, I’ve been able to streamline workflows, analyze complex datasets, automate documentation, and simulate real-world decision-making environments with greater precision.
To ensure that this adoption remains both effective and ethically responsible, I apply a structured framework that begins with defining clear project objectives and selecting AI tools consistent with PMI’s best practices. I emphasize the use of accurate, unbiased information to uphold data integrity and decision quality. At an organizational level, it is equally essential to implement robust governance policies and cybersecurity response frameworks that ensure resilience, compliance, and ethical use of emerging technologies.
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Paulette Soto Project Manager| Government Florida, United States
I am employed by a county government, and due to budget and security considerations, our available tools, options, and resources are quite limited. Currently, we are authorized to use only Copilot. I am interested in learning more about AI, as I believe it is a powerful tool that can significantly facilitate and enhance our work.
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Owais Abbas Brampton, Ontario, Canada
Still learning to enhance my basic knowledge of Gen AI for applying it as any opportunity arises.
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Rita Mansukhlal Kotecha Head of Operations Analytics| H&M Stockholm, Sweden, Sweden
Hi Claudia,

We use a lightweight internal checklist to assess Gen AI readiness across three dimensions:


Data governance – ensuring data used for prompts or fine-tuning is compliant, secure, and ethically sourced.
Use case clarity – validating that the Gen AI application solves a real business problem (e.g. summarising incidents, automating stakeholder updates).
Human-in-the-loop protocols – confirming that outputs are reviewed before decisions are made, especially in production environments.
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