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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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Visukumar Gopal CEO - Chief Enabling Officer| SuVi Veratile Services Chennai, Tamilnadu, India
Purpose has to be clear, then Processes need to be defined for the integration, then Preparing people for usage of the new models.
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Jorge Escalante Project Manager/Educator| FHI360 Tegucigalpa, Francisco Morazán, Honduras
Typically, readiness assessments involve evaluating data privacy and security measures, ensuring compliance with ethical guidelines, implementing robust data validation processes, and establishing mechanisms for monitoring and mitigating bias in generated content. Additionally, organizations often conduct thorough risk assessments and establish clear guidelines for responsible AI use to mitigate potential negative impacts and ensure alignment with organizational goals and values.
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Moses Singh M.B.A., P.E., P.M.P. Manager - Gas System Planning - Gas Technical Services| Liberty Utilities Suwanee, Ga, United States
We have been using GEN AI in my MBA courses. It has helped us to gain deeper understanding of business needs and offer great discussions.
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1 reply by Claudia Alcelay
Feb 29, 2024 4:11 PM
Claudia Alcelay
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Thank you Moses, Chat GPT for example, fosters creativity which applied in a business needs context can be a great source of discussion.
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Anonymous
I am learning to use AI in my current project. I have been using it in risk management. But I am overwhelmed with all the information available at the moment, so I must select very well the information sources.
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1 reply by Claudia Alcelay
Feb 29, 2024 4:14 PM
Claudia Alcelay
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I understand you very well. A good starting point for a project manager is the Gen AI overview course by the PMI, or our recent one focused on understanding data in project management.
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RAMANESWARA RAO RONGALA Head - Projects| Construction Industry Charlotte, United States
Dear Claudia Alcelay,
Greetings...
We have not yet implemented any of these AI tools into our projects yet.
After going through the course material, I am compelled to explore the options to implement few of the tools enumerated in the lecture.
Warm Regards
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1 reply by Claudia Alcelay
Feb 29, 2024 4:21 PM
Claudia Alcelay
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Please Ramaneswara, it would be great if you can share your experience so that the community can learn.
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Nkwenti Hoza Giresse Plant design Engineer and Construction management| None Chemnitz, Germany
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.
Zohaib Qadir, this is a detailed and well-structured guideline for choosing and implementing AI more effectively. Thanks for sharing.
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Boon Kuan Low Singapore, Singapore
1. Problem Statement - what are the business challenges that you would like to tackle?

2. GenAI Readiness Assessment Checklist
- Data source and data set
- Environment, process and human readiness and capabilities (skillset, training and exposure/ experience)
- Systems availability and capabilities
- Data Hygiene
- Use Case

3. Regulatory and compliance requirements

4. Risk Management and Mitigation

5. Toolset evaluation (opensource, enterprise apps or in-house developed)
- supportability options
- systems lifecycle

6. Stakeholders'e engagement and buy-in

7. Project initiation
- Conceptual idea (Tabletop walk thru or pilot)
- Data sanitisation
- Test/ re-test and evaluate
- Capture feedback and Refine the model

9. Release management, change management and operation management
- Training. refine and re-learn
- feedback approach
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AMIR BAHARVANDI Construction Project Manager| Zarrin Afagh Koohrang Dubai, DU, United Arab Emirates

Dear Claudia, that's the $64000 question.
As a construction project manager, I believe it is crucial to embrace technology and leverage its benefits to enhance project workflows. When it comes to working with Generative AI data, it is essential to have a systematic approach in place to assess readiness and ensure a smooth integration into project workflows.



To assess data readiness, the checklist may include questions such as:


Do we have a sufficient volume of high-quality data available for training the Generative AI model?
Is the data diverse and representative of the project characteristics?
Have we addressed any privacy or legal concerns associated with the data collection process?
Is the data properly labeled and organized for effective use by the Generative AI algorithms?

In terms of team readiness, the checklist could include questions like:


Do team members have the necessary knowledge and skills to work with Generative AI data?
Have we provided adequate training or resources to upskill the team in understanding and utilizing Generative AI effectively?
Are team members aware of the potential benefits and limitations of Generative AI in the context of our specific project?

Furthermore, it is crucial to evaluate the readiness of the infrastructure and tools required for integrating Generative AI into workflows. My questions are:


Do we have the necessary computational resources to handle the computational demands of Generative AI algorithms?
Have we identified suitable software platforms or tools that support Generative AI and integrate well with our existing systems?
Have we conducted any necessary tests or pilots to ensure compatibility and performance?

If we can answer these questions accurately, by utilizing such checklists and protocols, I think we can systematically assess our readiness for working with Generative AI data.



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1 reply by Claudia Alcelay
Feb 29, 2024 4:19 PM
Claudia Alcelay
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Thank you for sharing Amir, I am recently exploring the importance of mindset and the impact of the degree of readiness that a company has, their core values, culture… to shift towards data driven decisions.
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Mohammed SLAOUI Transformation Progam Lead| Freelance Courbevoie, France

Absolutely, we’ve established a comprehensive checklist that aligns with our company’s data governance policies to ensure we're well-prepared for incorporating Generative AI into our processes. This checklist includes:


- Data Privacy and Security Assessment: We rigorously evaluate the sources of our training data for Gen AI to ensure they comply with GDPR and other privacy regulations.
- Bias and Fairness Audit: Before implementing any Generative AI model, we conduct an audit to identify and mitigate potential biases in the data that could affect the outputs.
- Performance Benchmarks: We've set specific performance benchmarks that Gen AI models must meet, which are continuously monitored to ensure the models perform as expected without degrading over time.
- Ethical Considerations Review: Our ethics committee reviews the applications of Gen AI to prevent any misuse or outcomes that could be ethically questionable.
- Continuous Learning and Updating: We've instituted protocols for regular updates and retraining of our models to adapt to new data and evolving standards.


We've found that these measures are critical not just for readiness, but for maintaining trust and reliability in the Gen AI systems we deploy. I'm eager to learn about the strategies others have put into place and look forward to reading more comments on this thread!
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Prasanjit Mandal Delivery Head| Technoidentity Hyderabad, TG, India
If we think of stages, the identification of good or sanitized datasets is crucial, choosing the right AI model amongst the available ones, determining the running environment like a cloud instance on Azure or AWS EC2 or even on-premise hosted high performance GPU, what domain specific data we will feed to the AI model for fine tuning and training, identification of the end goal on how the response output needs to be represented i.e. which modality like text, speech, image, video or charts, look-n-feel of the data representations.

To summarize, the whole initiative needs a lot of manpower initially from a prompt engineering or data engineering point of view and eventually, working with the LLM using Python language to develop a smarter iteration and go on improving the logic and improving the pitfalls.
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