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! Saving Changes...
Swati SharmaSanta Clara, California, United States
We are in an early stage of establishing any processes or checklists to asses the AI readiness specifically within the projects. But I do see use of AI slowly starting to integrate with documentation tools and ticketing systems to improve the user experience and people beginning to understand and get a taste of how AI can assist us in getting better outcomes throughout the organization. To bring AI into the projects will require risk assessment, compliance, establishing policies, conducting trainings, auditing, infrastructure and many other aspects relating to security and privacy of data. Other than that buy in from leadership, strategic roadmap, financial readiness, process readiness can help with assessing the readiness. Saving Changes...
I don't have a clear cut answer to your question, Claudia. However, I believe it's going to be tough to incorporate AI quickly and it will find resistance in the beginning just like Agile and Agility did but sooner or later it will find its way everywhere.
PMI did two good actionable approaches by creating the PMI AI Assistant and releasing the GenAI Course.
I'd be interested to see what other members of this community have to say about this.
All, while I appreciate that we take the time to think strategically about an implementation of Ai, I am struck by the lack of clarity in how an enterprise would carry out an Ai adoption program while maintaining it's operational and proprietary data secure. Perhaps my question exposes inexperience. Using chatbots to document meetings and tasks is great, but some companies may not want their work and interactions with vendors training the publicly available models, even with premium agreements. LeanIX last year sponsored a BCG presentation on LLM gardens. That enterprise has to add a new and expensive layer in it's architecture that would contain and LLM, service and system layers and a host of proprietary, regulatory and industry libraries in order to realize the promise of AI. What does this imply for all those companies that have cobbled together their IT infra over decades? What about data integrity? What about Security and system updating? Will enterprise allow workers to build agents to automate work? What does an integrated ecosystem actually look like? While we're well along in the development stage, the application stage of AI is going to be constrained by energy an expense. All feedback is welcome. Saving Changes...
Pallab MajiSenior Engineer| Carrier CorporationBengaluru, KA, India
Here is the checklist for successful integration techniques of Generative AI in Project Management:
1. Strategy & Planning
✅ Define Clear Objectives – Identify specific project areas where AI can provide value (e.g., scheduling, risk management).
✅ Select the Right AI Tools – Choose AI solutions that align with project needs (e.g., MS Copilot, ChatGPT, Jira AI).
✅ Ensure Data Readiness – Clean and structure project data for AI processing.
✅ Stakeholder Alignment – Communicate AI benefits and obtain approvals from leadership and teams.
2. Implementation & Execution
✅ Pilot AI in a Controlled Environment – Start with a small use case before full-scale deployment.
✅ Integrate AI with Existing Tools – Ensure compatibility with PM tools (e.g., MS Project, Jira, Asana).
✅ Train the Team – Educate project teams on AI usage and best practices.
✅ Set AI Governance Policies – Define rules for AI-generated content, decision-making, and accountability.
3. Performance Monitoring & Optimization
✅ Track AI Effectiveness – Measure KPIs such as time savings, accuracy, and adoption rates.
✅ Refine AI Models – Continuously update AI settings based on feedback and performance.
✅ Ensure Compliance & Security – Regularly audit AI outputs for ethical and regulatory compliance.
✅ Encourage Feedback & Iteration – Gather insights from users and refine AI applications.
4. Scaling & Continuous Improvement
✅ Expand AI Use Cases – Gradually integrate AI into more project areas based on success.
✅ Automate Repetitive Tasks – Use AI for reporting, document generation, and meeting notes.
✅ Optimize Decision-Making – Leverage AI for predictive analytics and risk assessment.
✅ Stay Updated on AI Trends – Continuously explore new AI advancements for PM. Saving Changes...
We are utilizing AI to accurately generate meeting minutes, integrate with Microsoft Office 365 to gather and summarize relevant information related to the topics, and use it in our software development with Co-Pilot and Visual Studio to improve team efficiency and write quality code. It has also helped us develop policies, procedures, etc.
We are still exploring more and more ways to utilize AI in our company. For example Laboratory experimental problems detection, scanning of images and automation etc. Lots of learning...
Saving Changes...
Hugh WiegelAgile Leader and Release Train Engineer| Florida Power & LightBaltimore, Md, 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!
Hello Claudia,
We developed internal checklists to determine if the project or initiative could benefit from AI or other transformative technology. Then we also have checklists to determine the best approach for the AI tool/model we propose would best fit the problem/challenge being addressed. Types of data are one item on that checklist. Saving Changes...
Hi Claudia, in my company we have multiple guides on GenAI readiness as well as shorter checklists. My project has its own SOP for AI use which includes a process to discuss data that will be used with our clients/get their approval, which GPTs to use, and a process for evaluating the outputs with client and subject matter review (because AI does hallucinate). GenAI is a tool to complement our more detailed work and sometimes quickly test ideas that require more human thought and input. Saving Changes...
This is a very interesting question. In my company, people are already using genAI to some extent because it is already embedded into our day-to-day tools (Copilot in Teams, Visual Studio and Edge, JIRA, Acrobat, etc.), but we don't hear anything about regulating this usage from upper-management. This is particularly concerning if some employees decide to use public models available to upload files with sensitive private content, so definitely something to keep in mind. Saving Changes...
HAMEED ALYAJORIAudit and Risk General Manager , Project Manager| Yemen Customs AuthoritySn, Yemen
Dear Claudia
As AI adoption accelerates, a structured approach is essential to ensure seamless integration. Here’s a checklist to help you maximize the benefits of AI:
1- Understanding Data Before Implementation
🔹 Quantitative vs. Qualitative Data: Are your decisions based on numerical metrics (e.g., test scores) or subjective insights (e.g., survey responses)?
🔹 Structured vs. Unstructured Data: Do you rely on organized datasets (e.g., spreadsheets), or do you need to analyze raw data like audio recordings and videos?
🔹 Real-time vs. Historical Data: Do you need instant insights (e.g., live traffic data) or a retrospective analysis of past trends?
2- Ensuring Data Quality and Effective Management
✅ Big Data Analysis: Leverage AI to process massive datasets such as internet search data or e-commerce transactions.
✅ Data Governance: Establish clear policies for data protection and regulatory compliance.
✅ Synthetic Data Processing: Use artificial datasets for model testing and simulation when real-world data is scarce.
3- Choosing the Right AI Technologies & Models
🔹 Selecting the Right Model: Does your project require deep learning, natural language processing, or another AI approach?
🔹 Seamless System Integration: Ensure AI solutions align with your existing IT infrastructure.
🔹 Ethical Considerations: Address biases and fairness in AI algorithms.
4- Preparing Teams & Encouraging AI Adoption
✅ Employee Training: Invest in upskilling programs to ensure teams are AI-ready.
✅ Fostering Innovation Culture: Promote collaboration between AI and human expertise for optimal outcomes.
✅Effective Communication: Keep stakeholders informed about AI benefits and encourage active participation.
5- Continuous Improvement & Future Expansion
🔹 Performance Monitoring & Evaluation: Regularly assess AI effectiveness to align with business goals.
🔹 Scalability Planning: Develop a roadmap for future AI enhancements.
🔹 Risk Management: Implement contingency plans to mitigate unexpected AI failures.
I work in the healthcare consulting industry, and we use ChatGPT to evaluate and analyze customer surveys to better prepare ourselves for project implementations. As far as using checklist and tools that allow for better AIGen preparedness, this has not happened. Our use of tools are based on the consultants understanding of LLMs and how they would like to better manage projects. Based on your question, it could be beneficial for us to start operationalizing the use of LLMs to make our teams more efficient. Saving Changes...
Anonymous
AI discussions underway but non-specific at my company. Saving Changes...
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