Challenges remain the same from long time ago (Gen AI is outside there from long time ago): AI entities have a grade of confidence associated to them that must be published with the results. So, the final decision still remains in human being hands. The same for the outcomes: helps me to search patterns in a hugh amount of data to create the information I need adding that today I can find data public and outside my organization.
One of my favorite successes with generative AI has been using it to make everyday project work easier. I started small — asking AI to help summarize meeting notes and draft clearer updates for the team.
Over time, I noticed how much faster we could organize lessons learned and spot repeating patterns across projects. What surprised me most was how creative the results felt when combined with human judgment. It didn’t replace our thinking — it helped us see things from new angles and save time for what really matters: leading people and making good decisions.
HongPhuong Le Saving Changes...
Anonymous
We have used LLM's for one-off solutions but have not yet employed it at scale. Saving Changes...
As a recent MBA graduate in PM, I wrote my dissertation, "AI in Project Management in Ireland: A Study of Literacy, Policies, and Ethical Issues." This research gave me an opportunity to deepen my understanding of AI's potential to brainstorm and support text generation, but also its inherent threats, such as 'hallucinations' and ethical issues. My main takeaway is to keep learning, because its evolution is too fast; as old issues are fixed, new challenges immediately emerge, demonstrating the captivating nature of this continuous improvement process. Saving Changes...
Anonymous
Jan 04, 2024 4:56 AM
Replying to Sergio Luis Conte
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Challenges remain the same from long time ago (Gen AI is outside there from long time ago): AI entities have a grade of confidence associated to them that must be published with the results. So, the final decision still remains in human being hands. The same for the outcomes: helps me to search patterns in a hugh amount of data to create the information I need adding that today I can find data public and outside my organization.
My company- a fintech is very particular about NOT using synthetic data in production. We are looking at workarounds. Would appreciate any recommendations. Saving Changes...
Lately, I’ve been exploring how generative AI can support me in my role as a project manager, and it’s been an eye-opening experience. I’m still early in my learning journey, but I’m already seeing how AI tools can make a real difference in managing projects more efficiently. From drafting project documents to summarizing meetings and even generating ideas for risk analysis or communication plans, AI is helping me save time and focus more on strategy and people. I’m excited to keep experimenting and building my skills so I can integrate AI more effectively into my day-to-day project work. It’s clear that AI isn’t just a trend—it’s becoming a real partner in how we deliver successful projects. Saving Changes...
Generative AI speed up tasks—it unlocked new ways of working, provided predictive insights, and improved stakeholder engagement. The key was combining strong governance with iterative learning. Saving Changes...
Anonymous
I am not really using AI, but I am so interesting in this technology, that is so important! I want to use especially in excel.
I am astonished, about everything IA can be used.
Many thanks! Saving Changes...
Absolutely, we’ve experienced meaningful and sometimes unexpected successes with Generative AI across our projects. One of the biggest advantages has been the ability to accelerate analysis, documentation, and solution design, especially in software development and project management workflows. Gen AI has helped us reduce repetitive tasks, uncover patterns in project data, and improve the quality and speed of deliverables. However, these improvements only work when paired with strong safeguards. In our organization, we apply a Responsible AI Framework that includes strict privacy controls, data-classification guidelines, and secure environments for interacting with AI tools. Sensitive or client-specific information is never exposed to public models. Instead, we rely on private instances of OpenAI / Azure AI, combined with internal knowledge bases, to ensure that all outputs remain compliant and confidential. We also emphasize ethical use and human oversight. Gen AI supports decision-making, but it never replaces it. Every generated result is validated by the team to avoid bias, inaccuracies, or excessive reliance on automated outputs. The challenges mainly involved understanding the boundaries of the technology—learning what AI can safely handle and where human judgment is essential. The benefits, on the other hand, have been significant: improved productivity, faster prototyping, clearer documentation, and more informed project decisions. Saving Changes...
Yes, I’ve experienced some unexpected successes using Generative AI in project-related work, particularly in automating reporting and improving stakeholder communication. Innovative outcomes achieved:
Automated status reports: I used Gen AI to summarise complex operational data into concise, stakeholder-friendly updates. This saved hours of manual effort and improved clarity.
Risk analysis support: By feeding historical incident data into an AI model, I generated predictive insights on potential risks and mitigation strategies. This helped prioritise actions before issues escalated.
Creative ideation: For brainstorming sessions, Gen AI provided alternative approaches for process optimisation, which sparked innovative solutions we hadn’t considered.
Role of data: Data quality was critical. Clean, structured historical data enabled the AI to produce accurate summaries and meaningful predictions. When the input was inconsistent, outputs were less reliable—highlighting the importance of robust data governance. Challenges encountered:
Data privacy concerns: Ensuring sensitive project data was anonymised before using AI tools.
Bias and accuracy: AI sometimes produced generic or biased recommendations, requiring human validation.
Change management: Convincing stakeholders to trust AI-generated insights was initially difficult.
Benefits realised:
Time savings: Reduced manual reporting effort by 60–70%.
Me too Ming, I am learning a lot in this and other AI-related threads: how companies are buying synthetic data, how they are using it for user acceptance testing, on a personal basis... I am sure that if you want to implement any of these experiences in your company our community will be eager to help you. Please share your experience if you enter the Gen AI.
I've had several successes using generative AI to support my work. For example, I've used A1 tools to generate first drafts for internal communications and stakeholder updates, saving significant time while maintaining a consistent tone and structure. I've also used A1 for process documentation and summarizing complex information, which has improved clarity and reduced effort across the team. Overall, generative AI has enhanced productivity, encouraged more strategic thinking, and enabled faster decision-making. Saving Changes...
PMI Team Member
