How are you using Generative AI in your project management workflows?

I hope to use the workflow tools like Zapier.com and Make.com combined with Claude, ChatGPT and other LLMs for the following at work:

1. Analyze all Lessons Learned from all projects completed and provide a report with strengths and weaknesses and categorized.|

2. Analyze RFPs and provide a recommendation based on various categories (this would be a service of the Enterprise PMO for any teams looking to implement new software or change existing software with a consulting agency)

3.Analyze software licensing fees to determine which vendor has the best pricing (this would be a service of Enterprise PMO for any teams looking to implement implement without a consulting agency)

4.Compare software product and service offerings or companies competing in the same space against the technology stack already existing at my company to determine which software best meets our requirements and existing infrastructure (this would be a service offered by the Enterprise PMO for any teams researching new software)

I’ve been integrating AI into our project workflows to streamline processes and boost efficiency, but I’ll admit it’s not without its challenges. We’re using AI tools for task automation—like generating schedules and tracking progress—and predictive analytics to forecast risks and resource needs. It’s been a game-changer for reducing manual grunt work and giving us data-driven insights to guide our planning. However, letting AI handle actual decisions? That’s where it gets tricky. The tech is powerful, but it lacks the human judgment and context we rely on for complex calls, so we’re keeping it in an advisory role for now.

For workflows, we’ve designed a replicable process that saves us significant time: we pull raw project data (timelines, budgets, etc.), feed it into an AI tool for analysis, then use the outputs to refine our strategy in team discussions. We’ve strung together tools like ChatGPT for quick drafting (e.g., status reports) and a project management platform with AI plugins for resource optimization. This combo cuts down hours of manual effort and improves accuracy, freeing us to focus on stakeholder engagement and creative problem-solving.

That said, what’s stopping us from fully leaning into AI is the decision-making piece. It’s tough to trust AI with high-stakes choices—like approving scope changes or reallocating critical resources—because it doesn’t “get” the nuances of our project’s ecosystem. We’re still figuring out how to bridge that gap.

I haven’t yet explored PMI’s Practical Application of Generative AI for Project Managers course, but it’s on my radar—especially to see if it tackles this decision-making hurdle. I’d love to hear from others: How are you balancing AI’s capabilities with human oversight? Have you found workflows from the course that address this? If so, what’s worked (or hasn’t)? Looking forward to learning from the community!

As a Technical Project Manager, I leverage Generative AI to enhance efficiency across project workflows:

Project Planning & Scheduling – I use AI to generate WBS templates, optimize schedules, and predict risks based on historical data.

Communication & Reporting – AI helps me to automates status reports, summarizes meetings, and drafts stakeholder updates, saving time on documentation.

Resource & Budget Management – AI helps me to forecast resource needs, optimize allocation, and refine cost estimates.

Risk & Issue Management – Generative AI support me to analyzes project trends to flag risks early and suggests proactive mitigation strategies.

Agile & Scrum Enhancements – Generative AI assists me in writing user stories, prioritizing backlog items, and supporting sprint planning.

Knowledge Management – Generative AI helps me to automates documentation and extracts insights from past projects to improve future performance.

AI-powered Assistants – I use AI chatbots for quick project updates, workflow automation, and seamless tool integration.

Finally, AI-driven tools allow me to prioritize strategic decision-making while improving project outcomes.

This is one of the first things we have applied at my workplace, we currently use an AI summarizer though (Many on the market, I have seen them used in other organizations too during meetings and they all do a similar job).

We only use it in specific calls and meetings though as some of the people we work with have data governance rules that do not allow the sending of data to third parties (I suppose big corporations can make their own AI Meeting Note Takers - the API is probably out there somewhere).

The way we implement this is different though, the data comes in from the AI note taker as soon as the call is over, I take out the key highlights and plug them into ChatGPT to rewrite them (I share a guide with the LLM to replicate the way I write notes) and then I plug them into our meetings notes template and get it out.

What used to take me 2-4 hours to finalize, is being done in an hour (As I need to align the team on our Next Steps before sending them out to the client).

The reason it used to take so much time, is we used to record our sessions, and I would have to go through them again and match them against my notes to make sure I am not missing out on anything.