With organizations increasingly focusing on cost reduction and improved efficiency through AI adoption across projects, Project Managers are also evolving the way they drive, manage, and deliver projects.
Depending on the nature of the project, AI can significantly improve efficiency, especially when PMs adopt and integrate AI tools effectively into their day-to-day activities.
In my role, where I work on SaaS implementations for customers, I leverage Enterprise ChatGPT extensively by creating dedicated Projects and GPTs for reusable PM activities. Some of the areas where AI has helped include:
JIRA analysis for aging items, dashboards, and insights
Automated email responses in Outlook
Automated Slack responses
Using SharePoint documents as a RAG knowledge base
PPT creation, including kick-off decks and solution presentations
Status summaries and executive updates
Risk management
Other recurring PM activities
For many of these activities, I have observed up to a 40% reduction in effort.
Of course, the actual efficiency gain may vary based on the type of project, organization, AI tools used, and how deeply AI is embedded into PM workflows.
We would love to hear your thoughts and experiences:
How much time are PMs saving by using AI?
Please share your inputs in the following format so the insights can be useful across different types of projects:
Nature of Project:
Example: Product Development, Embedded, ERP Implementation, Gaming, SaaS Implementation, etc.
Please share specific PM activities where AI has helped, such as reporting, risk management, stakeholder communication, documentation, planning, or governance.
Looking forward to learning from the community’s experiences.
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Luis BrancoCEO| Business Insight, Consultores de Gestão, LdªCarcavelos, Lisboa, Portugal
Nature of Project: Complex transformation programs, SaaS/ERP implementations, PMO environments and AI-enabled operating models.
Percentage of Effort Saved as a PM Using AI:
In my experience, AI can reduce effort by 20% to 50% in highly repetitive and information-intensive activities such as: • Reporting, • Meeting summaries, • Stakeholder communications, • Dashboard preparation, • Risk consolidation, • PMO coordination, • Knowledge retrieval, • Recurring governance activities.
But I think the bigger shift is not only time savings.
AI is evolving from an occasional productivity tool into a persistent operational layer embedded into project workflows, collaboration platforms and organizational knowledge systems.
That is why I see AI increasingly operating as part of the team’s cognitive support system, while humans must remain firmly inside the decision, accountability and strategic alignment loop.
Otherwise, organizations may accelerate execution faster than they improve governance, integration and decision coherence.
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1 reply by Arun Vedula
May 26, 2026 4:28 AM
Arun Vedula
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Totally Agree Luis, that's why Human-in-loop is important !!
Nature of Project: Complex transformation programs, SaaS/ERP implementations, PMO environments and AI-enabled operating models.
Percentage of Effort Saved as a PM Using AI:
In my experience, AI can reduce effort by 20% to 50% in highly repetitive and information-intensive activities such as: • Reporting, • Meeting summaries, • Stakeholder communications, • Dashboard preparation, • Risk consolidation, • PMO coordination, • Knowledge retrieval, • Recurring governance activities.
But I think the bigger shift is not only time savings.
AI is evolving from an occasional productivity tool into a persistent operational layer embedded into project workflows, collaboration platforms and organizational knowledge systems.
That is why I see AI increasingly operating as part of the team’s cognitive support system, while humans must remain firmly inside the decision, accountability and strategic alignment loop.
Otherwise, organizations may accelerate execution faster than they improve governance, integration and decision coherence.
Totally Agree Luis, that's why Human-in-loop is important !! Saving Changes...
Program Manager| HARPER SRLSanto Domingo / Distrito Nacional, Dominican Republic
Nature of Project: Software Development, Cybersecurity, Infrastructure, and Strategic Initiatives Percentage of Effort Saved: Around 20–30%, depending on the activity. Tools: ChatGPT, Claude, and Gemini. Examples: Meeting summaries, status reports, documentation drafts, risk reviews, presentation content, stakeholder communications, and organizing information from multiple sources. The biggest benefit for me has not been the time savings itself, but being able to spend more time on decisions, stakeholder management, and strategic discussions.
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1 reply by Arun Vedula
Jun 25, 2026 3:41 AM
Arun Vedula
...
Thank you Lissette - 20-30% is still a great deal. Agree with your point on the time savings during key decisions and strategic discussions.
Nature of Project: Software Development, Cybersecurity, Infrastructure, and Strategic Initiatives Percentage of Effort Saved: Around 20–30%, depending on the activity. Tools: ChatGPT, Claude, and Gemini. Examples: Meeting summaries, status reports, documentation drafts, risk reviews, presentation content, stakeholder communications, and organizing information from multiple sources. The biggest benefit for me has not been the time savings itself, but being able to spend more time on decisions, stakeholder management, and strategic discussions.
Thank you Lissette - 20-30% is still a great deal. Agree with your point on the time savings during key decisions and strategic discussions. Saving Changes...
Project Managers save 20% to 40% of their time by using AI to handle repetitive administrative work. Example: Instead of spending hours digging through emails and spreadsheets, a PM uses Jira AI or Microsoft Copilot to instantly summarize project risks and write weekly status reports in minutes. Saving Changes...
I'm not sure AI has saved me much time on projects. GenAI usage has had some impact on how I spend my time - validating content more than creating content - but my personal use of AI hasn't resulted in projects getting done faster.
To be fair, part of this is due to the culture of the company where I work and the nature of my role - my time is not dedicated to project management. The company doesn't want a project administrator.
I've worked at companies where meeting notes were more important and you were expected to send them out fairly soon after the meeting. I can see GenAI having greater impact at companies like that, but there might be additional steps that could be taken that don't involve AI that could be more beneficial. At one company, I pushed back because it became obvious fairly quickly that most people weren't reading them and there were more important things for me to do. I didn't stop publishing them, but I also didn't put them before activities that were more critical for project success. GenAI usage would still have had value, in this case, but it would have been secondary.
The use of GenAI by project team members has had greater impact on the project schedule than my use of it, but it's not always about less work - sometimes it's due to greater confidence in the information (even if that may be just an illusion). I'll be looking into Optimization AI tools to see if it helps even more. Claude code and Github Copilot are helping with development and code review, but I'm not sure about speed improvements, yet. AI-driven Anomaly Detection is something I need to look into to help with testing. Predictive AI tools, tied to our data, help leadership make faster decisions.
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