Preeti GuptaSenior Technical Program ManagerChicago, United States
What AI tools are you using in your project management work — and how effectively are they helping you?
In my own work, I mainly use AI for drafting emails, status reports, and executive updates, which has been a solid time-saver. That said, I haven’t yet been able to use it meaningfully beyond communication and documentation (e.g., planning, risk management, forecasting, or dependency tracking).
Curious to learn from others:
• What tools or use cases have moved the needle beyond writing?
• Where has AI not lived up to the hype?
• Any practical examples of AI actually improving delivery or decision-making?
Would love to hear real-world experiences.
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Sergio Luis ConteHelping to create solutions for everyone| Worldwide based OrganizationsBuenos Aires, Argentina
Most of us are using AI in project management from more than 30 years ago. Most of the people and organizations are not aware of that because some of then are embedded into tools. For example, to run Montecarlo analysis and schedule optimization. You put above planning as a matter of communication and I do not agree with that. The same with the other items. My it is true if you use AI just for communication and documentation components inside the planning, risk, forecasting components of those process. With that said I encourage people and organizations to avoid the use of generative AI as a synonym of AI. I am not saying you are doing that. And related to generative AI, if it is well understand and the whole process to implement it is follow then it will help a lot to create information from data then roles can take decisions about the process you stated in your comment. But the key thing is: clearly understand the process to implement it. For example, to understand that Responsible Ai component is a key ingredient for success. Saving Changes...
What’s resonated for me is that the real shift isn’t whether AI is used in project management — it’s where it actually influences judgment.
Like Sergio noted, many teams have been using algorithmic techniques for years (forecasting, Monte Carlo, optimization), often embedded in tools. What’s new with GenAI isn’t analytical rigor — it’s accessibility and speed. And that’s both the opportunity and the risk.
One distinction I’ve found helpful is this: traditional analytics optimizes known questions, while GenAI helps surface unknown ones. Forecasting models, Monte Carlo simulations, and optimization techniques are powerful when the structure of the problem is clear and the variables are understood. GenAI, by contrast, is most valuable earlier — when the problem is still fuzzy, assumptions are implicit, and the real risk lies in misalignment rather than math. It doesn’t replace analytical rigor; it precedes it by helping teams clarify intent, expose contradictions, and frame better questions before committing to models, plans, or execution.
Where I’ve seen AI genuinely move the needle beyond writing:
Sensemaking, not prediction. Using AI to synthesize large volumes of inputs (status updates, risks, dependencies, decisions) into patterns executives can act on — not to decide for them, but to surface trade-offs earlier.
Decision framing and options analysis. Helping teams articulate “what would have to be true” for different paths, or stress-test assumptions before committing. This is especially useful when decisions are reversible vs. hard to undo.
Governance hygiene. Drafting decision logs, clarifying ownership, and making implicit assumptions explicit. This doesn’t feel flashy, but it directly improves delivery by reducing downstream ambiguity.
Where AI hasn’t lived up to the hype (yet):
Automated planning and dependency management. These still depend heavily on the quality of inputs and human context. AI can assist, but it doesn’t replace the conversations that actually resolve conflicts.
Risk identification without accountability. AI can generate long risk lists, but without ownership and escalation paths, it often creates more noise than clarity.
The pattern I keep seeing is this:
AI adds the most value when it improves how decisions are made, not when it just accelerates documentation or produces answers faster than the organization is ready to absorb.
Used well, it becomes a cognitive copilot. Used poorly, it just amplifies existing process and governance gaps.
Curious how others are seeing this distinction play out — especially in environments where AI adoption is outpacing decision discipline. Saving Changes...
Hi Preeti, what we've done with AI is build a lessons learned tool that can generate insights inside of Jira. This is a Jira marketplace app called WorkshopIQ that we've made available to others but have found that AI can help generate insights from the data you collect from capturing lessons learned; super interesting stuff.
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1 reply by Preeti Gupta
Feb 03, 2026 10:57 AM
Preeti Gupta
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Jacob, this definitely sounds interesting, I would love to learn more about this
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Preeti GuptaSenior Technical Program ManagerChicago, United States
Feb 02, 2026 7:53 PM
Replying to Jacob Vu
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Hi Preeti, what we've done with AI is build a lessons learned tool that can generate insights inside of Jira. This is a Jira marketplace app called WorkshopIQ that we've made available to others but have found that AI can help generate insights from the data you collect from capturing lessons learned; super interesting stuff.
Jacob, this definitely sounds interesting, I would love to learn more about this Saving Changes...
Luis BrancoCEO| Business Insight, Consultores de Gestão, LdªCarcavelos, Lisboa, Portugal
Good question. I see a clear pattern emerging in practice.
Today, most teams are using AI exactly where you describe – communication, reporting and summarisation. That delivers efficiency, but not transformation. It saves time, but it does not materially change how decisions are framed or owned.
Where I’ve seen AI genuinely move the needle is when it is used as a thinking and sensing layer, not as an automation shortcut.
A few concrete examples from real project environments:
• Risk and assumption surfacing Using AI to scan charters, business cases, contracts and logs to surface implicit assumptions, weak signals and internal inconsistencies. Not “predicting risk”, but exposing blind spots earlier, while decisions are still reversible.
• Scenario framing, not forecasting AI helps structure alternative scenarios, constraints and trade-offs at key decision points. It does not replace judgement, but it improves the quality and discipline of the conversation before commitments are made.
• Stakeholder perception analysis Analysing qualitative feedback, meeting notes and survey comments to detect shifts in trust, confidence and perceived value. This often provides more actionable insight than traditional status indicators.
• Decision preparation, not decision automation The greatest value appears when AI helps leaders slow down and think better – clarifying options, consequences and ownership – rather than accelerating the delivery of poorly framed work.
Where AI has not lived up to the hype, in my experience:
• Autonomous planning and scheduling in complex, human-centric projects • “Predictive” delivery promises built on weak or biased historical data • Tools that bypass governance, context and accountability in the name of speed
The key insight for me is this: AI creates value when it augments human sensemaking and reinforces responsibility. It creates risk when organisations try to use it to escape decision ownership.
Used well, AI doesn’t manage projects. It helps humans manage uncertainty more consciously.
That shift – from speed to sensemaking – is where real project maturity begins. Saving Changes...
I use PMI Infinity™ to go beyond communication, automating reporting, tracking dependencies, and generating AI-driven insights for forecasting and risk management. It helps identify project risks early, prioritize tasks, and support data-driven decisions. Saving Changes...
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