In which situations do you use generative AI in relation to portfolio management? Which PPM processes benefit the most from generative AI in your experience?
Do you have any concrete examples of how you use it in practice?
Program Manager| HARPER SRLSanto Domingo / Distrito Nacional, Dominican Republic
In a PMO or portfolio management context, I use GenAI mostly for reporting, executive summaries, meeting summaries, communications, risk analysis, and drafting project artifacts. I've also found it useful for identifying trends across projects, preparing stakeholder updates, and consolidating information from multiple sources. The biggest value is usually reducing administrative effort so more time can be spent on analysis and decision-making. Saving Changes...
Imran AfzalAuthor| The Strategic PMOCary, NC, United States
Natacha,
Great question! In my experience, the most useful GenAI applications in PMO and portfolio management are not necessarily about “automating portfolio decisions.” They are more often about improving the quality, speed, and consistency of the information that supports those decisions.
A few areas where I see practical value:
a. Synthesizing portfolio information
GenAI can help consolidate updates across multiple projects, identify recurring themes, summarize risks, and prepare executive-ready narratives. This is especially useful when information is scattered across status reports, Jira, Confluence, spreadsheets, meeting notes, and dashboards.
b. Improving executive communication
One of the most useful applications is converting detailed delivery information into concise leadership summaries. For example: What changed? What decisions are needed? What risks are emerging? Where are priorities unclear? This can reduce reporting burden while improving the quality of the conversation.
c. Risk and dependency analysis
GenAI can help detect patterns across project updates, such as repeated schedule pressure, unresolved dependencies, inconsistent assumptions, or risks that appear local but may have portfolio-level implications. Human review is still essential, but AI can help surface weak signals earlier.
d. Portfolio sense-making
This is where I think the bigger opportunity sits. Portfolio management is not just reporting and prioritization. It is helping leaders interpret complex information, understand trade-offs, and make coordinated decisions. GenAI can support that by helping compare alternatives, summarize competing perspectives, and clarify where strategic intent and execution reality may be drifting apart.
e. Drafting and improving PMO artifacts
GenAI is very useful for first drafts of portfolio reviews, governance materials, decision logs, RAID summaries, stakeholder communications, meeting agendas, and lessons learned. The value is not that the first draft is perfect. The value is that it gives the PMO a stronger starting point.
To illustrate some of these points, one practical example from my own work involves using Cursor with MCP integrations and AI models such as Gemini or Claude to interact directly with portfolio and delivery data stored in Jira.
Rather than manually gathering information across dozens of projects, I can use natural language prompts to retrieve data, identify patterns, generate analyses, and create visualizations that would otherwise require significant manual effort.
What I find most interesting, however, is that the value does not come from the dashboard or chart itself. The outputs become inputs into established operating mechanisms such as portfolio reviews, program reviews, leadership discussions, and dependency reviews. They help surface emerging risks, resource constraints, delivery bottlenecks, competing priorities, and strategic trade-offs that may not be obvious when looking at individual projects in isolation.
In that sense, AI is not making portfolio decisions for us. It is helping us transform fragmented information into insights that support better conversations and better decisions. The goal is not improved reporting for its own sake, but stronger alignment between strategic intent and execution reality.
That said, I would be cautious about using GenAI as the decision-maker. Portfolio decisions involve strategy, politics, funding, capacity, risk appetite, organizational context, and leadership judgment. AI can help inform those decisions, but it should not replace accountability for them.
For me, the most valuable use case is not simply “AI generates reports.” It is “AI helps the PMO create better shared understanding so leaders can make better portfolio decisions.” The biggest limitations I see are data quality, fragmented tools, unclear governance, inconsistent terminology, and overconfidence in AI-generated outputs. If the underlying portfolio data is weak or the organization does not have clear decision rights, GenAI may simply make confusion faster and more polished. Saving Changes...
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