AI is an enabler, not a requirement—clarity on business value, data readiness, and compliance must come first.What specific business outcome are we targeting, and which existing pain point is AI expected to address? Is this about productivity improvement, decision support, cost optimisation, or quality/risk reduction? Do we already have a clear use case that aligns with our data, governance, and compliance constraints—rather than adopting AI for technology’s sake? Saving Changes...
Rodrigo QuezadaCEO| ARQO PROJECTS PARTNERS Santiago, Region Metropolitana, Chile
I think it is giving a context of uncertainty and the introduction of a greater number of variables still unknown. AI could provide an earlier and timely analysis to advance the project. Saving Changes...
Itumeleng MokgotsiCAPEX Project Manager| Gold FieldsVanderbijlpark, Gauteng, South Africa, South Africa
AI outputs are based on patterns, not context ownership. As a PM, you’re still accountable for scope clarity, stakeholder alignment, and decision quality. If AI-generated insights are taken at face value without validation, that’s where credibility starts to slip. Firstly, accountability doesn’t shift—only the inputs do Secondly, Interpretation risk is a governance issue, not an AI flaw Thirdly, the real differentiator is application, not access Fourthly, AI should enhance your judgment—not replace it NB!! Use AI more for analysis and drafting, less for final decision-making Saving Changes...
Itumeleng MokgotsiCAPEX Project Manager| Gold FieldsVanderbijlpark, Gauteng, South Africa, South Africa
AI is a tool, and like any tool in project management, its impact depends on how deliberately you use it and how well you govern it.
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Itumeleng MokgotsiCAPEX Project Manager| Gold FieldsVanderbijlpark, Gauteng, South Africa, South Africa
Mar 25, 2026 9:08 AM
Replying to Dwight Clarke
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When someone says, “We should use AI,” they’re not giving you a requirement; they’re giving you a signal. From a PMI perspective, your role is to translate that into value by first asking what problem we’re actually trying to solve.. If the outcome isn’t clear, the solution shouldn’t be either. From there, identify the real need (automation, augmentation, insights, or user interaction), validate whether the necessary data actually exists and is usable, and define success in measurable terms. Only after assessing feasibility, technical, organizational, and governance constraints, should scope be defined. And in some cases, the right answer is not to use AI at all.
That's correct Saving Changes...
Erich SchimmelHead of Finance and Operations and Senior Consultant| Hesse Consulting GroupMannheim, Germany
When someone says “we should use AI,” I don’t hear a solution—I hear a signal. Usually, it points to pressure around efficiency, speed, or competitive anxiety. But the mistake I see repeatedly is treating AI as the starting point rather than the outcome. Most comments here rightly ask “what problem are we solving?”—but I’d go one step further: AI is not just about solving problems. It changes how decisions are made—and who is accountable for them. That’s where things tend to break. So I typically reframe the conversation around three questions:
What decision or process actually changes? If nothing changes in how decisions are made, AI is just cosmetic.
Where does accountability sit after AI is introduced? Many initiatives fail because responsibility becomes blurred while risk remains very real.
What is the economic lever? Cost, revenue, or risk—if none of these move in a measurable way, it’s noise.
What goes wrong is that “AI” becomes a shared word with different underlying expectations—automation, innovation, cost cutting—without alignment on outcomes or ownership. A simple test I use: If we removed the word “AI,” would the business case still stand—and who would own the decision tomorrow?
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Pam WillisonPM Consultant| Seven 3 LLCTowson, Md, United States
It's important to understand the context and what they consider AI. Saving Changes...
When someone says, “We should use AI,” they’re not giving you a requirement; they’re giving you a signal. From a PMI perspective, your role is to translate that into value by first asking what problem we’re actually trying to solve and can we look for a workaround, because using AI should be analyzed by 360 degrees and value add to the business and the ROI. Saving Changes...
Paul WaggonerProgram Manager| Consultant - FreelancePapillion, Ne, United States
Mar 25, 2026 9:08 AM
Replying to Dwight Clarke
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When someone says, “We should use AI,” they’re not giving you a requirement; they’re giving you a signal. From a PMI perspective, your role is to translate that into value by first asking what problem we’re actually trying to solve.. If the outcome isn’t clear, the solution shouldn’t be either. From there, identify the real need (automation, augmentation, insights, or user interaction), validate whether the necessary data actually exists and is usable, and define success in measurable terms. Only after assessing feasibility, technical, organizational, and governance constraints, should scope be defined. And in some cases, the right answer is not to use AI at all.
Very good points, AI is a very powerful tool, but not required for every project. This is where the sutdy of AI and understanding its application comes in handy. Saving Changes...
Absolutely dependent on what the actual requirement is.Whether there is a real need for AI or will using AI increase the complexity are points to ponder. Some simple automations could also resolve it.Data security should also be key with the usage of AI. Saving Changes...
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