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.
I agree with you, many times people are just pressured to use AI, but it is necessary to get the requirements clear first.
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2 replies by Kefilwe Forane and Ron Trosclair
Apr 07, 2026 4:18 PM
Kefilwe Forane
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Well said. It helps to first pause and ask yourselves fundamental questions that will guide the direction of the project prior to jumping into AI. e.g., What problem are you trying to solve? Does the problem necessitate the use of AI? From there, the decision to apply AI or not emerges naturally and with far more intentions.
May 06, 2026 1:25 PM
Ron Trosclair
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Requirements are still important. Especially when defining AI and how to use this new tool.
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Ivan Samuel SantanaProject Management Trainer & Consultant| ITPROIECTUSLas Palmas, Spain, Spain
I participated in one of these projects, which involved developing a chatbot to help respond to citizens’ inquiries. Shortly afterward, a manager suggested introducing AI as a tool so that the chatbot could respond intelligently. However, nothing was said about the need to update and reorganize all the information available on the website so that the chatbot could provide coherent and well-structured answers. Before considering the application of AI, it is essential to first assess whether we have the appropriate environment for the project to be successful.
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1 reply by Rita Averitte
Apr 09, 2026 9:11 AM
Rita Averitte
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Thats definitely true. Environment is everything for success
That's so true, it depends a lot of who is talking about AI, there are different kinds of get AI into your daily life, it's that because I wanted to join this course. We need to understand what we want to do with IA, there's a world of options. Saving Changes...
This comes up a lot and over time I’ve learnt that when someone talks about AI, it's often out of fear of being left behind or replaced by AI. Saving Changes...
I usually tell different kinds of AI work apart by how specific the request is. Clear goals and defined data point to real use cases, while vague ideas like “let’s use AI” often show confusion. What goes wrong is that everything gets grouped as “AI,” creating unrealistic expectations.Yes, I’ve been in conversations where people meant totally different things like chatbots vs. data analysis. I notice it when goals don’t match.Now, when someone says “We should use AI,” I ask: What problem are we solving and what does success look like? Saving Changes...
Kimayra RodriguezAgile Software Delivery Manager| GFR MediaToa Alta, Puerto Rico
The term “AI” has become a buzzword that often masks very different business needs. To clarify the intent, I usually focus on these three pillars:
1. The indicators for distinguishing the work Generative AI (Content/Creativity): If the person is looking to draft quick reports or generate images, they are seeking efficiency in creation. Predictive AI (Analysis/Data): If they ask, “When will the project be finished?”, they’re looking to reduce uncertainty based on historical data. Process Automation: Sometimes they say AI, but what they really need is a workflow that eliminates repetitive manual tasks.
2. What Can Go Wrong? When everything gets mixed up, the biggest risk is a misalignment of expectations. If the client expects a “magic solution” that makes complex ethical decisions and we deliver a basic chatbot, the project is doomed to fail from the start. Additionally, data cleaning costs are often underestimated if the type of AI to be used isn’t defined.
3. My personal experience I’ve been in meetings where an executive asked for AI to “be more innovative,” while the technical team understood they had to implement complex machine learning models. What helped me was asking, “What specific problem are we trying to solve that’s currently taking up the most of our time?” That grounds the conversation immediately. Saving Changes...
Anna GalstyanProject Manager| VGM ProjectsYerevan, Armenia
When someone says “we should use AI,” my first reaction as a construction PM is simple: Where exactly do you see it helping on our project?
On site, everything comes down to time, cost, and quality. If AI can clearly improve one of those — great, let’s define where and how. If not, it risks becoming just another layer without real impact.
Curious how others in construction are approaching this—where have you actually seen it work?
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1 reply by Paul Waggoner
May 13, 2026 4:15 PM
Paul Waggoner
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As a project manager you will be working with a team of maybe 6-8 members, each with a different idea what AI is or what it can be used for. This confusion can pose a challenge and delay at the beginning of your project. Again, AI implementations require a "mindset" change.
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Maloy MannaData Tech Cloud security PM| AXAParis, France
Can't agree more with Luis Branco. In fact "we should use AI" is a great opportunity to learn more about the issues, constraints and bottlenecks perceived.
Stakeholders don't always feel right complaining outright, and this could be: either : genuine suggestions to use AI technology to improve outcomes and/or : couching their frustrations about constraints and roadblocks like slow processes, poor quality
Steer the conversation to find out their motivations, the Why behind the ask: - is it fear of missing out (FOMO)? With the current hype around AI, maybe they think AI is the silver bullet - is it strategic signaling ? To look innovative or good in front of their audience? - is it genuine intent about overcoming some constraint, or improving something further ?
AI is a tool, it's not the job. Find out what's the "job to be done".
But wait! You've already discovered something which you didn't plan for. AI capabilities and hype have been increasing over the last couple of years and directly affect business. The first step was to build awareness and consensus to get everyone on the same page about AI.
If you don't "get" what your stakeholder means, you need to get your act together to build a shared understanding and vocabulary of what AI means in your context (project/program/organization).
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1 reply by SHARON TANUI
Mar 30, 2026 8:28 AM
SHARON TANUI
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I totally agree with your sentiments on AI adoption in the construction field. There's a buzz about AI being the new kid on the block but as people in the construction filed it is important to identify where and how to incorporate AI, may be just as a guide on how to navigate building projects but the human interface is still very much a necessity that may not be negated by AI.
Distinguishing AI types involves identifying signals like memory retention (reactive vs. limited memory), architectural complexity, and whether it operates as a "tool" (isolated task) or an "operator" (embedded in workflows). Misclassifying these—or lumping them together—causes AI to underperform, becoming expensive, unreliable, or hallucination-prone when simple reactive tools are tasked with complex reasoning
Distinguishing AI types involves identifying signals like memory retention (reactive vs. limited memory), architectural complexity, and whether it operates as a "tool" (isolated task) or an "operator" (embedded in workflows). Misclassifying these—or lumping them together—causes AI to underperform, becoming expensive, unreliable, or hallucination-prone when simple reactive tools are tasked with complex reasoning Saving Changes...
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