Thiago Silva PereiraGerente de Projetos| Tecnocomp | Tecnologia & ServiçosSão Bernardo do Campo, Brazil
Quando alguém diz “devemos usar IA”, analiso a necessidade por trás da demanda, buscando clareza sobre o problema a ser resolvido. Prioritizo agilidade, aderência a padrões, respostas assertivas e quando necessário, ajusto o direcionamento para garantir uma solução eficiente e com valor real. Saving Changes...
I enjoy reading everyone's perspective. Thanks for the insight. Saving Changes...
Anonymous
Community emergency response teams (CERTs) might benefit from an AI solution. CERTs train to fill the gap that is likely to occur between when a major disaster happens and several days before local emergency fire and rescue can respond. In these cases. CERTs augment 911 responses until fire and rescue assets can take over. In the interim, a CERT incident commander (IC) needs to make team deployment decisions and maintain accountability, visibility and awareness of many dynamic facets, including: identification of individual trained CERT volunteers, spontaneous untrained volunteers, volunteer turnover, where search teams have been/need to be deployed, results of searches (e.g., identification, condition, and location of victims) and grouping teams for recovery, and tracking of victim recovery, staging and evacuation of victims to medical facilities, not to mention reporting, logistics, PR, communications, et cetera! The current accountability process CERT deployment is manual and subject to confusion and coordination issues . At the local IC level, an AI solution for resource awareness and decision support appears desirable, but may not be feasible on a personal laptop/tablet or smart phone. However, such a solution might be helpful at the 911 level.
You analysis provides a starting point for analysis.
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Rama GoluguriBusinessman| BusinessmanNidadavole, Ap, India
It is an advise suggesting potential benefits of AI in the arena of project,program and portfolio managements based on PMIs seven patterns mental model or approach,offcourse,with an embedded caution of gold plating. Saving Changes...
SANTOSH BADGUJARCHIEF OPERATING OFFICER| Accumax Lab DevicesAhmedabad, Gujarat, India
Michael, this is one of the most important diagnostic questions a leader or PM can ask before any AI initiative begins.
In my experience as a COO, when someone says "we should use AI," they're usually expressing one of several very different underlying needs:
1. "We have a tedious, repetitive task that's consuming disproportionate time" - This is the most straightforward case. The need is automation, and AI tools may well be the right answer. The question to ask: What specifically is the task, and what does good output look like?
2. "We're falling behind competitors who are using AI" - This is anxiety and competitive pressure masquerading as a technology request. The question to ask: What outcomes are competitors achieving? Is AI actually the mechanism, or is there something else we're missing?
3. "We have a visibility or decision-making problem" - Sometimes "use AI" means "we don't have the data or analysis we need to make good decisions." Before jumping to AI, ask: Do we have clean, structured data? Is the problem lack of analysis or lack of data?
4. "I've seen a demo and want that" - Enthusiasm for a technology without a clear use case. The question to ask: What specific problem were you trying to solve when you saw that demo?
5. "Leadership is asking us to do something with AI" - Compliance-driven adoption without a genuine use case. This is where AI initiatives often fail.
The PM's value in these conversations is precisely this diagnostic work: translating vague technology requests into specific, solvable problems with measurable outcomes. That's scope definition, not just technology strategy. Saving Changes...
Kashif AhmedTechnical Project Manager| Alrajhi BankRiyadh, Saudi Arabia
Usually when we talk about AI at the workplace it means speedy solution with precision. More often we discuss automation or connecting different jobs simultanously and running them autonomously. We hardly discuss about innovation and using LLMs in solutions. Additionally, AI Automation is misunderstood with general / non-AI based Automation.
In my openion there is a long way to go make people / organization understand the AI use cases in Project Management department. To me, AI can be used to understand and draw a pattern through lesson learn register and help us to develop realistic project plans with most potentially expected risks. Saving Changes...
Anonymous
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.
Agree Saving Changes...
Anonymous
Agree Saving Changes...
Shafreej HidayathDubai Health AuthorityDubai, DU, United Arab Emirates
When someone says, “we should use AI,” we should first treat it as an early solution hypothesis rather than a fully defined requirement. The statement needs to be unpacked because AI can mean many different capabilities, including automation, machine learning, predictive analytics, generative AI, natural language processing, chatbots, anomaly detection, or decision-support systems.
The first question we should ask is: What business problem are we trying to solve? AI should be linked to a clear organisational need, such as reducing processing time, improving accuracy, lowering cost, predicting risks, reducing manual effort, improving service quality, or strengthening decision-making. Without a clearly defined problem, AI may become a technology-driven initiative rather than a value-driven business solution.
Next, we should identify the specific process, workflow, or decision point where AI is expected to create value. For example, are we using AI to classify documents, forecast demand, detect anomalies, automate repetitive tasks, support decision-making, prioritise work, or generate reports? This helps convert the broad idea into a practical and manageable use case.
We should also assess data readiness. AI depends on reliable data, so we need to evaluate whether the required data is available, accurate, complete, accessible, secure, and legally usable. Poor data quality can lead to unreliable outputs, biased results, and poor business decisions.
Another important area is benefits realisation and success measurement. The AI initiative should have clear KPIs, such as reduced turnaround time, improved prediction accuracy, fewer errors, increased productivity, better compliance, or improved customer experience. This allows the project team to measure whether AI has delivered real value.
Finally, we should consider feasibility, stakeholder alignment, risks, and governance controls. This includes cost, timeline, technical integration, user adoption, privacy, bias, explainability, cybersecurity, accountability, compliance, and human oversight.
In summary, when someone says, “we should use AI,” we should translate the statement into a structured business and project discussion. We need to clarify the problem, validate the use case, assess data readiness, evaluate feasibility, define measurable benefits, align stakeholders, and establish governance controls. This ensures that AI is not implemented as a trend, but as a responsible, practical, and value-driven solution aligned with organisational strategy. Saving Changes...
When someone says "we should use AI," they're rarely asking a technical question. They're expressing one of several very different needs — and confusing them leads to failed projects, wasted budgets, and frustrated teams. Here's how I unpack it: 1. Are they solving a real problem — or following a trend? The first question I ask is: what outcome are we trying to improve? "AI" is not a goal. Faster document review, fewer manual errors, better forecasting — those are goals. If no one can name the problem, the conversation isn't ready yet. 2. What kind of "AI" are we even talking about? Automation? Predictive analytics? Generative AI? Computer vision? Each has different requirements, costs, risks, and governance implications. "Use AI" can mean anything from a simple rule-based bot to a large language model — and they're not interchangeable. 3. Who owns the outcome — and who owns the risk? This is where project managers add real value. AI systems need sponsors, not just champions. Someone must own the data quality, the model decisions, and the accountability when things go wrong. 4. Are we ready for it? Data availability, team skills, regulatory constraints, change management — readiness is often the honest blocker no one wants to name. The phrase "we should use AI" is actually an invitation to lead. The PM's job is to turn vague enthusiasm into a scoped, accountable initiative. Saving Changes...
"If you have an apple and I have an apple and we exchange apples then you and I will still each have one apple. But if you have an idea and I have an idea and we exchange these ideas, then each of us will have two ideas."
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