Directly jumping to AI as the solution often indicates a lack of understanding the problem statement. PM practitioners need to work with business users to clearly articulate their challenges, needs vs wants, validate AI prerequisites within specific context before embarking on AI centric solution. Saving Changes...
We should use AI often sounds decisive, but it usually hides very different intentions. I’ve found that using AI patterns as a mental model helps unpack whether the real ask is automation, insight, recommendation, or behavior change. For project managers, framing the problem clearly is often more critical than the AI itself. Saving Changes...
When someone says “we should use AI,” the conversation is rarely about technology itself. It is usually about pressure for speed, efficiency, innovation, or competitive leverage. The first step is to clarify intent.
Three signals help distinguish what is really being asked.
First, decision proximity. Is AI automating a task, augmenting human judgment, or moving toward managing objectives autonomously? These are fundamentally different categories of work. The closer AI gets to consequential decisions, the stronger the need for governance, traceability, and explicit oversight.
Second, problem clarity. Is there a clearly defined business problem with measurable impact, or is AI being treated as the starting point? When the solution precedes the problem, misalignment and inflated expectations follow.
Third, accountability design. Who owns the outcome if an AI-driven recommendation fails? When responsibility becomes diffuse, risk scales faster than performance.
In many organizations, “AI” simultaneously means efficiency, experimentation, and cost reduction to different stakeholders. Misalignment becomes visible when decision flows and ownership are unclear. A common tipping point is when stakeholders use the same word “AI” but describe different success metrics.
The real shift is not from manual to automated. It is from “man in the loop” to “man in control.” Without deliberate design of responsibility, capability increases while accountability erodes.
Clarity of purpose, category of AI work, and ownership separates disciplined transformation from technological noise.
I agree that clarity of purpose is the key. In my experience, people often use "AI" as a catch-all term without distinguishing whether they mean automation, data analysis, or generative AI. That can lead to unrealistic expectations and confusion about outcomes. I've found that asking, "What specific problem are we trying to solve?" helps align everyone on the objective before discussing AI tools. In project management, AI delivers the most value when it supports planning, reporting, risk management, and decision-making rather than replacing accountability. Saving Changes...
Christopher SmithSr. Project Manager| Experian HealthDallas, Tx, United States
A very timely objective Saving Changes...
Meryem ElouaarProject Manager - Communication & Marketing| Maroc PMETangier, Morocco
When someone says "we should use AI," two things are packed into that sentence. One: an admission. Something is already broken or too slow. The comment is really about a problem, not about AI. Two: a bias. It assumes AI is the fix before the problem is even defined. It jumps to the solution before naming the question. So the real first step isn't picking a tool — it's diagnosis. Before anything else:
What exactly is frustrating people?
Who's asking, and why do they care?
Is this a process issue, a data issue, or a real capability gap?
What does "solved" look like, with no tech attached?
Only after that diagnosis can you ask if AI is even the right answer, or if something simpler would do the job. Saving Changes...
When someone says “We should use AI!” as per my experience they mean the following as per my experience * Automate the manual mundane tasks that are done in a tradional way * Implement an Agentic AI solution as a replacement for human to meet quick Project Delivery Timelines * Realize the value of the project in a quicker way to satisfy the project sponsor. Saving Changes...
h2 /h2 Now after completing my training "PMI Essentials: Seven AI Project Patterns" I know exactly how to approach this question, not always AI is the best solution, and when selected, some steps are necessary to be taken to get the most valuable results. Saving Changes...
Cuando alguien dice "¡Deberíamos usar IA!", suele verla como una varita mágica. En realidad, casi siempre buscan automatizar tareas manuales, generar predicciones con sus datos o simplemente innovar para competir. Para diferenciar los tipos de proyecto, la señal clave es el tipo de datos y la madurez del problema. No es lo mismo construir un modelo de machine learning desde cero, que integrar una API lista para usar o montar un pipeline de automatización. El gran peligro de meter todo en la misma bolsa bajo el término "IA" es la corrupción del alcance (scope creep) y la frustración del equipo. Se pierde tiempo intentando desarrollar código complejo cuando una solución estándar bastaba, o se subestima la infraestructura y la gobernanza de datos necesarias. Para evitarlo, la clave en los kickoffs es dejar de hablar de "IA" en abstracto y aterrizar la conversación de inmediato en módulos específicos, roles de usuario y tipos de datos. Saving Changes...
Expectation: I see AI as a tool that can take care of repetitive and time-consuming tasks, like meeting notes, tracking actions, organizing information, or creating first drafts of project updates. That would give project managers more time to focus on communication, solving problems, and supporting the team. Challenges: One of the biggest challenges is making sure the information AI provides is accurate and secure. There's also the learning curve of adopting new tools and finding the right balance between using AI to increase efficiency without relying on it for decisions that still require human judgment and experience. Saving Changes...
Anonymous
Expectation: I see AI as a tool that can take care of repetitive and time-consuming tasks, like meeting notes, tracking actions, organizing information, or creating first drafts of project updates. That would give project managers more time to focus on communication, solving problems, and supporting the team.Challenges: One of the biggest challenges is making sure the information AI provides is accurate and secure. There's also the learning curve of adopting new tools and finding the right balance between using AI to increase efficiency without relying on it for decisions that still require human judgment and experience.
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