Absolutely — this happens all the time in projects. One of the biggest signals that people are talking about different kinds of AI is when the conversation jumps straight to “let’s use AI” without anyone explaining what problem we’re solving.
In my experience, a few things usually tip me off:
- Vague language: When someone says “AI” but can’t clarify whether they mean automation, analytics, machine learning, or generative AI. - Solution-first thinking: If the idea starts with the tech (“we should use AI”) instead of the outcome (“we need to reduce manual effort in X”), it’s a sign the team isn’t aligned. - Unrealistic expectations: When people assume AI can magically fix everything — from data quality to process gaps — that’s usually a clue that we need to slow down and define scope properly. - Missing prerequisites: If no one mentions data, governance, or integration, it’s clear the conversation is still at the buzzword stage.
What tends to go wrong when everything gets lumped together is simple: We end up designing the wrong solution for the wrong problem. Teams overestimate what’s possible, underestimate the effort, and skip the foundational steps like data readiness, user training, and change management.
Whenever someone says “we should use AI,” I’ve learned to gently ask: “What outcome are you hoping AI will help us achieve?” That one question usually reveals whether they mean automation, insights, content generation, or something else entirely.
It keeps the conversation grounded and helps everyone get on the same page before we start talking tools or architecture. Saving Changes...
When someone casually says, “we should use AI” in a project management context (like PMP), it’s rarely a precise request. It’s more of a signal — and unpacking it means clarifying what problem they want solved, how AI fits, and what outcomes they expect. Here’s how you can break it down: h2Layers to Unpack/h2
Problem definition Are they pointing to inefficiency, lack of insight, or repetitive tasks? AI is a tool, not a goal — so you need to ask what pain point they’re trying to address.
Scope of AI use Do they mean predictive analytics, automation, natural language processing, or decision support? “AI” is broad, so narrowing down the type of application is critical.
Value proposition What benefit do they expect — cost savings, speed, accuracy, or innovation? This helps you measure success later.
Integration feasibility Do you have the data, infrastructure, and skills to actually implement AI? Many “we should use AI” statements ignore readiness.
Risk and governance How will bias, compliance, and accountability be managed? AI introduces ethical and operational risks that PMP frameworks emphasize.
Stakeholder alignment Is this a top-down directive, or grassroots enthusiasm? Understanding who’s pushing for AI clarifies whether it’s strategic or opportunistic.
In PMP terms, “we should use AI” is really a business case initiation moment. It belongs in the Initiating Process Group where you define objectives, constraints, and success criteria. Treat it like any other proposed solution: validate assumptions, assess feasibility, and align with project goals. Saving Changes...
Most individuals relate AI to LLM lihe ChatGPT. There are very few individuals who realize that AI is on an "agentization" process, evolving from the current assistant status.
Agentization refers to the process of turning an AI system (such as a LLM) into an autonomous agent that can:
Perceive its environment (through inputs, data, APIs, sensors, etc.)
Make decisions based on goals
Take actions using tools or external systems
Adapt based on feedback or changing conditions
AI can be a powerful assistant, but it’s essential to have a foundational understanding of the topic and know how to ask the right questions in order to get the most accurate and meaningful responses. Saving Changes...
I hear different things when different people say “we should use AI”. When it is a colleague/teammate, the suggestion is usually helpful because they understand the working level tasks. When I hear it from leadership, I sometimes hear a disconnect between the practical use of AI for the desired outcome. Sometimes I experience a hope behind the words that AI can perform a task (that it may or may not be sophisticated enough to perform). Sometimes I hear the desire to impress our clients or company leadership to say we used AI to complete a task. Sometimes, I hear a new way of using AI in a new scenario, and I learn something valuable that I continue to apply in future AI-use scenarios.
The most important thing to do when I hear that phrase, is ask follow up questions. What are you hoping to accomplish? What is the outcome? How do you plan to prompt AI to obtain that result? Is there anything else that needs to be considered to achieve this outcome? Asking follow up questions are key to determining the best path forward, with or without AI.
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.
Luis, I have learnt a lot from your submission. Thank you for sharing. Saving Changes...
In my opinion, when someone says, “We should use AI,” they are acknowledging that this technology is here to stay and that we should learn how to leverage it effectively. AI can help us save time, improve productivity, and focus on tasks that require human judgment and creativity. Saving Changes...
Antonio GonzalezSenior Enlisted Leader| US Army Executive Protection Directorate (CID)
When someone says, “we should use AI,” my first reaction is usually that they don’t yet understand AI well enough to use it effectively. Successful AI adoption starts long before selecting a tool. It requires expectation management, risk analysis, and a clear understanding of the problem you’re trying to solve. Simply deciding to “use AI” without defining the business need is putting the cart before the horse. Organizations also have to invest in human capital. People need training, governance, and the time to learn how to identify where AI can genuinely add value. The real question isn’t “How can we use AI?” but rather, “Which parts of our processes should we examine, and where can AI produce meaningful, acceptable, and manageable outcomes?” Without that level of foresight and investment, “we should use AI” often becomes a solution in search of a problem. Saving Changes...
Rami BilalProject Manager| Engineering Principles Company For Engineering ConsultancyMakkah, Saudi Arabia
Artificial intelligence is considered the weapon of the modern age in accomplishing tasks efficiently and easily. Saving Changes...
Marcin ScholkeSoftwaredeveloper, Architect| LOV111VOL.comCottbus, Germany
When people say ‘let’s use AI,’ I ask: are we solving a problem or just chasing a trend? Today AI reads data; tomorrow it may interact with the human brain itself. The biggest question will not be whether AI is powerful enough -,- it will be whether humans are prepared for that level of connection Saving Changes...
PMI Team Member
