Before considering AI, we need to clearly define the problem, the expected outcome, the right methodology, and, most importantly, the business value and organizational impact.
In many cases, the statement “we should use AI” comes without any of this clarity and is simply driven by trend rather than real need.
AI is currently a major focus, and many organizations feel pressure to adopt it. However, while many want to implement AI, only a few truly understand what it means in practice and where it creates real value. Saving Changes...
It's very common, wherever I go, to hear people asking for the use of AI as if it were a cry for help. This request usually comes even before assessing the problem to be solved. Sometimes it sounds like a desire to outsource risk and responsibility. Saving Changes...
Ron TrosclairProgram Manager| AEVEX AerospaceRockwall, Tx, United States
Mar 25, 2026 11:29 AM
Replying to anonymous
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I agree with you, many times people are just pressured to use AI, but it is necessary to get the requirements clear first.
Requirements are still important. Especially when defining AI and how to use this new tool. 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 enjoyed reading your structured view of the issue. I too have found that a structured approach is necessary. I think the oldest problem remains the problem still. What do you want? Until the PM is able to clearly define the desired outcome there is no way to help guide the project in the correct direction. Saving Changes...
You need to find out what their expectations are: What specifically will it be used for? What benefits are you expecting from using it? Who will be managing/overseeing it? What are your implementation plans? Saving Changes...
We often turn to AI out of a lack of clarity: we don’t know exactly what the problem is, but we feel that “AI might help.” When someone says, “We should use AI,” they’re usually really talking about saving time, automating processes, reducing costs, or improving decision-making. A common mistake is attempting to implement AI before clearly defining the actual problem. The conversation should start by asking: What is failing today? What process would you like to improve? And do we really need AI, or just better processes and organization? The real challenge isn’t identifying where AI can be used, but determining in which cases AI provides quantifiable value rather than unnecessary complexity. Saving Changes...
We often turn to AI out of a lack of clarity: we don’t know exactly what the problem is, but we feel that “AI might help.” When someone says, “We should use AI,” they’re usually really talking about saving time, automating processes, reducing costs, or improving decision-making. A common mistake is attempting to implement AI before clearly defining the actual problem. The conversation should start by asking: What is failing today? What process would you like to improve? And do we really need AI, or just better processes and organization? The real challenge isn’t identifying where AI can be used, but determining in which cases AI provides quantifiable value rather than unnecessary complexity. Saving Changes...
Anonymous
Feb 19, 2026 1:05 PM
Replying to Luis Branco
...
Great question.
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.
in our organisation i have been asked to find ways of using ai to support our pmo teams resource capacity and capabilities by automating repetitive tasks. Saving Changes...
Tanya BoydDirector of Creative Collaboration| Project Success AcademyBaton Rouge, La, United States
I love using AI in practical ways to solve problems and help me to brainstorm scenarios better. I think that most of the misinterpretation stems from not having a shared language. Even when approaching AI usage, we need to go back to the basics of effective communication.
What is the problem we are trying to solve? How do we envision that AI will help us? How can we start incrementally, train, and then work our way up?
It also involves doing internal assessments and skills analysis to truly determine what level of understanding our teams have currently and then formulating a plan that bridges the gaps in a sustainable way. Saving Changes...
I think somebody should come up with a way to breed a very large shrimp. That way, you could ride him, then, after you camped at night, you could eat him. How about it, science?
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