When asked to "consult" AI, I see it as an opportunity to speak, discuss, or open my eyes to a new point of view. Also, AI does not hurt the aspects of your job once it is consulting. Saving Changes...
Fetoon AlmalkiSaudi Mining Service CompanyRiyadh, 01, Saudi Arabia
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.
What often goes wrong when everything gets labeled simply as "AI" is that teams start expecting one type of system to solve problems meant for another. For example, a predictive model may be treated as if it can make autonomous decisions, or a chatbot may be expected to provide accurate forecasting. This leads to unrealistic expectations, poor project design, unclear accountability, and disappointment with results. Saving Changes...
Should it recognise something, predict something, detect anomalies, generate content, optimise choices, or act autonomously? What data do we have? Historical data, images, text, audio, transaction records, user profiles, operational logs, or real-time signals? What level of risk is involved? Is the output low-risk, advisory, or linked to decisions affecting money, safety, rights, employment, health, or compliance? Who remains accountable? AI can support insight, but accountability normally stays with people, especially in high-impact decisions. What does success look like? Accuracy, speed, cost reduction, better service quality, fewer errors, improved forecasting, or stronger decision-making? Saving Changes...
A common clue that people are talking about different things is when one person focuses on predictions, another on automation, and a third on chatbots, while all of them keep saying "AI." They are using the same word but describing different capabilities, risks, and success criteria. The conversation usually becomes clearer when you ask: "What specific outcome do you want the AI to produce Saving Changes...
It is recognised that AI can assist, do you agreed that Copilot is the best option to use as an AI tool for capturing informaiton and resume meetings? Saving Changes...
When someone says, “We should use AI,” how do you decipher what they’re really asking?
What they really mean is that they have a problem they need to solve, and what they’re really asking is what that problem is.
What signs help you differentiate between the various types of AI work? And what typically goes wrong when generalizing?
Once the problem is identified, you need to identify which AI patterns are related to that problem and could solve it. Generalizing doesn’t allow you to define a precise and correct solution.
Have you ever been in a conversation where “AI” meant different things to different people? What made you realize this?
Yes, which means that you must first identify the problem, assess who is affected by that problem, identify the AI patterns that could solve that problem, determine if you have enough data to apply AI, and explain to all stakeholders how the solution works, its feasibility, risks, and limitations.
Share your experiences on how to interpret the question "Should we use AI?" in the comments below.
First, you must identify the problem, assess who is affected by that problem, identify AI patterns that could solve that problem, determine if you have enough data to apply AI, and explain to all stakeholders how the solution works, its feasibility, risks, and limitations.
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, that was a very specific answer, and I think you have had this happen enough to be clear/. In traditional practices, I saw action to solve problems begin before the real problem was defined. This is even more important when using AI. 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.
Agree, this is a good summary. Any discussion must start within having the value or outcome pretty clear. This must be our north star to guide next steps and decisions. Can AI help in this first step? sure it is, but it will need our help (we are accountables) to decide wether its proposals are aligned with the outcome or not Saving Changes...
Anonymous
Mar 25, 2026 9:08 AM
Replying to Dwight Clarke
...
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--AI isn't always the answer! Saving Changes...
PMI Team Member
