Scope ambiguity. When someone says, “We should use AI,” they are usually not defining a project—they are expressing an intention. The project manager’s job is to turn that intention into a clearly defined business problem with objectives, deliverables, risks, costs, and success metrics. As a project manager, instead of asking “What kind of AI do you want?”, I would ask:
What business problem are we trying to solve?
How is that problem measured today?
What specific process are we trying to improve?
What human decision are we trying to support or automate?
What data is available, and what is its quality?
What would measurable success look like in the next 90 days?
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Anonymous
Either they are i’ll equip, (new to tech), no solid understanding of expected results (not new but lack skill to predict results through training) tedious redundancies or some type of repeatable system thats makes since to hand off (more experienced) Saving Changes...
Alfonso GuevaraProject Manager III (PM3)| JaeVeo LLCDowney, Ca, United States
When someone says, “We should use AI,” I would ask them to please elaborate in what way. There are many factors to discuss first when deciding how AI would play apart in a project. There should be controlled tests done as well, prior to putting it into motion. possibly test in increment's or mock data. Adding unknown AI risk to a live/active project is unnecessary. We must also factor all the pro's and cons to asses if it would actually be beneficial or just create more issues or bottlenecks. Saving Changes...
Anonymous
This is an impressive analysis to the problem at hand. I definitely agree to this submission Saving Changes...
I would proceed with the 5 Why's methodology. Need to understand the problem we want to solve and after that see if AI might be one of the solution and analyze if this is the wanted solution that will bring the needed value. Saving Changes...
One of the biggest signals is the problem being solved. Sometimes “AI” means automation, sometimes analytics, and other times generative tools. What often goes wrong is treating them all as the same thing, which creates mismatched expectations, unrealistic timelines, and unclear outcomes. I've learned to start by asking, “What decision or task are we trying to improve? Saving Changes...
Rupesh GhelaniSenior Project Management| Evolit Consulting GmbHSt. Pölten, 3, Austria
Feb 19, 2026 1:05 PM
Replying to Luis Branco
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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.
Well structured answer! For me this is just another level of requirements analysis. Understand the business process and find the steps, which can be automated or supported with AI. Improve data, wherever you can. Saving Changes...
Christina MartinProject Management| Mastec Wireless ServicesUnion Grove, WI, United States
I would first clarify what problem we are trying to solve and what outcome we want to achieve with AI. Then I’d look at the specific tasks, risks, data, and human involvement needed to determine whether AI is actually the right solution. Saving Changes...
One of the main signals that helps me differentiate types of AI work is the problem we are trying to solve. For example, using AI to automate repetitive tasks is very different from using it to predict outcomes, analyze large amounts of data, or support decision-making. I have participated in conversations where the term “AI” meant different things to different people. Some people were thinking about generative AI tools such as ChatGPT, while others were referring to automation, machine learning, or predictive analytics. I realized this when the expectations, required data, and expected outcomes were very different. When someone says, “We should use AI!”, I think the first step should be to understand the actual business problem rather than immediately choosing an AI solution. If all AI concepts are mixed together, teams can create unrealistic expectations, select the wrong technology, or start a project without a clear objective. Defining the problem, expected value, available data, and success criteria first makes it much easier to determine what type of AI approach is appropriate. Saving Changes...
I have found that in many cases the discussion starts with "how do we incorporate AI" rather than assessing "where there are areas for improvement". AI tools may be the solution, but they may not. Properly defining the problem(s) or identifying gaps is always the first step. Saving Changes...
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