“AI” often means different things to different stakeholders. I’ve found the key is to clarify whether we need identification, prediction, or decision support; otherwise expectations quickly become unrealistic. Saving Changes...
The terminology AI is so vague that it creates confusion in the business world. The use of AI has spread to all industries and could refer to something as simple as having meeting notes transcribed, to fully generating a project plan, with complex tasks, tracking, prototyping, accounting and many other aspects during the project lifecycle. We have now arrived at the point where AI is as unspecific as the term Internet.
Continued use and development of AI tools will soon shift our user language to specific tool names rather than the broader description of an burgeoning industry. Saving Changes...
Ajibola RobinsonProject Manager| Arlington Public SchoolsAlexandria, Va, United States
I believe when a team or person says, "We should use AI," I feel there are two distinct "outputs" being considered in the use of AI, and a lot of people may not yet fully grasp the "Ask" of AI.
One output is simply using AI as a super information gathering tool, which allows people and teams to get access to up-to-date current information about any number of issues across any industry and platforms. However, the use of AI here to simply obtain better information and not make decisions may not improve efficiency.
Another possible output is using AI not only to gather information and metrics et al, but also to proceed with decision-making based on the information and deeper insights the team or person now has via access to AI.
The second output with decision-making via AI in many cases will also result in more efficiency, but I feel there needs to be a way to also validate information coming in via AI sources, and a lot of additional oversight is needed to prevent errors. Saving Changes...
This is an impressive analysis to the problem at hand. I definitely agree to this submission. Saving Changes...
Amro AliTechnical Designer, Developer, Trainer| City of TorontoToronto, ONTARIO, Canada
As discussions increasingly reference the use of AI, it is important to recognise that these conversations often reflect strategic objectives such as improving efficiency, managing delivery risk, or reducing operational overhead. The focus should be on clearly defining the intended business outcomes and success measures before determining whether AI is the appropriate enabler. Saving Changes...
Anthony ScarpaceMechanical Engineer| IMEGNorth Tonawanda, Ny, United States
Mar 25, 2026 9:08 AM
Replying to Dwight Clarke
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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.
When someone says “we should use AI,” they’re usually not making a technical request—they’re expressing a hope, a pressure, or a signal. Your job is to uncover which problem they actually want solved and what success looks like.
1. Separate the signal from the request That sentence often means one (or more) of these:
“We feel behind.”
A credibility or competitiveness concern.
“We want efficiency.”
Cost, speed, or scale pressure.
“We want better decisions.”
Data overload or uncertainty.
“Leadership told us to.”
Top‑down mandate without clarity.
“We heard a success story.”
Anecdotal influence, not a defined need.
Before talking about models or tools, reflect it back: “When you say use AI, what outcome are you hoping to change?”
2. Anchor the conversation on the problem, not the technology A useful unpacking question set:
What’s hard or slow today?
Where do humans make repetitive or inconsistent decisions?
What’s expensive, risky, or error‑prone?
What data do we already have but don’t fully use?
If the problem isn’t clear without mentioning AI, AI is probably premature. If removing the word “AI” makes the idea fall apart, it wasn’t a real problem statement yet.
3. Identify who the AI is for AI use cases fail when the “user” is vague. Ask:
Who will use or be affected by this?
Are they analysts, inspectors, managers, customers?
Will AI assist, recommend, or automate?
This clarifies feasibility fast:
Assist → low risk, high adoption
Recommend → governance needed
Automate → high trust + controls required
4. Clarify the job to be done Translate “use AI” into one of a few concrete job types:
Generate (text, drafts, reports, summaries)
Classify (documents, images, cases)
Extract (fields from PDFs, forms, images)
Predict (risk, failure, demand)
Detect (anomalies, fraud, non‑conformity)
Optimize (routing, scheduling, allocation)
If none fit cleanly, pause - forcing AI rarely ends well.
5. Surface constraints early (this is where realism enters) Gently test assumptions:
Data: Do we have enough, and is it usable?
Accuracy: What happens when AI is wrong?
Compliance: Legal, regulatory, or ethical limits?
Integration: Where would this live in the workflow?
Change management: Will people trust or resist it?
This reframes AI as a socio‑technical system, not just software.
6. Ask for a success metric - then right‑size the solution A powerful question: “How will we know in 6 months that this worked?” Examples:
20% faster processing
Fewer manual reviews
Better consistency across inspectors
Improved audit traceability
Often, a simple rule engine, dashboard, or automation meets the goal better than advanced AI. That’s not failure - that’s good design.
7. Reframe the original statement By the end, you should be able to restate their idea as: “We want to [improve X] for [these users] by [doing this task better/faster/cheaper], under [these constraints]. AI might help here.” If they agree with that sentence, now you can talk about 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.
AI is definitively necessary in leading projects and team now a days. Serious problems when it comes to bring technology to the teams that most probably will do a better job that the employees themselves. We need to prepare AI professionals before making big shifts in the modus operandi. Saving Changes...
Ontresicia AveretteSenior Project Manager| Huntington HospitalCa, United States
Feb 22, 2026 7:45 AM
Replying to Sergio Luis Conte
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The first thing is to clarify what AI means. Human beings are using AI from more than 50 years ago. We are surrounded of AI entities embeded inside refrigerators, air conditioners, cell phones, etc, etc, Unfortunately in the last time some people and organizations are contributing to the general confusion using generative AI as a synonim of AI.
When someone suggests we use AI, the first thought is that we can use it to forecast budgets and develop clearer, more detailed measures of the project's status and success. Saving Changes...
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