Yes, this comes up a lot. People often say “AI” but mean very different things, and you can usually tell because their expectations don’t line up. Someone might expects instant results, someone else talks about data problems, and another worries about cost or infrastructure. When someone says “we should use AI,” the best way to unpack it is to ignore the buzzword and focus on basics: (i) what problem are we trying to solve, (ii) what outcome do we want, and (iii)what would success actually look like (KPIs). Once that is clear, it becomes obvious whether AI is really needed—and if so, what kind. Saving Changes...
Mihran KochyanSr. Program Manager| Mihran Kochyan & AssociatesNovi, MI, United States
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.
This reminds me of the origination of the relational database (yes, I am that old). Leadership would interject with the design of databases, saying they wanted to be the first to convert. Some cases caused the early deployment to RDB with a repository that was not suitably designed.
We need to be careful selecting the right tool for the right situation Saving Changes...
"We should use AI" argument could be the first move snaking a clogged drain, where the problem to solve deal with a massive quantity of data or unstructured data. AI tools could give you a leg up, and help you use your elbow grease and rainy day fund wisely. Saving Changes...
In my experience as a project manager working on AI initiatives, one of the clearest signals to differentiate types of AI work is the level of clarity around the problem vs. the fascination with the technology.
When someone says “we should use AI,” I usually try to quickly understand:
- Are we talking about simple automation, advanced analytics, or actual AI/ML models? - Is there a clear use case with measurable impact, or is this driven by trend? - Do we understand the level of effort, data readiness, and organizational change required?
What often goes wrong when everything is lumped under “AI” is that:
- Unrealistic expectations are set (assuming everything works like ChatGPT). - The effort around data, integration, and governance is underestimated. - Simple problems that don’t require AI get overcomplicated, while real AI use cases are not properly scoped.
I’ve definitely been in conversations where “AI” meant completely different things to different people. The biggest tell is usually the language, some refer to prompts and assistants, others to models, and others just mean “automate something.”
That’s where the PM role becomes critical: translating “we should use AI” into “what problem are we solving, with what data, and what outcome are we expecting?”
In the end, it’s less about pushing AI and more about ensuring the solution, AI or not, actually delivers value. Saving Changes...
Right now I am seeing a stigma where older generations are looking at the use of AI with disdain. When I once said that I wanted to have AI help with ideas to a set of problems he looked at with me as if to say I was cheating. I would love to have a AI agent available that can perform the more mundane tasks in my job. Saving Changes...
Virginia KernSenior Technical Project Manager| DataStaffMatthews, Nc, United States
Mar 19, 2026 11:15 AM
Replying to Omar Jabbar
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I’ve been asked this many times, and my first response is always: what do you want to achieve with AI? Once the outcome is clear, we can define the right approach, tools, and path forward.
When someone says, “we should use AI,” I don’t treat it as a solution, I treat it as a signal.
In my experience as a Project Manager working on AI and digital transformation, that statement usually reflects pressure to improve efficiency, reduce costs, or keep up with competitors, rather than a clearly defined need.
So the first thing I do is unpack it by shifting the conversation from technology to problem. I try to understand: What is not working today? Where are the actual bottlenecks or pain points? What kind of data and decisions are involved in the process?
I also look for clues about what they really mean by “AI.” Sometimes they’re thinking about simple automation, sometimes analytics, and other times something more advanced, but it’s rarely explicit.
From there, my role is to bring clarity: Is this a problem that actually requires AI, or could it be addressed through process improvements or simpler solutions?
So instead of answering “yes” or “no” to AI, I reframe it into a more useful question: “What are we trying to solve, and what is the simplest way to create value?”
That usually leads to a much more grounded and productive conversation. Saving Changes...
Oscar MiganiProject Management| Rural energy AgencyDodoma, 3, Tanzania, United Republic Of
Mar 25, 2026 4:50 AM
Replying to Douglas Boyd
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It is recognised that AI can assist, but we need to obtain clarity as to what AI system is to be used as there are many.
it's true that AI are so many, as user we must know which one gives expected outcome, unless otherwise it will be considered as AI does not add value. Saving Changes...
When someone proposes “we should use AI,” the discussion is rarely about technology in isolation. More often, it reflects organizational pressures for speed, efficiency, innovation, or competitive advantage. The essential first step is clarifying intent. Three signals help distinguish what is truly being asked:
Decision proximity Is AI being applied to automate routine tasks, augment human judgment, or move toward autonomous management of objectives? These represent fundamentally different categories of work. The closer AI comes to consequential decisions, the greater the need for governance, traceability, and explicit oversight.
Problem clarity Is there a clearly defined business problem with measurable impact, or is AI being treated as the starting point? When solutions precede problems, misalignment and inflated expectations are inevitable.
Accountability design Who owns the outcome if an AI-driven recommendation fails? When responsibility is diffuse, risk scales faster than performance.
In many organizations, “AI” simultaneously signals efficiency, experimentation, and cost reduction to different stakeholders. Misalignment becomes visible when decision flows and ownership are unclear. A common tipping point arises when stakeholders use the same word — AI — but mean different success metrics. The real shift is not simply from manual to automated. It is from “human in the loop” to “human in control.” Without deliberate design of responsibility, capability increases while accountability erodes. Disciplined transformation depends on clarity of purpose, categorization of AI work, and explicit ownership. These elements separate meaningful progress from technological noise. Saving Changes...
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