The interesting thing is that now everyone talk about AI and everybody claims for it, in reality we were using AI since a while but we called it Algorithms. The real breakthrough is the accessibility and the speed... The first question to ask is..."Whats the problem we would like to solve"? Saving Changes...
I agree that when someone says “we should use AI” we don’t have to take it as a must but instead think if and how AI could help in fostering the problem we are facing. Saving Changes...
For me, when someone says, “we should use AI,” the first step is to understand the business problem behind the suggestion. AI is a tool, not an objective, so I would ask questions such as: What challenge are we trying to solve? What outcome are we expecting? How will success be measured? This helps shift the conversation from a technology-driven approach to a value-driven one. As a Project Manager, I believe it is important to identify the underlying business need before evaluating whether AI is the most appropriate solution. In some cases, process improvement, automation, or better data management may deliver the desired results more effectively. By focusing on the problem, expected benefits, available data, and key success metrics, we can determine whether AI genuinely adds value and supports the strategic goals. Saving Changes...
Rohit DuaSiemens Energy, UKManchester, United Kingdom
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.
i completely agree with this statement.
Saving Changes...
Salem AlsubiaeTechnical Engineering Section Head| National Water Company
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.
h1 /h1Before we decide to use AI, what problem are we solving, what part of the workflow is the bottleneck, and how will we know it worked?” That turns a slogan into a concrete conversation about value, feasibility, and risk. Saving Changes...
I've lost count of how many times I've been in conversations where "AI" was a term tossed around like a buzzword, only to realize that everyone in the room had a different understanding of what it meant. It's as if "AI" had become a Rorschach test, with each person projecting their own interpretation onto it.
What usually tips me off is when someone says "We should use AI!" without providing any context or specifics. It's like they're waving a magic wand, expecting AI to solve all their problems without putting in the effort to understand what AI can actually do. That's when I know it's time to dig deeper and ask clarifying questions.
I recall one conversation where a marketing team was excited about using "AI" to personalize customer experiences. However, when I probed further, it became clear that some team members thought AI meant using machine learning algorithms to analyze customer data, while others believed it meant using chatbots to automate customer support. Meanwhile, the IT department was thinking about implementing a specific AI-powered tool to optimize their workflow. It was a classic case of everyone talking about AI, but no one being on the same page. To navigate this, I suggested we take a step back and define what we meant by "AI" in the context of our project. We broke it down into specific use cases, such as natural language processing, predictive analytics, or computer vision, and then discussed which ones were relevant to our goals. By doing so, we were able to have a more meaningful conversation about how AI could be applied to solve real problems, rather than just throwing the term around like a buzzword. It was a valuable lesson in the importance of clear communication and defining terms when discussing emerging technologies like AI. LET'S DISCUSS! What are some of your experiences with the term "AI" being used in different ways? How do you navigate these conversations and ensure everyone is on the same page? Saving Changes...
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