One of the biggest signals for distinguishing different types of AI work is the expected outcome—whether the goal is automation, prediction, or content generation. For example, if the focus is on insights and forecasting, it’s likely predictive AI; if it’s about creating text, images, or code, it points to generative AI. What often goes wrong is when everything gets labeled simply as “AI” without clarifying the use case. This can lead to unrealistic expectations, poor tool selection, and misalignment with business objectives. I’ve definitely been in conversations where “AI” meant different things to different stakeholders. Usually, I notice it when requirements are vague—like “we should use AI to improve efficiency” without defining how. That’s when I step in to ask clarifying questions about the problem we’re trying to solve, the data available, and the desired outcomes. In my experience, the key is to shift the conversation from “using AI” to “solving a specific business problem with the right AI approach.”
Yes, we need to shift the conversation to "solve a business problem with right AI approach". I agree to this submission. 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.
This is an insightful analysis. Your proposal correctly identifies that the drive to adopt AI is often a response to business pressures for increased speed, efficiency, and competitive leverage, rather than a purely technological initiative. The framework you've outlined, based on the three signals of decision proximity, problem clarity, and accountability design, offers a robust method for clarifying intent. This approach is essential for aligning stakeholders and ensuring that the implementation of AI is a disciplined transformation, not just technological noise. By focusing on the deliberate design of responsibility, organizations can successfully transition from a "man in the loop" model to one of "man in control," ensuring that capability and accountability evolve in tandem. Saving Changes...
When AI is proposed, I first seek to understand the underlying business need by identifying the problem to be solved, the desired outcome, and whether AI is the most suitable approach. I then evaluate data availability, feasibility, and potential value before recommending a practical solution. Saving Changes...
Anil RahejaProject Manager| Tadweer GroupAbu Dhabi, United Arab Emirates
AI or Artificial Intelligence is a very broad term and has been in existence for a while now. Many industries have been using different kind of Algorithms. Google and Netflix have been using it for classification and ranking to provide users with relevant suggestions, while manufacturing Industry has been using it to automate tasks. Post COVID AGI came into prominence and since then has become a buzz word. Like mentioned by so many colleagues here, Its important to understand what we want to achieve, why we need it and who will be the users. What are the existing problems that we want to solve and what level of future proofing is expected. Once we have this basic clarity, we can move ahead with a clear problem definition and this solution. Saving Changes...
Anonymous
AI should be used to address challenges. Understanding these challenges is critical before assessing how AI can be used to create benefits. Understanding how competitors leverage AI should be a key factor when undertaking initial assessments. Saving Changes...
When someone says, "we should use AI," how do you interpret what they're really asking?
I interpret this question as a sign of the need to explore a new, more agile and faster approach to effectively deliver real value. However, AI isn't limited to generative intelligence; it also includes models that can be trained on one or more specific topics! Saving Changes...
I usually try to separate “use AI” into three buckets first: automate a task, generate content, or support a decision. From there, I try to understand the goal and how much "human" effort will be required vs. how much of the task we'll handle with AI. The part that tends to go wrong is people jumping to AI tools before they’ve defined the workflow, owner, and success metric. Saving Changes...
I agree with you, many times people are just pressured to use AI, but it is necessary to get the requirements clear first.
Well said. It helps to first pause and ask yourselves fundamental questions that will guide the direction of the project prior to jumping into AI. e.g., What problem are you trying to solve? Does the problem necessitate the use of AI? From there, the decision to apply AI or not emerges naturally and with far more intentions. Saving Changes...
I personally believe that using AI can be a great advantage, as long as we are clear about what we want to achieve with it, know what we are requesting, how we require the result, and we must be co-creators with the AI to achieve the final value. Saving Changes...
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