The first signal I look for is the actual business problem. Sometimes AI work is about prediction, sometimes automation, sometimes reporting, and sometimes just better search or better data handling. The confusion starts when everything gets labeled as “AI” without defining the purpose. When that happens, expectations become unrealistic, teams get misaligned, and people assume one solution can solve every kind of problem.
Yes, I have been in conversations where AI meant different things to different people. For one person, it meant chatGPT-like content generation. For another, it meant dashboards, automation, or advanced analytics. What usually tips me off is when people agree on the word “AI” but describe very different outcomes. That is usually the moment I realize we are not discussing one thing. we are discussing different needs under the same label. In my experience, when someone says we should use AI, the real task is to slow down and clarify the ask. I try to understand: do they want faster decision-making, reduced manual effort, forecasting, better customer interaction, or just something innovative-looking? From a project management perspective, that clarity is critical. Otherwise, AI becomes a buzzword instead of a solution. Saving Changes...
Before you can look at outcomes, a clearly defined business problem is the key. You need to know what you're solving for before looking at the path to get there. Saving Changes...
Most individuals relate AI to LLM lihe ChatGPT. There are very few individuals who realize that AI is on an "agentization" process, evolving from the current assistant status.
Agentization refers to the process of turning an AI system (such as a LLM) into an autonomous agent that can:
Perceive its environment (through inputs, data, APIs, sensors, etc.)
Make decisions based on goals
Take actions using tools or external systems
Adapt based on feedback or changing conditions
totalmente de acuerdo contigo , creo que el concepto de agentizacion describe totalmente lo que podemos de manera preliminar preguntarnos,, mi agente de IA puede gestionar esta tarea? y a partir de ahi, estabecer las directrices de ue debemos hacer nosotros para que la IA lo logre 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.
La claridad de propósito de lo que queremos lograr, la identificacion de con que agente de IA podems hacerlo y hasta donde se puede controlar son ahora tareas comunes, sin embargo es totalmente cierto que la responsabilidad de las respuestas y la verificacion de las mismas en la emocion de la rapidez con que se pueden lograr los datos se puede difuminar siendo un riesgo mayor a la tarea que se pretende lograr Saving Changes...
Nam NguyenProject Management| IBMBa Dinh District,, HN, Viet Nam
When someone says, “we should use AI,” they are rarely expressing a concrete requirement. More often, it is a signal of pressure or expectation—the pressure to move faster, operate more efficiently, or avoid falling behind. In my role as a management and project management consultant, my responsibility is to translate that signal into measurable business value, rather than deploying technology for its own sake. My first step is always to clarify intent: What problem are we actually trying to solve? Without a clearly defined desired outcome, introducing AI only increases complexity and risk. I typically rely on three signals to unpack what is really being requested. The first is decision proximity. Is AI being used to automate tasks, augment human judgment, or make decisions autonomously? These represent fundamentally different categories of work, each requiring different levels of governance, oversight, and risk management. The second is problem clarity. If AI is proposed before the business problem has been clearly defined and made measurable, misalignment and inflated expectations are almost inevitable. The third is accountability design. If an AI-driven recommendation turns out to be wrong, who owns the outcome? When ownership is unclear, risk tends to scale faster than performance. In my consulting practice, I’ve frequently encountered conversations where “AI” meant automation to one stakeholder, innovation to another, and cost reduction to a third. The disconnect becomes most visible when everyone agrees on the term “AI” but defines success using entirely different metrics. At that point, it becomes clear that the issue is not the technology itself, but how the conversation is framed. From a PMI perspective, the real shift is not from manual work to automation, but from “man in the loop” to “man in control.” Clear purpose, proper categorization of AI work, and explicit ownership are what separate disciplined transformation from technological noise. And in some cases, the most responsible project decision is not to use AI at all. Saving Changes...
Whenever AI comes up in discussions, it is a solution offered to increase speed, reduce manual steps, etc. but we hit the wall when it comes to what tool is best and how to deliver value. Saving Changes...
When someone says “we should use AI,” it’s important to unpack what they mean. First, clarify the problem definition—are we solving for efficiency, accuracy, cost reduction, customer experience, or innovation? Next, identify the scope of AI use: is it automation of repetitive tasks, generating insights through analytics and predictions, enabling creativity like content generation, or augmenting human work with copilots? Then assess data readiness—do we have clean, accessible, and ethically sourced data of sufficient quality? Consider value versus hype—is AI truly the best solution, or could simpler process improvements or automation suffice? Evaluate risks and constraints such as privacy, bias, compliance, and explainability, and ensure decisions can be audited. Finally, address implementation practicalities—who owns the system, how it integrates with workflows, and what training or change management is required. This structured approach ensures AI adds real value rather than being adopted for trendiness. Saving Changes...
The implementation of IA into a organization requires a transformation of the mind of people and understand the origins of data information. All infraestructures requires a robust data platform and processes with a high maturity. Saving Changes...
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