When I hear, ‘we should use AI,’ I treat it as the start of a problem-framing discussion, not a technology decision. I first clarify the business problem and intended outcome, then determine whether AI is the best fit. Only then do I explore which AI capabilities or tools are appropriate. Saving Changes...
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.
I agree, problem first, fit second, technology third. Saving Changes...
Hassan MasoodCountry Sales Manager| Aviat Networks (S) PTE LTDIslamabad, Pakistan
For me, the signal is simple: can they clearly define the problem and expected outcome? If not, “AI” is just a placeholder. What goes wrong is everything getting lumped together—automation, analytics, and ML all called “AI.” That creates hype, wrong expectations, and overcomplicated solutions. I’ve been in plenty of conversations where each stakeholder meant something different. The moment you ask “what decision are we improving?” things usually get clearer—or it becomes obvious AI isn’t even needed. In most cases, the shift is from “let’s use AI” to “what’s the simplest way to solve this problem?” Saving Changes...
There is no one good answer to this question but asking back what one really needs to achieve using AI in their day to day work. What efficiencies one looks for in their daily workflows and overall performance valuations. Saving Changes...
Letting the machine help us do the boring, repetitive, or complex thinking faster. Letting machine Read a lot of information quickly and find patterns humans might miss and Suggest answers, predictions, or actions. Saving Changes...
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.
Honestly? Every time someone says “we should use AI,” I’ve learned to slow down and ask… use it to do what exactly?
Because I’ve sat in meetings where the sponsor meant automated reports, the tech person meant a machine learning model, and the client was picturing a chatbot. We were all in the same room hearing the same word but having completely different expectations. Nobody caught it until scope was already a mess.
The verb is usually your first clue. Automate, analyse, generate, create etc. they’re not the same conversation, not the same budget and definitely not the same project.
Lately, my favourite question to cut through the noise is: “What does your Monday morning look like after this is live?” Their answers to this question tell me everything. Saving Changes...
Por lo general en el ámbito de proyectos de inversión, en Peru no se enfatiza el concepto de IA en los procesos de cada proyecto, generalmente enfocado en Proyectos de Inversion Publica, ya existe un sistema en el que se enfocan las inversiones y se determinan los procedimientos según la tipología de cada proyecto. Saving Changes...
Mohammed Munir YounisSenior Business Analysis Consultant based in Vancouver - Canada| Nisaba Consultancy Services Inc.Surrey, British Columbia, Canada
Feb 19, 2026 1:05 PM
Replying to Luis Branco
...
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.
I think one of the basic issues with failures in adopting AI solutions (process wise), is that teams fail adopt the technology, without really looking at whether processes and people are not usually automation ready. This starts with a good change management plan that takes into consideration the assessment of the readiness of the current state (both people and process wise) before making a decision on what is the logical next step. Saving Changes...
PMI Team Member
