When someone says "We should use AI". Then first need to unpack what problem we actually want AI to solve rather then start with model or tool. Need to understand what problem we are solving ,like we should us AI to improve our labor work which is done manually and who is facing the problem .Involve the person in it .What should AI do by breaking the requirement in action .What type of data like structured ,unstructured we have to use in analysis .What is expected from the data outcome ,so it will be like Problem -user -task -data -AI capability-outcome-measurement .If we get the answer then can think of the model or tool used like prediction and anomalies, recognition ,language ,recommendation ,hyper personalization ,Autonomous system ,goal driven system.
Saving Changes...
Rajasekhar TatavarthiSenior Project Manager| Home Team Science and Technology AgencySingapore, Singapore
The honest unpacking of "we should use AI" is usually: what decision are we trying to move, and are we ready to own it if the machine gets it wrong. Everything else, the tooling, the model, the vendor, is downstream of that.
Step 1: Find the trigger "We should use AI" almost always traces back to one of these:
Someone saw a competitor or vendor demo something impressive
A cost or headcount conversation is happening upstream
A task has become painful enough that anything sounds better
Leadership wants a visible signal of modernization
None of these are bad reasons. But they're different problems wearing the same sentence, and the fix for each is different.
Step 2: Ask what's actually slow, wrong, or expensive If nobody in the room can name the specific step that's broken, "AI" isn't a solution yet, it's a mood. I'd push for a concrete before/after: what takes how long now, what should it take instead.
Step 3: Ask who's accountable for the output Not who runs the tool. Who answers for it when it's wrong. If that person doesn't exist yet, that's the real gap, and no model choice fixes it.
Step 4: Separate "faster" from "different" A lot of AI requests are really asking to do the same judgment faster. A smaller number are asking to change who or what makes the call. Those need very different levels of scrutiny before you build anything. 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.
Signals that help me tell different kinds of "AI work" apart: is it prediction (forecasting, classification) or generation (text, image, code)? Is it a one-off analysis or a system meant to run continuously in production? Is a human still in the loop making the final call, or is it fully automated? And is it rules-based automation dressed up as "AI," or actual ML/LLM-driven inference? Those four questions usually separate "let's use AI" into something concrete. 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.
Good Saving Changes...
David JastrowCo-Founder, COO| RxChatBoxCherry Hill, Nj, United States
"We should use AI" can get misinterpreted as "Your skills are no longer required." AI needs to enhance the work that real humans do, not replace that work. If we are not evolving, we are regressing. Saving Changes...
Laura TorokRegulatory Affairs Program Manager| PhilipsRoseville, Mn, United States
What are you hoping to accomplish? What are your goals? Saving Changes...
lo que interpreto es una actualización que debemos realizar ya que la IA siempre ha existido solo es la actualización del conocimiento para poder aplicarlo en nuestra vida laboral. Saving Changes...
If some one says " lets Use AI " my first question would be , what is your understanding about AI. What do you want to achieve using the AI and how do you want to do it.
Do you have any roadmap or have you been using AI before.
If some one says let's Use AI , my question would be " what do you want to achieve using the AI and what is your primary goal ." Do we have any road map as to what we need from the AI and have we ever used it before ? this will help understand us the knowledge gap and requirement of future steps that need to be taken. Saving Changes...
PMI Team Member
