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.
Completely agree, the need to understand the need for the AI, often brings insights to what AI means to people. It often comes up as a solution to simplify a repetitive annoying task/process. Many a times the need for AI comes after broken/stalled processes block agility and scale. Forcing the organization to manually solve urgent issues while simultaneously trying to include AI. 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 with this statement wholeheartedly. Over dependence on AI is possible when we underestimate human potential and overestimate the use of tools. 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. Saving Changes...
A member of my team is recommending our first foray into using AI on our project. The approach appears to include a mixture of assistant, predictive, generative, and agentic. I am struggling to understand how the different approaches will work together. Saving Changes...
A specific example is an AI chat we implemented in the company to provide customer support on service related issues only to be providing generalized responses or giving alternative response to contact to customer service. Hence, response is delayed and action is not taken in time.
For an AI chatbot to be effective in this context, it should provide solutions to simple issues which are mundane.
An AI that interfaces with the customers should be able to tell the difference between technical support requirements of a service provided to a customer from customer usability issues. The data and the rules provided to the AI should be sufficient enough for the AI to differentiate between technical support requirements and customer usability issues 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
I agree with this assessment of agentic ai as well. Utilizing ai supports the need for teams to work smarter, increasing productivity as well as creativity. Ai is not a cure-all, however, a human-first approach should improve ai output. Saving Changes...
I would unpack “we should use AI” as a request to clarify what specific problem needs improving, what part of the workflow AI would affect, and what measurable outcome is expected. 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.
"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 entirely agreed with above idea. Saving Changes...
Organization first need to understand where, when and how to implement AI. Automation of mundane Workflows is an important first step, but I can not represent anywhere near the full potential of AI to an Organization. Saving Changes...
PMI Team Member
