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.”
Totally agree, the first step should be to clarify what type of AI is required and what is expected of it, and then to determine the costs and feasibility before taking any further decisions or measures. Saving Changes...
AI is the how, not the why. When the call to 'use AI' comes up, I always redirect the focus to the intended outcome first. Once we know exactly what we want to achieve, we can intelligently map out the right tools and strategy to make it happen. Saving Changes...
When someone says "We should to use AI" as a PM I need to reconstruct the goal from the intended value to the beginning of the project like available data. Saving Changes...
JOHANA LEONFunctional Manager| Universidad Industrial de SantanderBucaramanga, Santander, Colombia
When the organizational strategy is unclear to all members of an organization, the purpose of a specific role is lost; consequently, the focus shifts to meeting effectiveness indicators while neglecting the efficiency sought by the organization's owners. AI tools should not merely support process performance but rather be conceived as an additional organizational process in their own right. Saving Changes...
Paul WaggonerProgram Manager| Consultant - FreelancePapillion, Ne, United States
Again the key question is "why is the project being requested and what problems are expected to be resolved. Excellent comments in this discussion have been posted by others. Saving Changes...
Using AI will help to get the Key details of huge data and that will help to make quick and better decision, definitely save your time. Saving Changes...
Coming from a project management background in the public and education sectors, I've found that the phrase "we should use AI" often hides several very different expectations. For one stakeholder, AI might mean automating repetitive workflows; for another, it means generating reports and presentations; for leadership, it often means predictive insights for decision-making; and for technical teams, it could imply deploying machine learning models or agentic systems. Unless these expectations are surfaced early, projects quickly suffer from scope creep, misaligned success metrics, and unrealistic timelines. One signal I look for is whether the conversation starts with the technology or the problem. If the first question is "How can we use AI?", we usually end up searching for use cases. If the first question is "What process, decision, or outcome are we trying to improve?", the solution often becomes much clearer, and sometimes AI isn't even the right answer. I've seen this repeatedly in implementation projects, where the most valuable role of AI wasn't replacing people but augmenting decision-making, accelerating routine work, and freeing teams to focus on high-value stakeholder engagement. As project managers, our role is to move the conversation from the excitement around AI to a shared understanding of the problem, the expected outcomes, and the value being created. Only then can we determine which type of AI, if any, is actually needed. 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 great analysis thinking .I agree Saving Changes...
PMI Team Member
