Excelente aportación, la IA es una oportunidad importante en los grandes procesos de liderazgo, administración y ejecución de proyectos Saving Changes...
Yes, I’ve definitely seen “AI” mean very different things in the same conversation. One person may be talking about simple automation, another may mean generative AI for drafting or summarizing. The signal I listen for is whether people are describing the tool, the workflow, or the decision impact. In project environments, that difference matters because each type of AI work has a different level of risk, validation, governance, and change management need. In my consulting work, especially around PMO optimization, Smartsheet-enabled workflows, governance, and AI readiness, I try to bring the conversation back to the problem being solved. Is the goal to reduce manual status reporting? Improve portfolio visibility? Predict schedule risk? Draft meeting summaries? Trigger follow-up actions? Those are very different AI use cases. AI adoption works best when it starts with problem definition, stakeholder alignment, workflow decomposition, and governance built in early rather than added after the fact. What tends to go wrong when everything gets lumped together is that teams either over-govern low-risk use cases or under-govern high-risk ones. A meeting summary tool does not need the same controls as an agentic workflow that updates project data, sends communications, or recommends portfolio decisions. For PMI practitioners, I think the opportunity is to act as translators: clarify the type of AI work, map it to the right governance and delivery model, and make sure AI augments the work instead of creating more noise, risk, or disconnected pilots. Saving Changes...
Greg BlevinsSenior Project Manager| J.E. Liesfeld Contractor, Inc.Henrico, Va, United States
Too often, the suggestion "we should use AI" is an un-informed grasp at a speedy solution. An increasing number of managers view AI as a search engine. AI is a tool. The artifacts (governing rules, processes, delivery systems) of the business must all be considered in the creation of the structure. Then, the 'tool' AI can be applied specifically to those elements which it can complement. The 'search engine' AI can not deliver a sustainable solution for the project. Saving Changes...
When someone says “we should use AI,” I see it as a starting point rather than a clear requirement. The first step is to pause and understand what problem is actually being raised and what outcome is expected. Often, the real need is related to improving efficiency, reducing cost, or making better decisions. Once that is clear, the focus should shift to whether the right data is available and if it is reliable enough to support a solution. Only after understanding the problem, the data, and the expected impact can we determine if AI is truly the right approach or if a simpler solution would deliver the same value. At the end of the day, the goal is not to use AI for its own sake, but to create meaningful improvements for the end users and the overall process. Saving Changes...
Robert DurhamManager, IT Projects - Sr. PM Corporate Financial & HR IT| PFG - Performance Food GroupLouisa, Va, United States
Feb 19, 2026 1:05 PM
Replying to Luis Branco
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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 agree with your observations. The first point of failure I have experienced each time is when the business leader wants us to leverage an AI solution (wanting speed and faster time to market) but when IT leaders hear the request they see the need for training AI, added cost for new experienced resources because the time to learn AI isn't available on the current team. Saving Changes...
The key question is not whether we should use AI, but what value we are seeking to create. A clear understanding of the desired outcomes, benefits, and success metrics provide the foundation for identifying where AI can generate a meaningful impact! Saving Changes...
Christiaan van den BergProject Management Consultant| Project & Business Consultancy Pty LimitedBurlington, Ontario, Canada
AI is used by individuals for speed to of obtaining information and knowledge. Improve quality of written work. Obtain structure for performing work. Saving Changes...
Anonymous
Should we be using multiple AI modules ? Different module may produce different outcomes ?
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.”
Should we being multiple AI models at the same time. Models may quantify risk significantly different. Saving Changes...
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