Dario MontecastroProject Manager| CitigroupJersey City, Nj, United States
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.
Excellent insight. Saving Changes...
Jennifer McGrathProject Manager| Inmar Intelligence u1mperial, u1o, United States
When someone says "Let's use AI" all I hear is we need to move faster. Most of the time that brings trying to move the cart before the horse. In my experience when this statement is being said, they are already feeling the pressure to move fast and building the right foundation for an "AI" project tends to fall to side. Setting expectations is key to a successful project. Saving Changes...
As a PM, use of AI in projects helps in plan and coordinates the project, manages resources, timelines, and risks, and ensures effective communication among teams. Saving Changes...
When someone says, “we should use AI,” I think the first step is to avoid jumping straight into tools or vendors and instead ask what problem they are actually trying to solve. “AI” can mean many different things. They may be looking for a chatbot, automation, prediction, better decision support, or simply a faster way of handling information. So I would first try to understand the current challenge, what is not working, what outcome they expect, and where human effort is creating delays or bottlenecks. For example, if a company says it wants AI to improve customer service, I would not immediately suggest building a chatbot. I would first ask what the real problem is. Are customers waiting too long for responses? Are staff spending too much time reading and routing requests? Are customers asking the same questions repeatedly? Once the problem is clear, then we can determine whether AI is actually needed and what type of AI would make sense. For me, the main idea is to move the conversation from “we want AI” to “this is the problem we need to solve.” From there, it becomes easier to think about the right data, people, system, risks, and whether we should buy an existing solution or build something ourselves. 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.
Thanks for the insightful discussion , Saving Changes...
It is of the perception it helps things move faster, eases our approach and a permanent solution is fixed Saving Changes...
Adeniyi OjoOjo Group LLCBrookshire, Tx, United States
In project scheduling, “use AI” can mean summarizing updates, forecasting dates, or changing the schedule. I can tell the difference by asking what decision we need to make. When these tasks get lumped together, people may trust a polished answer without checking the data or assumptions behind it. Saving Changes...
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