Khalid RagabGeneral Manager| Projects Holding WLLSharjah, Sharjah, United Arab Emirates
We need to ask whether using AI would have better ROI, simplify processes and enhance the quality of delivarables? Saving Changes...
Shawn BriscoeProject Delivery and Support Services| OTIF Inc.
A.I often finds a place beyond the right ball. The question of benefits delayed in face of a knee jerk response to strengthen fences...bolster gates. I have experienced where the mere suggestion of its use, and the seeming threat of future reliance on the technology has created new communication challenges to its introduction. Some of the classic findings in the early days of project management again arise, however with slightly more ferver, after all A.I goes beyond method....it reintroduces is to past errors in defining competition: Not on the basis of smarts and stealth, but on rigid competence. Saving Changes...
Ahmad ElabasiryEngineer| East Delta electricity production companyGammaliiah, Dakahliiah, Egypt
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.
From my experience, it means AI chat pod for most of people. And I think that it could take time for people to differentiate between the different types of AI Saving Changes...
Orlando GarciaStudent| Triumphant CollegeKilamba City, LUA, Angola
Signals for Differentiating AI Work
To prevent project failure, a PM needs to translate "AI" into its specific sub-discipline based on four core signals:
Signal 1: The Input-Output Type
Generative AI: Inputs are prompts/context; outputs are novel text, images, or code.
Predictive/Machine Learning (ML): Inputs are historical structured data (CSV, SQL tables); outputs are probabilities, classifications, or numerical forecasts.
Deterministic Systems/Rules Engines: Inputs are strict IF/THEN conditions; outputs are fixed actions (often mislabeled as AI).
Signal 2: Data Requirements & Pipeline Complexity
If the team asks for millions of cleaned historical rows, it's Traditional ML.
If the team asks for vector databases and retrieval-augmented generation (RAG), it's LLM/Generative AI infrastructure.
Signal 3: Deterministic vs. Probabilistic Acceptance Criteria
Does the client expect 100% exact, repeatable accuracy (e.g., financial ledger processing)? That requires deterministic automation or strict ML validation, not an open-ended generative model.
Does the client accept probabilistic outputs with a tolerance for variability (e.g., drafting marketing emails)? That fits Generative AI.
Signal 4: Testing & Quality Assurance (QA) Methodologies
For me personally in my experience, the key signals are the business problem, the expected outcome, the type of data involved, and how the AI will actually be used. AI can mean very different things, such as generative AI, predictive analytics, automation or AI agents. I have been in many conversations where people used “AI” as a broad term but had different expectations about what the solution should deliver. What usually highlights the difference is asking what problem are we trying to solve, who will use it, what value do we expect, and how will we measure success?
If everything gets lumped together as big “AI,” there is a risk of choosing the wrong technology, overlooking data, security and governance requirements, or focusing on the technology rather than the actual business outcome. Saving Changes...
As a Project Manager, when someone says, "We should use AI,” the focus should not be on the technology itself but on the business need behind the request. AI is a tool, and understanding the problem it is meant to solve is the first step toward delivering value. Key questions to ask include the following:
What problem are we trying to solve?
Identify the specific business challenge or pain point.
What outcome do we want to achieve?
Define measurable success criteria and expected benefits.
What is the current process?
Understand how the work is currently performed and where inefficiencies exist.
Is AI the right solution?
Determine whether automation, process improvement, or another technology may be more suitable.
Do we have the necessary data?
Assess data quality, availability, and compliance requirements.
Who will benefit from the solution?
Identify stakeholders, users, and the value they will receive.
What are the risks?
Consider security, privacy, accuracy, and adoption challenges.
Can we start with a pilot?
Test the concept on a small scale before investing in a full implementation.
When someone says, "We should use AI," I've learned that the real question is rarely about AI itself. It's usually about solving a business problem faster, reducing effort, improving quality, or enabling better decisions. Saving Changes...
Linda WolinPresident| WVVHWolin INC.Naples, FL, United States
Mar 19, 2026 11:15 AM
Replying to Omar Jabbar
...
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.
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.
I agree perfectly to this submission. Clarity of outcome is key to selecting appropriate tools. 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.
I think AI is useful for automating repetitive administrative tasks, data analysis to assist in decision-making on project solutions and predictive analytics for risk management to align expectations, minimize budget wastage and improve project efficiency Saving Changes...
I hope if dogs ever take over the world, and they choose a king, they don't just go by size, because I bet there are some Chihuahuas with some good ideas.
PMI Team Member
