What AI is has been and always will be a moving target. Assembly language, Fortran and COBOL were all called AI in the past. Basically, every IT technology that accomplishes something that previously required humans gets labeled AI for a period of time. New workers see many things as the way things are done now as the way it's "always been" and experienced folks see last year's advances as a leap forward. So, generations of worker will see the definition of "AI" differently. The best way to handle these differences is to clearly define what you are working on and the technologies being used. That way you are acting intentionally which is going to be more effective and goal driven. Saving Changes...
For me AI is a big field, so you need to understand basically at least the technologies behind it, for example:
Machine learning
Deep Learning
Neural networks
Natural Language processing
Large Language Models
Robotics
Computer vision
In order to create:
Predictive models that classify customers that are prone to churn and help you make special strategies for each group
Chatbots that handle questions and simple requests for customers, students or citizens
Agents that handle the sales process from prospect to order
Drones that can identify if a plant in the field is being attacked by a fungus, insect or the weather, and help a farmer to control its vineyard. Even apply appropriate chemicals in the proper amount.
The applications of AI are so wide , us as project managers, need to learn the technologies applications and business cases to solve different types of problems. Saving Changes...
When someone says “we should use AI,” they’re rarely being specific. As a project manager, your job is to translate vague enthusiasm into actionable requirements. Here’s how to unpack what’s actually being asked. Diagnose the Problem They’re Actually Trying to Solve What to ask: • “What pain point are we trying to address?” • “What’s taking too long or consuming too many resources right now?” • “Where are we losing time, quality, or money?” Why it matters: AI isn’t a solution—it’s a tool. Someone might say “use AI” when they really mean: • We need to automate repetitive tasks (data entry, scheduling) • We need to analyze data faster (risk assessment, trend forecasting) • We need better decision-making (resource allocation, scope management) • We need to improve communication (summarizing status reports, generating documentation) Example: A stakeholder says, “We should use AI to improve our project tracking.” The real problem? Team leads spend 3 hours daily compiling status updates manually. Clarify the Specific Use Case Questions to ask: • “Which process or deliverable would this apply to?” • “At what stage—planning, execution, monitoring, or closeout?” • “Who would interact with this AI tool—team members, clients, leadership?” Define Success Metrics Ask: • “How will we know this worked?” • “What does success look like in 3 months? 6 months?” • “What baseline are we measuring against?” Measurable outcomes might include: • Time saved per week/month • Error reduction (%) • Cost avoidance • Schedule improvement (variance) • Quality improvement (defect rates, rework) • Team satisfaction/morale
“We should use AI” is not a requirement—it’s a conversation starter. Your job is to: 1. Translate enthusiasm into specific problems 2. Define what success looks like 3. Map it to measurable outcomes 4. Assess feasibility, cost, and timeline realistically 5. Scope it as a proper project change or new initiative By asking the right questions, you move from vague wishes to actionable plans—which is exactly what PMI standards expect Saving Changes...
Anonymous
Great question. However, the answer depends largely on the type of an organisation we are a part of. Saving Changes...
When someone says let's use Ai, they are usually saying let's come up with a system that is fast, effective and reliable in assisting the team meet their daily deliverables. It is often a signal to come up with a solution to optimize work flow. Saving Changes...
When someone says let's use AI, they are often signaling for a system that makes work flow more efficient and to stream line deliverables. It is usually your teams way as a PM to assist them in implementing a way of solving day to day tasks into a more stream line work flow. Saving Changes...
Paul WaggonerProgram Manager| Consultant - FreelancePapillion, Ne, 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.
From a PMP, its better to slow down and follow the several steps recommended, be patient. Excellent recommendations included that should be followed. Saving Changes...
Leon FrancisProject Management (CMMI Level 5)| NARTech Inc.Savage, Md, United States
The idea of not simply using AI, but truly understanding the business problems that AI can solve, is essential. While many people say “use AI,” the reality is that different systems are designed to address different types of challenges. For example, ChatGPT, cloud-based AI services, and Copilot each excel in different areas—for instance, cloud solutions may be more effective for development-related tasks. Being able to identify which AI tool is best suited for a specific problem would be extremely valuable. Equally important is recognizing the limitations of AI and understanding when it may not be the right solution. Saving Changes...
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