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.
To quote the author - the real shift is not from the "manual" to "automated". Since the advent of machines man has long since been fascinated by the "Automation" of all kinds. There has to be a paradigm shift with regards to AI and it's usage. The HITL "Human-In-The-Loop" has to be flipped into "AI-In-The-Loop" - AITL. That is to say that Man has to be in control and reign in this "force" that can go astray if un-checked. Let it automate the mundane and the routine, and act as a "Smart-Assistant" while driving up plans, making tough decisions based on strategic insights that are not available to the human-eye at first-glance. Saving Changes...
"Separating disciplined transformation from technological noise'' is crucial, and so he needs to carefully pay attention to the steps. Saving Changes...
When someone in a meeting or proposal says, "We should use AI," from a project management perspective, this is rarely a concrete execution order. Instead, it is a "signal of an initiating project with ambiguous requirements."
Your core mission here is to deconstruct the true intent behind the words. While they are talking about technology on the surface, they are usually asking four core underlying questions: h31. What is the actual pain point? (Scope & Goal Management)/h3
Surface Statement: "Let's use AI!"
True Intent: "Can our current efficiency bottlenecks or business pain points be solved through automation or predictive modeling?"
PM's Interpretation: The stakeholder has usually spotted a problem—high costs, low efficiency, or underutilized data (e.g., overwhelmed customer service, slow data analysis, inaccurate forecasting). They want AI to be the silver bullet. The PM's first job is to clarify the "Why AI?", define a clear project scope, and establish Key Performance Indicators (KPIs).
h32. Is the ROI worth it? (Cost & Benefit Management)/h3
Surface Statement: "Implementing AI will save us a lot of time."
True Intent: "If I allocate budget and headcount to develop or procure AI, how much money will it make or save the company in the short and long term?"
PM's Interpretation: AI projects often come with hefty upfront costs (infrastructure, data cleansing, model training, or licensing fees). The stakeholder is actually testing the cost-benefit ratio. The PM needs to evaluate the timeline, resources, and capital required to build a solid Business Case that proves whether the project is worth the investment.
h33. Are our resources and data ready? (Resource & Risk Management)/h3
Surface Statement: "Everyone is using AI now; we need to keep up."
True Intent: "Do our current infrastructure, team capabilities, and 'data quality' actually support an AI project?"
PM's Interpretation: This is often driven by FOMO (Fear Of Missing Out). However, AI thrives on quality data (Garbage in, garbage out). When this idea is thrown out, the PM must trigger a risk assessment: Do we have clean data? Does the team have the capability to maintain the model? Or do we actually just need a simple automation script (like RPA) or an off-the-shelf SaaS solution instead of building AI from scratch?
h34. Can the organization handle the change? (Change Management)/h3
Surface Statement: "Let's replace this manual process with AI."
True Intent: "Once AI is introduced, how will our existing workflows be restructured, and will employees resist the change?"
PM's Interpretation: AI projects are rarely just technical projects—they are organizational change projects. Behind this statement lies anxiety or anticipation about future workflows. The PM must focus on Stakeholder Management, figuring out how to minimize user resistance and plan proper training programs.
h3 /h3The Bottom Line: In project management, when someone says "We should use AI," what they are really asking is: "How can we solve our current business problems using new technology, while keeping costs, risks, and resource utilization reasonable?" A PM's value lies in transforming that tech fantasy into a feasible, controllable, and deliverable project plan. Saving Changes...
The real question is how to respond to vague requests from executive leadership to use AI. Saving Changes...
Yijeng Vivian LiuTechnical quality manager| SAPScottsdale, Az, United States
For project managers, the values of AI brings is to reduce the effort or time for each task during planning and delivery. For example, using AI to prepare email and report for communication may reduce the manual effort and using AI to search for database about project risks for risk management. Saving Changes...
In capital projects and complex delivery environments, I often find that people use the term AI to mean completely different things. Some are talking about automating routine tasks, others are thinking about predictive insights, while some are referring to tools that generate content or support decision making. When everyone uses the same term but means different things, confusion quickly follows. What usually goes wrong is that the conversation starts with “we should use AI” instead of asking what business problem needs to be solved. Whether the goal is better forecasting, faster reporting, improved document quality, or earlier identification of risks, defining the outcome first makes the right solution much easier to identify. In my experience, the most productive discussions focus on business value, data quality, governance, and accountability. When those elements are clear, AI becomes a practical tool for improving project performance rather than just another buzzword. Saving Changes...
AI is generative data gathered from past incidents. There are billions of data in the form of text, audio and video. Maximum data are garbage for 70% of the population of the world. Only if one is knowledgeable is AI appropriate for her/him. I usually start by asking what “AI” is expected to do: generate content, classify data, predict outcomes, automate a workflow, support decisions, or replace a manual process. Those are very different types of work. The signals that help me separate them are the input data, the expected output, the level of human judgment required, the risk if the output is wrong, and who remains accountable for the decision. Problems start when everything is simply called "AI". A chatbot, a dashboard, a rules-based automation, a machine-learning model, and a generative AI assistant get treated as the same things. Then expectations become unrealistic, governance becomes weak, and teams often choose the wrong tool for the problem. In my experience, when someone says “we should use AI", the real question should be: what problem are we solving, what decision are we improving, what data do we have, and what level of accuracy, transparency, and human oversight do we need? Saving Changes...
When they say AI, I associate it with efficiency and the speed of deliverables that are submitted. And working in a regulatory faremwork, in my field, it is both a blessing and a challenge. Its a maze I'm trying to overcome but I know it gets even vast and deep. Most of the ''AI'' I use is to try and write effective prompts to generate risk assessments and effective test methods that I need to help write for my users. Saving Changes...
Elisa BezerraProject Manager| Capgemini PortugalFundão, Castelo Branco, Portugal
The first step is to understand the real business problem behind the request. The focus should be on the expected outcome, measurable value, and which process or decision needs improvement. In telecom projects, for example, AI should support strategic goals such as cost reduction, efficiency, customer experience, or faster deployments. A good project manager translates the AI idea into business impact, KPIs, and practical value. Saving Changes...
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