Diane DanielsPM III| Health NetShingle Springs, CA, United States
Responding to the question for what is really being asked when someone says, "We should use AI". this opens up a multitude of question for what the overall desire is. Do we need to solve an immediate pain point, keep up with competitors, or as a means of efficiency. Ultimately, it all applies but it is critical to understand what the specific business problem is and the root cause of the problem before deciding on AI direction. Saving Changes...
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.”
Thanks for laying this out so clearly. I agree that one of the biggest challenges in AI discussions is when the term "AI" gets used as a catch‑all without defining the actual intent. The distinction between automation, prediction, and content generation is critical, and it really does shape everything—from data requirements to stakeholder expectations. Saving Changes...
I've actually run into this a few times recently. The moment someone says, "Let's use AI," I stop the conversation and ask, "What do you want it to do?" I've noticed that everyone has a different definition of AI. Some people are thinking about ChatGPT, some want predictions, some want automation and others just want reports faster. That's usually where the confusion starts. I've found it's much easier to break the problem down first. Does the system need to recognize something, predict an outcome, detect unusual activity or simply answer questions? Once that's clear, the conversation becomes much more practical and it's easier to decide whether AI is even the right solution. One thing I've learned is that not every business problem needs AI, and that's okay. Sometimes a simpler solution gets the job done better.
...
1 reply by Melvin Awute
Aug 05, 2026 7:28 AM
Melvin Awute
...
Khushboo Verma -
That is very true. AI can perform a whole lot of different tasks. It is important to know and understand what the problem is and see how best AI can be integrated in the solution. That is, if it is even necessary at all. Very good explanation on your part.
Saving Changes...
Wozuru DikeProject Accountant / Manager| The Tunstall Partnership LtdNottingham, , United Kingdom
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.
While I agree with your three signal approach (decision proximity, problem clarity and Accountability design) to understanding what an Ai work could entail, I think the list is inexhaustible. A basis team understanding of the mental model would guarantee a good framing that keeps the focus on the problem. This helps the team identify the Ai pattern, become intentional about tools, data requirements, personal etc and helps provide clarity about project constraints, dependencies and risks as well as match stack holders expectations with project outcomes.
Perfect question. should be supported by analysis as how it can contribute to the daily routine and how can this be scaled? having AI just as a trend would be a waste, especially if org readiness fails to support it. Saving Changes...
I had the opportunity to highlight what was interesting about the question posed during the company evaluation: "Why we use artificial intelligence?" PMI PMO of the Year Awards 2026. ✅ Innovation, AI, and sustainability are no longer optional — they are becoming core PMO capabilities
...
1 reply by Melvin Awute
Aug 05, 2026 7:36 AM
Melvin Awute
...
Giorgi Lobjanidze -
I agree with you.
A few years back, social media was a rising internet plague. Business and individuals who adapted and integrated it into their operations are reaping the results today.
There is no longer the debate about if social media should be integrated today. It has become a core capability in businesses and project management today.
It is the same with AI today. It is innovation.
This is just to add to what you have already highlighted on. I am a beginner in the corporate world and looking to do extremely well.
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.
what’s really being asked when someone says “we should use AI” in the comments below. Great! For me and through my work experience when some says we should use AI, it means we should clean up our quality of work using AI, for instance improve writing of the project reports and also use AI to analyse the data submitted such that the analysis aids decision amking. Saving Changes...
Wozuru DikeProject Accountant / Manager| The Tunstall Partnership LtdNottingham, , United Kingdom
Business alignment should be at the core of any decision to use Ai as no single Ai pattern can address all business needs and meet stack holders expectations. A clear understanding of the business needs will help address if Ai is necessary, what kind of Ai is needed based on identified pattern, data needs and availability, etc. The success of any Ai project depends largely on the mentality of the project team than on the technology itself. When projects fail, it’s not the Ai tool that failed, it mostly stems from a lack of proper alignment of business objectives with project expectations and final outcomes by the project teams. This is a mis-match responsible for most Ai project failures. However, the iterative nature of Ai projects makes it easy to reassess, re prioritise and realign business objectives with outcomes before further resources are wasted.
...
1 reply by Melvin Awute
Aug 05, 2026 7:24 AM
Melvin Awute
...
Wozuru Dike -
I especially like how you made reference to the fact that the AI tool is not responsible for the failure of the project. AI is just like another project team member. If its efforts are not aligned with the project objectives and business aims, the results it will achieve will not be in alignment with where the project is headed.
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.
so you start with ChatGP and ask question about your project and requirement and ask question of which AI Model is best suitable to your Project and what is intended to accomplish Saving Changes...
PMI Team Member
