Distinguishing AI types involves identifying signals like memory retention (reactive vs. limited memory), architectural complexity, and whether it operates as a "tool" (isolated task) or an "operator" (embedded in workflows). Misclassifying these—or lumping them together—causes AI to underperform, becoming expensive, unreliable, or hallucination-prone when simple reactive tools are tasked with complex reasoning Saving Changes...
This question describes a major problem for some, which is the failure to begin integrating artificial intelligence into daily life and work. Saving Changes...
Mohamed El-ZanatyQA/QC Manager| Kharafi National SAEAlexandria, Egypt
In my experience across international EPC and infrastructure projects, “we should use AI” often means three very different things depending on who says it.
- The executive usually wants predictive insights—risk forecasting, schedule optimization, or cost overrun alerts.
- The engineering lead** often means automating repetitive QC documentation, NDT report analysis, or welding parameter optimization.
- The client may simply expect “digital transformation” without a clear use case.
What helps me unpack it is asking: *What decision would AI help us make faster or better?* If the answer stays vague, I propose a small pilot on a well‑defined pain point—like using AI to cross‑check thousands of weld logs against WPS requirements. That shifts the conversation from hype to measurable value.
The biggest risk when everything gets lumped together is chasing “AI for AI’s sake”—investing in tools that don’t align with actual project needs, creating integration chaos, and disappointing stakeholders who each had different expectations. A quick alignment workshop with the sponsor, technical team, and end users usually surfaces those differences before the first line of code is written.
Saving Changes...
Corey ShenkenRegional Innovation Lead| Ontario Centre of InnovationWindsor, Ontario, Canada
When it comes to my line of work, "we should use AI" typically means to understand a challenge or issue at its core by effectively collecting and analyzing all relevant data points to ideate solutions or real-time decisions that can be made to address the challenge or issue. Saving Changes...
I agree with you, many times people are just pressured to use AI, but it is necessary to get the requirements clear first. Saving Changes...
Mohammed Alkhazraji Head of Digital Projects Management & Value Realization Office| Department of Government EnablementAbu Dhabi, Az, United Arab Emirates
I unpack it by moving the discussion from technology to need. I ask: what problem are we trying to solve, what outcome do we want to improve, and what is not working today? In many cases, the real request is not for AI itself, but for better automation, faster decisions, improved accuracy, or a better customer experience. Saving Changes...
Nowadays, many CEOs are under tremendous pressure from the board to implement AI in their business workflows. They receive the mandate to evaluate, develop, experiment with, and finally scale a solution that has a clear impact on profit & loss and cash flow in a very short time. Despite this mandate looking totally appropriate and aligned with business logic, I frequently notice that they really do not understand the basics of how AI works, how it will impact their business, and what problems it can solve; even they don´t know where to start. Frequently, the conversation is dominated by the tools' features instead of the company's strategic problems, how they were identified, what workflows must be redesigned, and how the proper level of data quality is guaranteed, how to identify the right tool for the right problem, and how to manage the change and people's resistance to change within the organization. Discussion of the regulatory risks AI implementation poses to the organization, data and model governance, etc. The absence of these issues in the conversation signals that our organization needs to reach a higher level of preparedness and maturity before undertaking an AI project. Saving Changes...
Without clear frameowork on AI, it is just a buzz word that will mean different thing to different people. With clear framework, it will have better understanding on what is AI, its use case, what it can do and cannot do, the critical success factors, its risk of hallucinatian, bias and lack of cultural-context and human empathy when design, built and lack of fit and proper trained data, trade-off, accountability, implication(ethics, regulatory compliance, legal), return of investment, people, AI, AI-Humanoid/Robotics complementation to bring its usefulness to uplift people quality of life. Saving Changes...
AMMAR OSMAN ABDALLAProject Manager| Alpha DataDubai, DU, United Arab Emirates
I believe from what I'm observing currently, majority of leadership personnel seem to have clear direction on AI and this is definitely embedded within strategies for short term road maps. Measurable success is already reflecting with enablement of immediate impact in corporate scorecards. The challenge that I see however, is the gap in knowledge within different corporate domains which definitely needs immediate action across all levels. Clarity on Data readiness is a key factor to identify low hanging fruits and quick wins for all engaged stakeholder's while exploring AI projects. Saving Changes...
Most individuals relate AI to LLM lihe ChatGPT. There are very few individuals who realize that AI is on an "agentization" process, evolving from the current assistant status.
Agentization refers to the process of turning an AI system (such as a LLM) into an autonomous agent that can:
Perceive its environment (through inputs, data, APIs, sensors, etc.)
PMI Team Member
