The AI-Empowered Professional: Why the Same Tool Gives One Person Magic And Another, Mediocrity: Reflections on the Plenary Session of RISE ICREATE 2026

By Genevieve B. Kupang

May 25, 2026, Binalonan, Pangasinan. There is a question that should unsettle every educator, researcher, and leader who has ever opened an AI tool with great expectations and walked away with something disappointingly generic: why does the same artificial intelligence give one person something transformative and give another something that would have been faster to write by hand?

I have sat through a considerable number of AI presentations at conferences and international forums across Asia and beyond. I can say with conviction that what Dr. Panos Panagiotakopoulos of the School of Global Business and Dr. Kent Peel of the School of Public Safety, both of Seneca Polytechnic, Canada, did at RISE ICREATE 2026 was something I had never encountered before. For the first time, in all my engagements with the subject of AI, I heard it explained through the metaphor of a restaurant. A restaurant with a kitchen, a chef, a pantry, a sous-chef, and a diner with guests already waiting at the table. It was, in a word, brilliant. And it changed everything about how the room received what followed.

This was the intellectual setting of the RISE ICREATE 2026 plenary session, where Panagiotakopoulos and Peel delivered what I can only describe as practically grounded and conceptually honest engagements with AI that I have witnessed in an academic conference setting. Their presentation deserves to be reflected upon. What they offered participants was more than a framework for using AI. It was an invitation to reclaim human agency in an age that is rapidly outsourcing thinking to machines.

A Framework That Cuts Through the Noise

Their central argument is simple, yet its implications are profound. They proposed that the usefulness of any AI system is not a fixed property of the technology itself. It is, rather, the product of four interdependent factors: the Harness, the Model, the Tools, and You. Expressed as a multiplicative equation, the logic is unforgiving: weaken any single factor, and the entire result diminishes. Set any factor to zero, and the whole enterprise collapses.

The Harness, they explained, is the environment surrounding the AI. The harness is the application or platform through which the model operates, like ChatGPT, Microsoft Copilot, Claude, and Google’s NotebookLM. These are not interchangeable containers holding the same substance. They are different restaurants with different kitchens, menus, and rules about what the chef is permitted to cook. The harness is the most invisible factor and the most decisive. Two professionals using the same model through different platforms will produce results that bear little resemblance to each other. Not because of their skill, but because the restaurant had already decided what was possible before they typed a single word.

The Model is the chef, the engine of intelligence within the kitchen. Frontier reasoning models are built for hard, multi-step thinking. Smaller, faster models are built for high-volume routine work. Choosing the wrong model for the task is a technical oversight. It is the difference between a thoughtful answer and a confidently wrong one. In academic contexts, that distinction carries real consequences. World-class generalists, specialists, and line cooks all wear the same uniform, they reminded us, but they are not the same. You do not ask a line cook to design the tasting menu.

The Tools represent what the AI can read and what it can do. They are the pantry stocked with ingredients and the sous-chef capable of acting on instructions. They drew a distinction between context in, meaning the documents, data, and live information the model can access, and action out, meaning the tasks it can execute like editing a spreadsheet, sending an email, running code, navigating a website. A Michelin-starred chef stranded in a desert is making canned beans, they said. The same chef in a fully stocked kitchen with sharp knives, a working oven, and a sous-chef who can plate and run dishes produces a feast. A model that can read but cannot act is a chef who can taste but never plate. The gap between reading and acting is wider than most users realize. It is growing fast.

The Factor Most People Are Underweight

It is the fourth factor, however, that I found most worthy of sustained reflection — and most relevant to those of us who work in higher education, research, and institutional leadership. That factor is simply called You.

And here is where the metaphor reached its stunning power. You, they said, are the diner with guests waiting at the table. You ordered the meal. You briefed the chef. You have a boss, clients, or students, depending on what emerges from the kitchen. Whatever leaves that kitchen, You are the one who serves it, and You are the one who bears responsibility for it.

I felt the weight of that image settle in ways that no technical explanation had ever managed before. The guests at my table, students, colleagues, and the communities whose interests I represent in forums, do not see the kitchen. They do not see the chef, the pantry, or the harness. They see only what I serve them. And that places the moral and professional burden of discernment squarely back where it has always belonged: with the human being who made the decision to press send.

Panagiotakopoulos and Peel identified three jobs that no professional can delegate to any AI model, however sophisticated. The first is The Order or the prompt. Specific requests produce specific results. Vague requests produce vague ones. The quality of what the AI returns is inseparable from the quality of what you asked. The second is The Brief, or the situational context that no model can see on its own– your audience, your constraints, and what success actually looks like in your institutional and cultural setting. The AI cannot know these things unless you provide them, and providing them well is a skill that must be developed deliberately. The third is The Taste Test, or your judgment before the output reaches anyone else. Polish, they reminded the audience, is not proof. A beautifully written paragraph that is factually wrong, contextually inappropriate, or ethically questionable remains all of those things regardless of how fluent it sounds.

This last point carries particular weight for those of us in the Global South, where AI-generated content may reflect assumptions, datasets, and cultural frameworks shaped primarily by the Global North. The responsibility to taste before we serve is a professional courtesy. It is an epistemic and ethical obligation.

The Caution the Room Needed to Hear

One of the most courageous moments of the plenary came when the speakers turned to what they called the hidden danger of increasingly capable AI. Their message was counterintuitive and important: as AI gets better, its failures become harder to spot. AI capability does not advance evenly across all tasks. A system that performs at superhuman levels on one kind of problem may be catastrophically wrong on another. And the more polished and fluent the output appears, the less likely we are to question it.

The non-negotiable rule they offered was as simple as it was vital: always taste before you serve.

This principle has implications that extend far beyond personal productivity. It speaks to how we teach our students to engage with AI-generated content, how we design assessment environments that preserve the integrity of scholarly thinking, and how we build institutional AI frameworks that are grounded in ethics, critical judgment, and genuine human accountability.

The Hidden Footprint

The plenary did not stop at professional empowerment. In a section that I found deeply resonant with my work in peace education and environmental advocacy, Panagiotakopoulos and Peel surfaced the ecological cost of the AI revolution. This is what they called AI’s hidden footprint. The data they presented was stark. AI electricity demand is projected to grow fivefold by 2028, reaching up to 300 terawatt-hours annually, more than the entire United Kingdom consumes today. Every 100-word AI response consumes approximately 519 milliliters of water. Carbon emissions from AI servers between 2024 and 2030 are projected to reach 44 million metric tonnes, on a par with New York’s annual emissions.

These figures are not presented to discourage engagement with AI. They are presented to demand honesty about what our enthusiasm for these tools actually costs. We need to insist that professionals who claim to care about sustainability cannot compartmentalize that commitment away from the tools they reach for every day. For institutions in the Philippines, where communities already bear a disproportionate burden of climate disruption and resource extraction, this dimension of the AI conversation is central.

The restaurant metaphor finds its sobering extension here as well. Every meal served from that kitchen has a supply chain. Every AI response has an energy, water, and carbon cost. The diner who orders thoughtlessly and wastes what is served is complicit, given the context of planetary limits. This, too, is part of what it means to be the empowered professional Panagiotakopoulos and Peel envision, one who uses these tools with both competence and conscience.

Five Habits, One Mindset

The speakers closed with five concrete habits. Choose the restaurant deliberately. Know your platform before you type. Pick the chef for the dish; match the model to the task. Stock the pantry every time and give the AI the context it needs to work with. Brief like a manager, not a search engine. Share the audience, goal, and constraints, because situational context is half the meal. And taste before you serve. No AI output should reach your audience without passing through your judgment first.

Underlying all five habits is a single mindset that Panagiotakopoulos and Peel articulated with precision in their closing statement: useful AI is not a property of the model. It is a property of the system, and you are inside it.

A Synthesis Worth Carrying Forward

As the co-host and synthesis presenter for Day 2 of RISE ICREATE 2026, I had the privilege of sitting with these ideas across the full arc of the day’s sessions and returning them to the audience in the form of an integrating reflection. What I carried forward from this plenary into that synthesis, and what I carry forward now into this article, is the conviction that the most important AI competency for educators, researchers, and institutional leaders is not technical fluency. It is critical discernment, contextual intelligence, and the moral seriousness to understand that every interaction with an AI system is, at its root, a human act with human consequences.

What made the contribution of Panagiotakopoulos and Peel so memorable, and I say this having engaged with AI discourse across multiple international platforms, was not the framework alone. It was the metaphor they chose to carry it. Before that plenary, I had heard AI described in terms of neural networks, large language models, training data, and computational power. I had never heard it described as a restaurant where I am the diner, my purposes are the guests at the table, and the chef can only cook what the pantry holds and what I have had the wisdom to order well. That image will stay with me. It is the kind of metaphor that transforms how you inhabit it.

The speakers gave participants a gift: a rigorous, honest, and humanizing account of what it means to use these tools well. They reminded us that the difference between magic and mediocrity lies in the person directing it, the context they provide, the judgment they apply, and the responsibility they are willing to own.

That is a lesson worth teaching, worth building institutional policy around.

And worth sharing far beyond the walls of any single conference.

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Photo Credits: WCC Aeronautical and Technological College

Genevieve B. Kupang, PhD, Dean, Graduate School and International Relations Officer, Baguio Central University, Baguio City, Philippines. WURI Historian | WUNI-L Secretary | CAIRO, Member, Board of Directors. Peace Education Coordinator, WCCI. 

Published in reflection of the plenary session of RISE ICREATE 2026, held at WCC ATC, Pangasinan, Philippines.

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