Despite aiming for rational decisions in digital initiatives, we often fall prey to cognitive biases like the sunk cost fallacy and the tendency to stick with initial choices, as illustrated by the Monty Hall problem. Recognizing these ingrained habits is crucial, especially when models or projects aren't yielding desired results, prompting the need to question assumptions and be willing to "switch doors." Distinguishing between productive persistence and the trap of sunk costs is a key skill in navigating complex digital landscapes.
Better decisions often rely on data, but real-world scenarios involve uncertainty and reactions to others, where behavioral science and bounded rationality become crucial. A recent session explored this by asking graduate students to model such situations, moving from intuitive thought exercises to formal mathematical representations. The diverse models created highlighted that computational modeling is not just technical but also about framing and understanding the emergent behaviors arising from local interactions and constraints.
At the ABCOMP 2025 Annual Conference, I addressed compliance officers on AI governance in banking. Through interactive scenarios exploring credit scoring biases and alert system overloads, we examined accountability challenges in AI-driven decision-making.
In education, particularly in B-schools that use the case-method approach, assessments have traditionally moved beyond rote memorization, pushing students to think critically, applying concepts and insights more meaningfully. The case method approach is something I learned when I joined the Asian Institute of Management; it centers around real-world scenarios, often with open-ended questions and no single “correct” answer. This encourages students to weigh perspectives, make informed decisions, and articulate their reasoning—skills crucial in any business context. At AIM, we often joke that our students are wired to respond with 'it depends.'
I was recently invited to engage with the directors and C-level executives of Xurpas, through the Institute of Corporate Directors (ICD), to discuss the tremendous opportunities in Data Science and AI. It’s clear that the demand for AI and data-driven solutions is surging, particularly in regions like the Philippines, ANZ, and across ASEAN. Companies that recognize this shift have a unique opportunity to lead in this fast-evolving space.
The awarding of the 2024 Nobel Prize in Physics to John Hopfield and Geoffrey Hinton sparked a flurry of discussion in both the AI and Physics communities. This blog reflects on the evolution of neural networks from the lens of a physicist turned AI practitioner, and explores the fascinating intersection between these two fields.
Here, I share insights from my journey teaching Data Science and Ethics of AI at the Asian Institute of Management, where I explore the dynamic interplay between evolving industry challenges and the foundational principles of data science leadership. Each term brings fresh perspectives that continually reshape and enrich the learning experience, underscoring the importance of developing holistic and adaptable leaders in the field.
Here, I explore the critical role of comprehensive digital transformation in successful AI adoption within enterprises. Drawing on insights from a recent industry session, I highlight the importance of tailored strategies, collaboration, and investment in diverse digital skills to navigate the complexities of AI integration.
In this post, I share my perspective on the importance and joy of lifelong learning in the AI era. Drawing from my experiences in data science and physics, I discuss how continuous learning has been crucial for my professional growth and personal fulfillment.
