• Data Governance in the Age of AI Agents: Lessons from Asia’s Regulatory Landscape

    Data Governance in the Age of AI Agents: Lessons from Asia’s Regulatory Landscape

    When Your AI Assistant Becomes a Compliance Liability The rise of agentic AI systems—autonomous workflows that can execute tasks, make decisions, and interact with other software—marks a watershed moment for data governance. As I’ve written before regarding professional integrity in data science, control over data is the cornerstone of trust. However, an AI agent that can…

    Continue reading

  • Building AI Factories: How Hong Kong Companies Can Create Scalable GenAI Infrastructure on a Budget

    Building AI Factories: How Hong Kong Companies Can Create Scalable GenAI Infrastructure on a Budget

    From Cost Centre to Competitive Engine: The “AI Factory” Mindset For Hong Kong’s dynamic businesses, the promise of Generative AI (GenAI) is tempered by a harsh reality: the perceived high cost and complexity of building a robust, scalable infrastructure. Many companies find themselves trapped in “Pilot Purgatory,” where impressive prototypes—like the autonomous agent systems we’ve…

    Continue reading

  • Escaping Pilot Purgatory 2.0: Strategies for Scaling Agentic Workflows in Production

    Escaping Pilot Purgatory 2.0: Strategies for Scaling Agentic Workflows in Production

    From Static Models to Dynamic Workflows: A New Frontier of Stalled Progress In my 2024 article, “Pilot Purgatory in Machine Learning,” I explored the frustrating gap where promising AI prototypes fail to deploy. Two years later, we face a more complex challenge: Pilot Purgatory 2.0. This isn’t about deploying a single model anymore—it’s about scaling dynamic…

    Continue reading

  • Agentic AI in 2026: From Hype to Real-World Deployment in Asian Enterprises

    Agentic AI in 2026: From Hype to Real-World Deployment in Asian Enterprises

    Introduction: Emerging from “Pilot Purgatory” Just two years ago, in my 2024 post on “Pilot Purgatory in Machine Learning,” I discussed the frustrating gap between promising prototypes and deployed production systems. Today, as we examine the state of Agentic AI in 2026, we witness a remarkable transformation. The landscape has evolved from isolated experiments with tools…

    Continue reading

  • Pilot Purgatory in Machine Learning: Why Most Models Excel in Prototyping but Fail to Deploy in Production

    Pilot Purgatory in Machine Learning: Why Most Models Excel in Prototyping but Fail to Deploy in Production

    The POC-to-Production Gap: A Persistent Challenge in Applied ML As data scientists, we’ve all encountered the frustrating reality of “pilot purgatory”—where promising proof-of-concept (POC) models deliver impressive offline performance but never make it to production. Industry reports highlight the scale of this issue: estimates suggest that 70-80% of ML projects fail to reach production deployment,…

    Continue reading

  • AI Agents & Autonomous Systems: How AI Agents Like AutoGPT Are Evolving

    AI Agents & Autonomous Systems: How AI Agents Like AutoGPT Are Evolving

    In the rapidly advancing world of artificial intelligence, a new frontier is emerging—AI agents and autonomous systems. These aren’t just models that respond to prompts; they are self-directed digital entities that think, plan, and act toward achieving goals with minimal human intervention.   At the center of this movement are AI agents like AutoGPT, BabyAGI,…

    Continue reading

  • Multimodal AI: Combining Text, Images, and Audio in Models (e.g., GPT-4V, LLaVA)

    Multimodal AI: Combining Text, Images, and Audio in Models (e.g., GPT-4V, LLaVA)

    Artificial Intelligence is evolving rapidly—from processing text in chatbots to understanding images and even interpreting audio. At the forefront of this evolution is Multimodal AI: models that can process and reason across multiple data types—text, images, audio, and video—within a unified framework. Multimodal AI is not just a technical leap; it’s reshaping how machines understand…

    Continue reading

  • The Misconceptions of LLM: Is a Large Model Really Omnipotent?

    The Misconceptions of LLM: Is a Large Model Really Omnipotent?

    In recent years, with the rapid development of large language models (LLMs), many corporate executives have been eagerly embracing this technology, believing it to be a panacea for all problems. Since early 2025, the rise of DeepSeek has further fueled market enthusiasm, especially in Hong Kong, where many enterprises have begun massive investments in LLM-related…

    Continue reading

  • Changes in Moral Standards and Current Workplace Challenges

    Changes in Moral Standards and Current Workplace Challenges

    In recent years, many business leaders and management teams have observed a sharp decline in work ethics, sense of responsibility, and dedication. Particularly after COVID-19, project execution efficiency in the Asian market has been significantly affected, mainly due to employees’ uncooperative attitudes and indifference toward project progress. Internally, companies are also facing challenges with younger…

    Continue reading

  • From Blocker to Builder: Transforming IT to Fuel Business Innovation

    From Blocker to Builder: Transforming IT to Fuel Business Innovation

    Introduction In today’s fast-paced business environment, technology is the backbone of growth, innovation, and efficiency. Yet, many organizations find their IT departments caught in a reactive mode, focused primarily on maintenance, security, and existing infrastructure, leaving little room for innovation or collaboration with other business units. As a result, IT can be viewed as a…

    Continue reading

0Shares