Generative AI (GenAI), especially LLMs, has transitioned from a buzzword to an essential business asset. Leading organisations have moved beyond the proof-of-concept (POC) phase and are now actively deploying GenAI solutions. Many non-technology companies have realised that developing proprietary LLMs is unnecessary for solving internal challenges . Instead, they acquire LLMs from vendors or the open-source marketplace. Originally published at LinkedIn Pulse on 23 March 2025

The article highlights integrating software engineering best practices, like design patterns, into AI and ML workflows. Engineers often focus on achieving high accuracy and performance in model development, sometimes overlooking essential practices that ensure code is modular, maintainable, and scalable. Engineers can create robust, scalable, and maintainable ML systems by incorporating design patterns like pipelines, factories, adapters, decorators, strategies, iterators, fa

As 2025 draws to a close, I find myself at the epicentre of a profound strategic misalignment in the technology world. On one hand, Artificial Intelligence (AI) has delivered at a scale few could have predicted even three years ago; it is now the undisputed operational core of global commerce, driving spectacular technological breakthroughs in fields from personalised medicine to autonomous manufacturing. Yet a persistent, unsettling anxiety grips the financial markets, the c

The introduction of modern Artificial Intelligence (AI), especially large language models (LLMs) and predictive models, is undeniably a defining technological moment for enterprises. These technologies offer exponential capabilities that feel like organisational superpowers , exponentially enhancing human productivity. Yet, this extraordinary potential is inextricably linked to an exponential amplification of systemic risk , creating a profound operational dilemma for

Artificial Intelligence (AI) is no longer a future consideration. It is a present imperative reshaping how businesses operate. AI enhances decision-making by delivering speed, precision, and insight at a scale that human intuition cannot match. By analysing vast datasets and identifying patterns, AI generates actionable intelligence that drives real-time value. The economic potential is massive, with trillions in possible value creation and significant productivity gains. Com

Australian businesses are trapped in a self-perpetuating cycle of mediocrity. While we claim to be innovative and proactive, many of our largest and most influential organisations are led by executives fundamentally ill-equipped to steer a modern technology and data-driven enterprise. The culprit is the pervasive reliance on the general manager archetype, a leader whose broad, non-technical background, while perfect for traditional business operations, becomes a liability in

The global executive suite has invested an estimated $30 billion to $40 billion in Generative AI (GenAI) initiatives. Yet, a staggering majority of these investments are failing to yield a measurable financial impact. The widespread failure to convert this capital expenditure into shareholder value signals a profound strategic misstep. The core issue? Treating AI as a standalone technological novelty, a shiny new chatbot , rather than a fundamental amplifier of exis

The recent reversal of the Commonwealth Bank of Australia's (CBA) AI-driven job cuts serves as a powerful cautionary tale for every business leader navigating the era of artificial intelligence (AI). CBA's initial decision to slash 45 customer service jobs and replace them with an AI-powered voice bot was a move rooted in outdated, Industrial-era management philosophies. This approach, focused on headcount reduction and operational cost-cutting, proved to be a strategic fai

As CDAO Melbourne marks its 10th anniversary with the 2024 conference, it’s an opportune moment to reflect on a decade of transformative evolution in Melbourne’s data and analytics landscape. As a regular participant, I have witnessed firsthand the remarkable progress. This article analyses my observations over the years, aiming to assess the evolution, identify the challenges for large-scale adoption, and develop a practical strategy to address the identified issues. A refle

Imagine a world where information and opportunities are not just a click away but are rather confined to certain demographics and regions. Sadly, such a world existed well before the late 20th century when the term 'digital divide' was coined. It served as a stark reminder of the gaping chasm that separated those who had the privilege of accessing modern information and communications technology from those who were left behind, either with no access or restricted access. Orig

With the advent of the data science practises, especially the likes of Artificial Intelligence (AI) or machine learning and their adaptation in sensitive areas, has drawn massive attention to this technology in the recent years. Whether it is cancer diagnosing system, Amazon’s AI recruiting tool or the US court’s profiling system Correctional Offender Management Profiling for Alternative Sanctions (COMPAS), we can always disagree to the decision made by the AI systems. In fac

Artificial Intelligence (AI), or more precisely Machine Learning (ML), has become an industry trend in the past 10 years. From a buzzword to a new way of automation and decision-making, ML has become mainstream. I have had conversations with multiple organisations keen to use ML, even without a real-world use case. Graduate schools are offering lightweight courses to the masses, consulting companies are providing services on adopting ML, and technology companies are building

Large Language Models (LLMs) represent a transformative technology in the realm of generative artificial intelligence (AI). These machine learning models are designed to understand and generate text that mirrors human language, learning from vast amounts of text through rigorous self- and semi-supervised training. LLMs have a broad range of applications, from generating text and automating workflows to sparking creative ideas and even writing software code. Some of the most p

Artificial intelligence (AI) has not created a new category of ownerless decisions in your business. Every outcome generated by an algorithm remains an act of the company, and therefore falls squarely within the legal and moral accountability of the board and executive. AI does not dilute board accountability In corporate law, the company is the decision‑maker, and the board is its collective mind. When a model approves a loan, denies a claim, reallocates staff, or routes co

In 2016, AI pioneer Geoffrey Hinton famously predicted we should stop training radiologists , claiming deep learning would soon outperform human doctors. Similarly, since as early as 2014, Elon Musk has been continuously promising that fully autonomous, sleep-at-the-wheel self-driving cars would be ready next year . Both instances highlight how brilliant minds frequently compress the timeline of complex AI deployment. Now, a bold new prediction has emerged from Microsoft, t

Every business wants to leverage big data and artificial intelligence (AI) initiatives. Lately, many businesses have tried to dive into big data and AI, but only a few have truly reaped its benefits. Though they had the right intentions, the failure occurred far too often. Here, we categorise common failures and provide guidelines for solving or avoiding them. For context, we present each failure type as a scenario and place these scenarios under three themes: Organisational