In 2023, the field of generative artificial intelligence (AI) experienced remarkable progress, with significant advancements in natural language processing, computer vision, and audio synthesis. These developments have unlocked a plethora of new opportunities and applications, shaping the future trajectory of technology across a wide range of sectors. This article provides an overview of the ethical considerations and responsible strategies for the development, deployment, an

The buzz around AI often overshadows a critical reality: over 85% of employees anticipate that AI will impact their jobs within the next 2-3 years, leading to a mix of excitement and apprehension. There’s a notable disconnect between leadership’s perception of readiness and the reality on the ground, with C-suite leaders more likely to cite a lack of employee readiness as a barrier, even as employees are already adopting generative AI tools at a higher rate than expected. Thi

Over the past two decades, I’ve used analytics to support decision-making across nearly every part of enterprises. Yet, I’ve never once built a dashboard. They’ve consistently failed to deliver the insight and agility needed for real-world decisions. Dashboards demand a fixed set of questions and perfectly cleaned data up front, take months to build, and quickly become obsolete. In practice, they trap insight in rigid charts, leaving users to scroll through stale reports inst

Artificial Intelligence (AI) is advancing rapidly, and agentic AI is emerging as a transformative innovation. This approach enables systems to tackle complex, multi-step challenges autonomously. Generative AI (GenAI) has laid the groundwork by demonstrating the ability to understand and generate human-like text, code, images, audio, and video based on vast datasets. These foundational models are crucial for agentic AI, providing capabilities for understanding and generating n

In the wake of the advent of Large Language Models (LLMs), a multitude of initiatives have been undertaken to enhance user experiences via Natural Language Processing (NLP). A vast array of articles has been penned to encourage organisations to embrace generative artificial intelligence (AI). These pieces, however, have predominantly focused on the impressive ability of LLMs to understand user inquiries and generate responses, and often criticise the fact that they overlook f

In today’s highly competitive environment, the rush to harness the transformative power of Artificial Intelligence (AI) is apparent. However, beneath the surface of many organisations, there is a subtle, often hidden, threat: Shadow AI. This isn’t the stuff of sci-fi thrillers; it involves the widespread use of AI tools and initiatives by individual teams or departments, such as marketing, HR, or customer service, without central oversight, approval, or understanding from IT

In the rapidly evolving landscape of Generative AI (GenAI), the importance of version control for prompts cannot be overstated. Initially simple and rigid, prompts have evolved significantly with advancements in AI technology, particularly with the advent of large language models (LLMs) like GPT-3 and GPT-4, which enable more natural and context-aware interactions. Today, prompts are integral to the performance and reliability of AI systems, necessitating meticulous version c

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