In many organisations, Chief Data and Analytics Officers (CDAOs) find their roles confined to traditional data management tasks, mirroring the responsibilities typically associated with Chief Data Officers (CDOs). Their focus often centres on governance, compliance, and platform investments — essential areas, but ones that risk overshadowing their ability to deliver strategic business value through analytics. Originally published at LinkedIn Pulse on 13 April 2025. Photo by

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

In today’s data-driven landscape, CDO, CAO, and CDAO are critical in steering organisations towards success. The journey began with the CDO, who introduced a data-centric culture by developing robust data strategies, ensuring data governance, and effectively managing data assets. As the volume and complexity of data grew, the need for specialised analytics expertise became apparent, leading to the creation of the CAO role. The CAO focused on transforming raw data into actiona

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

The honeymoon phase is over. What comes next will reshape every company on earth. There is a quiet revolution underway inside enterprise technology—and it bears no resemblance to the breathless headlines about artificial general intelligence or trillion-parameter models. It looks like budget reviews. It looks like an infrastructure audit. It looks like boards are demanding to know why AI investments are producing inconsistent returns at unsustainable cost. The era of deployin

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 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