The Hidden Cost of Accumulated Logic in Enterprise Processing Chains In organisations, especially in the financial sector, the integrity of reported numbers is rarely the core issue. The real challenge is understanding how those numbers came to be. Over decades, financial institutions and other complex enterprises have constructed multi-layered processing chains in which data moves from source systems to data management layers, onward to ledgers, and eventually into managemen

Financial services organisations are transforming faster than at any point in recent memory. Customer expectations are shifting. Digital capabilities are advancing. Regulatory pressures keep rising. Boards want efficiency and AI today, not tomorrow. Leaders design ambitious future states because they know the stakes are high. Yet the real struggle is not ambition. It is adoption. Across the sector, the same pattern appears. Transformations are well funded, strategically sound

Over the past few days, I’ve spent time reviewing Australia’s newly released  Guidance for AI Adoption: Implementation Practices . What struck me almost immediately is how strongly this framework echoes the long-standing principles that have guided compliance, conduct risk, and operational governance programs around the world. Whether we look to the regulatory traditions of Canada, the United States, the UK, Europe, or Asia-Pacific, the foundations are remarkably consistent:

Over the past year, expectations around climate and sustainability risk have continued to evolve. In Canada, OSFI’s B-15 guideline was introduced as a milestone in how banks and insurers are expected to manage and disclose climate-related risks. It signaled shift, from reporting climate as a standalone issue to embedding it into governance, risk, and business strategy. But in recent months, there’s been a recalibration. Institutions are navigating a moving target: regulatory