AI governance for risk and compliance teams is ultimately about operational trust. This blog explores how financial services organisations can move Gen AI systems safely into production through clear policy frameworks, AI risk ratings, observability, lifecycle ownership, audit trails, and APRA-aligned operational controls.
AI governance in financial services is ultimately about trust. This practical guide explores how to build Gen AI systems that can move safely into production through accuracy, explainability, operational controls, and compliance validation. From model optimisation to auditability, learn how regulated organisations can build AI systems that users, risk teams, and regulators can confidently rely on.
As organisations race to adopt generative and agentic AI, many are discovering a hard truth: AI ambition is only as strong as the data platforms that underpin it. Legacy SQL Server and Oracle environments were never designed to support the scale, integration, and velocity modern AI demands. This article explores why modern data foundations are now a strategic imperative for enabling AI at scale.
Compare AWS vector store options for RAG, including OpenSearch, S3 Vectors, Aurora pgvector, and more. This guide breaks down Bedrock Knowledge Bases integrations and custom pipeline approaches to help you choose the right solution based on latency, cost, and architecture.
In the age of AI, standing still is not an option. Continuous evolution and adaptability are essential for organisations to thrive, harness technology, and turn rapid change into a competitive advantage.
Agentic AI is redefining how enterprises measure success — not just in innovation, but in real financial returns. In this second step toward Agentic AI excellence, we introduce the Triad ROI Framework, a practical model for quantifying value across cost savings, revenue growth, and risk reduction. Learn how to assess impact over time, track the right metrics, and build the foundations for sustainable AI-driven performance.
Agentic AI is transforming the way we work, evolving from hidden tools in the shadows to trusted collaborators. In this blog, we explore the four stages of agentic AI, the key risks of data, hallucination, and context drift, and why human-in-the-loop governance is essential for building trust and maximising productivity.
AI coding assistants are changing how developers work. In this blog, Ian Ng shares how Amazon Q Developer became a powerful coding partner, helping automate routine tasks so developers can focus on architecture, problem-solving, and building better solutions faster.
Evaluating AI agents is the critical first step in achieving AI excellence. This blog explores modern frameworks and benchmarks that assess agents’ core capabilities, domain-specific performance, generalist reasoning, and evaluation methodologies, ensuring AI systems are reliable, adaptable, and ready to deliver real business value.
Advanced prompt engineering for AWS goes beyond basic prompts. By using techniques like chain-of-thought reasoning, few-shot learning, and iterative refinement, you can guide AI to deliver smarter, context-aware solutions that actually work within real-world project constraints. In this post, I share the methods I use on client projects to turn AI into a true consulting partner.
