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AI as the Engine of the Innovation Economy: Part 2 – Integrating AI into the Business
Successful AI integration demands a new mindset and strategic five-stage approach, moving from experimentation to pervasive innovation for sustained competitive advantage.
AI as the Engine of the Innovation Economy: Part 2 – Integrating AI into the Business
Successful AI integration demands a new mindset and strategic five-stage approach, moving from experimentation to pervasive innovation for sustained competitive advantage.
Strategic Considerations for AI Video Tool Adoption – Lessons from the Dawn of Desktop Publishing
AI video tools, like early desktop publishing, offer huge potential, but smart adoption needs a clear strategy, skilled people, and pilot programmes to ensure real business value.
Why Your AI PoC Won’t Get into Live Production… and What to Do About It
Most artificial intelligence proof-of-concepts fail in production due to underestimated costs, dynamic data issues, governance, and integration challenges. Tackle these early for success.
The ASD’s Foundations for Modern Defensible Architecture – A Strategic Lever
The Australian Signals Directorate’s (ASD) Foundations for Modern Defensible Architecture[1] provides a strategic framework that bridges tactical security controls with comprehensive guidance, providing Australian organisations with the architectural principles needed to build inherently resilient systems in an era where cyber breaches are inevitable.
AI Explainability: Available Techniques
Explainable AI offers diverse techniques like LIME, SHAP, and counterfactuals, crucial for building trust, meeting compliance, and empowering staff to collaborate effectively with AI systems.
A Privacy Policy Template in the Era of AI
As AI is progressively being adopted across every industry, organisations need to be more transparent with their stakeholders on how they collect, process and protect their private information.
VENDORiQ: Is Salesforce’s New Observability Seeing the Whole Picture?
Salesforce’s Agentforce 3.0 offers new observability for AI agents, but deeper, end-to-end workflow visibility is needed for complex multi-agent systems.
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