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.
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.
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.
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.
Adobe’s latest AI innovations, including GenStudio and LLM Optimizer, are enabling businesses to hyper-personalise customer experiences and boost visibility in AI-driven interactions.
Microsoft’s Azure expansion in Perth will cut latency and boost resilience for WA and ASEAN customers, enabling local hosting of critical workloads, especially for the public and resources sectors.
AI explainability goes beyond trust, fostering human oversight, collaboration, and control, especially for high-stakes decisions and system maintenance.
Despite the hype, true AI agents are elusive. They lack causal understanding, limiting effective autonomous action in varied environments.
ICT leaders: IT costs are rising but it’s an investment in organisational competitiveness. Focus on value realisation through adoption, integration, and process streamlining.
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