Oracle’s vision: AI-as-a-Service built into OCI and applications will drive productivity, minimise customisation, and secure business outcomes.
CustomGPT.ai’s new visual processing allows AI agents to incorporate images (diagrams, screenshots) as visual citations into responses, leveraging existing multimodal large language model (LLM) technology.
Google Cloud’s Gemini Enterprise elevates existing Gemini capabilities, moving beyond basic prompt-based AI to focus on agentic AI for measurable business value. This shift is Google’s strategy to monetise AI by enabling semi-autonomous services that directly leverage business data, overcoming initial market resistance to proving productivity gains from simple AI adoption
Model context protocol (MCP) is a vital standard enabling AI agents to automate tasks in core enterprise systems, but requires immediate, strong governance for safe adoption.
AI governance isn’t a brake on progress; it’s a strategic enabler of ethical, scaled innovation, requiring a shift to use-case focus and universal literacy.
Boards and executives must ask targeted questions to bridge their AI knowledge gap, ensuring responsible adoption and effective governance.
Creative AI is evolving beyond manual prompting to programmatic, high-throughput image generation with enhanced governance and analytics features.
AI-driven ‘vibe coding’ poses security risks, demanding a shift to DevSecOps where security is embedded throughout the automated software development lifecycle.
Despite the hype, GenAI is a multi-billion dollar bet on a technology vendors can’t own or control, requiring urgent regulation.
