Panel – AI Agents: The Changing Human & Artificial Intelligence Relationship
The emergence of Agentic AI is driving the next phase of generative AI innovation, significantly broadening the technology’s potential applications beyond what we have witnessed so far.
As AI agents rapidly transform the human-software relationship among our workforce, this panel will explore the capabilities of AI agents beyond what has been achieved so far, examine recent advancements and the vast opportunities that lie ahead.
Key Discussion Points:
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The Architecture & Components of an Agentic AI System: From authentication & security, frameworks & RAG to multi-agent orchestration, we will explore the building blocks of Agentic AI infrastructure and the paths to a multi-agent AI system.
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Use Cases, Challenges & Impacts: Innovative companies & forward-thinking governments are already implementing Agentic AI across different use cases, learn from them and prepare your workforce to embrace AI agents with our expert speakers.
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The AI Agent Economy: Gain actionable insights into the massive diversity of Agentic AI companies in the current market, for both enterprise and consumer applications.
Panel – AI-Ready Data: Building the Foundation for Effective AI Deployment
Facing increased competition & the imperative to improve operational efficiency and cut costs, many enterprises are keen on embracing new horizons promised by “traditional” AI & generative AI. However, they must first tackle their data foundations to unlock an AI transformation.
This session will delve into the drivers for building an AI-ready data infrastructure, pitfalls to avoid and strategies for success.
Key Discussion Points:
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Choosing Clear Goals, KPI & the Right Tech Stack: How can organisation set well-defined outcomes & implement the right platforms for success?
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Avoiding Data Silos & Reducing the Monitoring Workload: What are the best practices to ensure system interoperability & minimise the need for human intervention?
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Optimising Workflows, Standards & Practices: What is the right approaches for companies to build a solid foundation to reduce data risks and enable its business for scalable AI success?