Beyond Automation: Architecting the Autonomic Business with Knowledge Graphs

Most organisations today are automating inefficiency—deploying AI assistants and automation tools that execute tasks faster, but within the same broken processes. The result? Incremental gains of 10-20% that don't justify the investment or transform competitive position.

Meanwhile, a fundamental shift is underway that goes far beyond traditional automation.
Gartner has identified "autonomous business" as the next major wave following digital transformation—an era where self-managing, intelligent systems make decisions autonomously, adapt in real-time, and continuously learn from experience.

But most organisations miss achieving true autonomic capabilities as they just deploy more AI tools. It's about fundamentally rethinking how businesses operate, with knowledge graphs and ontologies providing the semantic foundation that makes autonomous operations possible.

In this session, we'll cut through the AI hype to explore what autonomic business actually means, why traditional approaches to AI implementation are fundamentally limited, and how knowledge graphs provide the context, explainability, and real-time adaptation that autonomic systems require.

- The critical distinction between AI assistants, agentic AI, and autonomic systems—and why it matters for business value - Why only 21% of firms can scale AI effectively, and what the successful 5% with true autonomic capabilities are doing differently - How knowledge graphs and ontologies work together to provide the semantic layer that enables context-aware, explainable autonomous operations - Real-world case studies showing 50-80% process-level transformation - The technical architecture: how we combine knowledge graphs with LLMs to reduce hallucinations by 60-80% while maintaining explainability - Why traditional databases and data lakes cannot provide what autonomic systems need—and what can - A practical roadmap for building autonomic capabilities