How RAG and Graph RAG Take Generative AI to the Next Level

Don't Panic! It's Just Data

Generative AI has captured global attention, powering everything from chatbots to intelligent assistants. Yet in the enterprise, its promise often hits a dead end. According to Gartner, 80 per cent of enterprise data remains unused or “dark,” because conventional AI struggles to interpret complex, domain-specific information.

In this episode of the Don't Panic It's Just Data podcast, EM360Tech host Trisha Pillay speaks with Andreas Blumauer, Senior Vice President at Graphwise, about how retrieval-augmented generation (RAG) and its advanced application, Graph RAG, are levelling up enterprise AI. Together, they explore the limitations of traditional AI, the critical role of knowledge graphs in improving data accuracy, and what it takes for organisations to successfully adopt these technologies.

Why Graph RAG Matters

While RAG enhances Generative AI by enabling it to retrieve relevant data from large knowledge bases, Graph RAG takes it further. By integrating knowledge graphs, Graph RAG preserves the relationships, sequences, and meaning inherent in enterprise data. This ensures AI outputs are not just collections of facts, but structured insights that reflect the logic of an organisation’s knowledge.

These advantages include:

• Higher accuracy: Retrieval precision can increase from 80% to 95%, reducing errors in AI outputs.
• Trustworthy results: Outputs are explainable and traceable, providing transparency that enterprises require.
• Scalable integration: Connects data across silos and departments, making AI adoption enterprise-ready.

“Graph RAG respects the structure of enterprise data instead of flattening it. That’s what makes it trustworthy,” explains Blumauer.

Generative AI opened the door to possibilities. RAG made it actionable. Graph RAG takes it to the next level. By transforming dark, siloed data into structured, actionable knowledge, Graph RAG helps organisations achieve the accuracy, trust, and scalability essential for navigating the next frontier of enterprise intelligence.

Takeaways

• 80 per cent of enterprise data remains unused or dark.
• Traditional AI struggles to interpret complex enterprise data.
• RAG retrieves information from within the enterprise data landscape.
• Graph RAG improves the accuracy of AI outputs.
• Knowledge graphs link data points across different silos.
• Building a knowledge graph is a strategic investment.
• Incremental growth is possible with knowledge graphs.
• Graph RAG can increase accuracy from 80 per cent to 95 per cent.
• Data quality and governance are essential for AI success.
• The future of enterprise AI relies on effective knowledge management.

Chapters

00:00 Introduction to RAG and Graph RAG

03:04 Understanding the Importance of Knowledge Graphs

05:46 Adopting RAG: Organisational Readiness and Strategic Investment

08:51 Real-World Applications and Benefits of Graph RAG

11:56 The Evolution of Knowledge Graphs in AI

14:46 Future of GraphRAG and Enterprise AI

17:36 Rapid Fire Questions and Closing Thoughts

About Graphwise

Graphwise is a leading enterprise AI company specialising in knowledge graph technologies. By combining retrieval-augmented generation (RAG) with advanced graph-based approaches, Graphwise helps organisations turn siloed, complex data into accurate, actionable insights, enabling smarter decisions, scalable integration, and trustworthy AI outcomes.