Observations
The Evolution of Graph Databases: From Complexity to Accessibility
Graph databases have traditionally been viewed as complex and resource-intensive, limiting their adoption to niche use cases. However, in the last quarter of 2024 and into 2025, all hyperscale Cloud vendors have significantly reduced the cost and complexity of deploying graph databases. These advancements have democratised access to graph databases, enabling organisations of all sizes to leverage their capabilities without requiring extensive technical expertise.
This shift is particularly impactful for AI applications, as graph databases excel at modelling relationships and contextual networks. By representing data as nodes (entities) and edges (relationships), they provide a framework for understanding complex interconnections, which is critical for advanced AI systems. The reduced barriers to entry are expected to accelerate the adoption of graph databases, fostering a wave of experimentation and innovation in AI applications.
Graph Databases in RAG
RAG is a well-established (but still rapidly changing) AI paradigm that combines the strengths of structured and unstructured databases and generative AI (GenAI) models. In RAG systems, graph databases can play a pivotal role by providing context-aware data retrieval. This enhances the accuracy and relevance of AI-generated outputs, addressing one of GenAI’s key limitations: the risk of producing plausible but incorrect information (hallucinations).
Graph databases enable RAG systems to perform multi-hop reasoning, where the AI can traverse multiple interconnected data points to derive nuanced insights. For instance, in healthcare, a graph database could link patient histories, medical research, and treatment outcomes to provide personalised recommendations via virtual agents or as recommendations to doctors. Similarly, in legal research, it could map case law, statutes, and precedents to deliver contextually relevant analyses.
The recent simplifications in graph database technology are expected to drive a surge in RAG applications across industries. Developers can now experiment with integrating graph databases into their AI workflows using ready-made software development kits and database Cloud services that they are already familiar with.
Agentic AI and the Role of Graph Databases
Agentic AI, which refers to AI systems capable of autonomous decision-making, is another area where graph databases are set to significantly impact. These systems require access to real-time, contextually rich data to function effectively. Graph databases provide the ideal infrastructure, enabling agentic AI to attach corporate information, such as organisational hierarchies, workflows, and operational data, to its decision-making processes.
For example, in supply chain management, a graph database could model the relationships between suppliers, logistics providers, and inventory levels, allowing an agentic AI system to optimise operations dynamically. Customer service could integrate customer profiles, interaction histories, and product data to deliver personalised, context-aware support.
The combination of graph databases with agentic AI is expected to drive the development of Industrial Agentic AI solutions – specialised systems tailored to specific business workflows. These solutions will enable organisations to automate complex processes, improve operational efficiency, and enhance decision-making capabilities.
AI Orchestration and the Convergence of Technologies
The future of AI lies in orchestration – the seamless integration of multiple AI technologies to deliver more powerful and efficient solutions. Graph databases are poised to play a central role in this convergence, serving as the backbone for integrating machine learning (ML), GenAI, and graph AI technologies.
For instance, in a retail setting, an AI orchestration system could combine customer sentiment analysis (ML), personalised marketing content generation (GenAI), and inventory optimisation (graph AI) to deliver a cohesive, data-driven strategy. Graph databases would provide the contextual framework needed to link these disparate components, ensuring the system operates as a unified whole.
This trend is expected to gain momentum between 2026 and 2027 as organisations increasingly recognise the value of graph databases in enabling advanced AI applications. However, realising this potential will require significant investments in data infrastructure, governance, and workforce training.
Challenges and Considerations
While the recent advancements in graph database technology are promising, they also present challenges. Organisations must ensure that their data is appropriate to the tasks at hand, well-categorised (as opposed well structured), and well-governed by robust frameworks to maximise the utility of graph databases. Additionally, the integration of graph databases into existing systems will require specialised knowledge, necessitating investments in training and development.
Ethical considerations are another critical factor. As graph databases enable more sophisticated AI applications, organisations must address data privacy, bias, and accountability issues. Transparent and explainable AI systems will build trust and ensure compliance with regulatory standards.
Next Steps
Invest in Data Infrastructure: develop robust data management and classification to support the integration of graph databases.
Experiment with RAG and Agentic AI: leverage graph databases’ reduced cost and complexity to explore innovative applications in RAG and agentic AI. Identify use cases that align with organisational goals and challenges.
Prepare for AI Orchestration: begin strategising how to integrate graph databases with other AI technologies to build comprehensive orchestration systems. This will require cross-functional collaboration and a clear understanding of business objectives.
Upskill the Workforce: invest in training programs to equip developers with the skills to work with graph databases and advanced AI technologies. This will be critical to realising the full potential of these tools.
