eccenca Corporate Memory mentioned in Gartner® research report on future of data engineering
New Gartner® research report highlights ontologies as a foundation for agentic AI orchestration based on trust and reliability.
Leipzig, July 30th, 2026 - eccenca Corporate Memory has been mentioned in a Gartner® research report as a representative knowledge graph solution for establishing and managing enterprise knowledge graphs that provide a semantic context layer for enterprise AI applications and agents.
In "Data Engineering 2.0 Enables AI With Agentic, Semantic and RAG Capabilities"1, the Gartner® analysts outline the responsibilities and next steps of data engineers in the wake of AI. They highlight the need for automating repetitive manual data tasks, utilizing the vast amount of unstructured enterprise data as well as building a context layer that acts as the cognitive knowledge interface between the distributed data sources and numerous AI agents within a company. As enterprises embrace AI technologies to reduce manual effort and costs, the role of data engineers will evolve. They will become responsible for orchestrating the evolving agentic AI ecosystem, and ensuring that the results are accurate, relevant and performant.
However, as a McKinsey survey highlighted in 2025, the majority of companies implementing AI remain in the experimentation or pilot phase. Only 10 percent were scaling AI agents in any individual function2. Cross-functional AI use was even less established.
We at eccenca learned that AI is only worthwhile and scalable for enterprises if it can be trusted. It must be transparent, and its results must be reliable and reproducible. Ontologies and enterprise knowledge graphs are fundamental to providing both context and consistency to AI applications and to reducing hallucinations. With data sovereignty, use-case-sensitive business context and the prevention of vendor lock-in becoming essential for companies, we recognize and support the fact that ontologies and logical frameworks should be based on open semantic standards like RDF, OWL and SHACL rather than proprietary standards. Many solutions still enforce the latter, leading to knowledge silos and exploding costs when cross-functional AI orchestration is implemented. Open semantic standards provide companies the flexibility for exploring composite semantic approaches as well as for expanding, scaling and reusing their knowledge graphs according to their business needs across their entire digital ecosystem - irrespective of their data sources and processing systems.
Furthermore, as enterprise AI evolves from generative AI applications toward orchestrated agentic AI systems, the knowledge graph-based context layer for these agents is easier to build and govern when it is composed from domain-specific ontologies instead of one comprehensive semantic layer. In a way, agentic AI needs agentic ontologies. And companies need to be able to orchestrate both. The newly integrated solutions and ontology marketplace in the enterprise knowledge graph platform eccenca Corporate Memory follows this modular approach. It allows for the fast implementation and ROI of use-case-specific AI projects as well as the reuse for the subsequent AI scaling and orchestration in companies.
Explore eccenca Corporate Memory at https://eccenca.com/products/enterprise-knowledge-graph-platform-corporate-memory
1 https://www.gartner.com/en/documents/7725257
2 https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai
