Industry executives and experts share their predictions for 2026. Read them in this 18th annual VMblog.com series exclusive.
By Ole Olesen-Bagneux, PhD, VP, Chief Evangelist, Actian
AI is at the absolute center of strategic C-level conversations, which elevates the importance of data since it is central to the success of AI projects. However, AI changes what’s up and down in data. In 2026, I predict AI will reshape priorities in data engineering, management, and governance.
1) Traditional data engineering and business intelligence are expected.
Traditional data engineering, consisting of pipelines for BI and more advanced forms of analytics, has enabled companies to realize their data-driven goals. However, AI is changing this in a radical way.
Traditional business intelligence and its supporting data engineering are no longer strategic aspirations, but foundational expectations. While these functions will not be neglected or descoped, they will not receive their own strategic funding when companies make internal investments.
A key reason for this shift is the augmentation of traditional business intelligence tooling by AI. Activities that were previously handled by humans (storing, transforming, transporting, and visualizing data) are becoming partly automated processes. Will it work perfectly? No. Human oversight is still needed, but this change significantly increases speed and reduces the timeline.
Not all organizations are prepared for this new reality.
2) The rise of unstructured data demands new skillsets.
Unstructured data-such as text, images, video, and sound-is key to training and improving AI. This is causing a fundamental shift where organizations need to manage unstructured data, a domain neglected by traditional data management approaches.
AI is turning that hierarchy upside down, but managing unstructured data is very different from managing structured data in tables. This shift requires new disciplines around context and semantics that have traditionally been managed outside of data management teams.
The risk is clear: companies that can only manage structured data will fail to keep pace with AI’s demands and will miss out on its biggest opportunities. To address these trends, you will need to hire specialists, which brings us to our third prediction.
3) The rise of the ontologist: The shift from data layer to metadata layer.
Certain elements from traditional data management are on the rise. One of these is the data catalog, which manages the metadata of structured data and makes it discoverable. Data catalogs are becoming key for AI, provided they are built on the right technology: a knowledge graph. A knowledge graph, structured by ontologies, delivers essential context for AI. This context increases precision on AI use cases and is foundational for the success of agentic architectures.
Accordingly, a niche role that previously existed will become the new strategic hire: the ontologist. Ontologists have been employed in organizations for a decade, but with the introduction of AI, the importance of their role is increasing. Ontologists typically have backgrounds in linguistics or information science and have moved into tech focusing on data. They create knowledge graphs to increase search power internally in companies. Their importance in 2026 stems from the fact that AI needs the knowledge graphs created by ontologists.
Conclusion
These three predictions for 2026 are all interconnected and can be combined into one single trend. Changes in data requirements for AI pushes priorities away from structured data, which has been implicitly agreed as the most important type of data, toward unstructured data. As a result, practices around structured data receive less attention and focus, while practices for unstructured data will be increasingly prioritized. This points toward 2026 as the year where ontologists will become highly sought-after experts to help companies achieve their AI goals.
The shift to unstructured data and the rise of the ontologist officially marks the end of the traditional data hierarchy and confirms that future AI success lies in mastering semantic context.
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ABOUT THE AUTHOR
Ole Olesen-Bagneux is a globally recognized thought leader in metadata management and enterprise data architecture. As VP, Chief Evangelist at Actian, he drives industry awareness and adoption of modern approaches to data intelligence, drawing on his extensive expertise in data management, metadata, data catalogs, and decentralized architectures. An accomplished author, Ole has written The Enterprise Data Catalog (OReilly, 2023). He is currently working on Fundamentals of Metadata Management (OReilly, 2025), introducing a novel metadata architecture known as the Meta Grid. With a PhD in Library and Information Science from the University of Copenhagen, his unique perspective bridges traditional information science with modern data management.
Before joining Actian, Ole has served as Chief Evangelist at Zeenea, where he played a key role in shaping and communicating the companys technology vision. His industry experience includes leadership roles in enterprise architecture and data strategy at major pharmaceutical companies like Novo Nordisk.Ole is passionate about scalable metadata architectures, knowledge graphs, and enabling organizations to make data truly discoverable and usable.
