arXiv:2607.02609v1 Announce Type: cross Abstract: For decades, data engineering has developed mature architectural principles for integrating, governing, validating, cataloging, and serving organizational data. The rise of large language models does not eliminate these concerns; it exposes a broader version of them. Organizational knowledge is becoming executable infrastructure: systems increasingly retrieve it, assemble it, reason over it, and act on it. This paper argues that enterprise artificial intelligence (AI) systems suggest a transition toward an architectural discipline for represent
The rapid advancement and integration of large language models are pushing organizations to formalize how they manage and leverage 'knowledge' as an operational asset, moving beyond traditional data engineering.
This paper highlights a fundamental architectural shift required for enterprise AI systems to operate effectively, treating knowledge as an executable infrastructure rather than just data.
The focus expands from data engineering principles to 'knowledge engineering' as a distinct and crucial discipline for building robust and reliable AI-driven organizations.
- · Enterprise AI providers
- · Knowledge management platforms
- · Organizations with strong data governance
- · AI architects
- · Companies with poor data quality
- · Legacy IT infrastructure
- · Organizations resistant to AI integration
Increased investment in knowledge engineering tools and methodologies across enterprises.
Emergence of new roles and professional disciplines focused solely on organizational knowledge architecture for AI.
Enhanced operational efficiency and competitive advantage for organizations that successfully implement knowledge-centric AI systems, leading to market consolidation.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.AI