Enterprise AI ambitions are hitting a wall: data governance. A new Qlik Technologies study reveals a massive gap between AI investment and deployment. While 97% of companies allocate funds to agentic AI, only 18% have achieved full deployment. Leading blockers include data quality, integration, and governance.
Sam Pierson, CTO of Qlik, emphasized that data remains the primary constraint. "The biggest constraint that these organizations have is the data," Pierson stated. He noted the challenge of extracting data from hundreds or thousands of legacy systems to integrate into AI architectures.
To bridge this gap, a deliberate data governance foundation is essential. This involves embedding governance directly into the architecture, not just policy documents. As AI initiatives move forward, data security becomes paramount, with AI inheriting its security model from the core platform.
Qlik's semantic layer provides crucial metadata context and upstream impact analysis. Building on open, standards-based approaches like Apache Iceberg and the Open Semantic Interchange allows for modularity and easier future updates, ultimately enhancing AI solutions. The path to an AI-first workday relies on a trusted, governed data layer.