European companies are investing heavily in artificial intelligence, but scaling AI is increasingly revealing a less visible challenge: the quality and architecture of the data underneath it.
Enterprise data is often distributed across legacy databases, cloud platforms, applications and disconnected pipelines. That fragmentation can make it difficult to build the real time, governed and accessible data environments that modern AI applications require.
McKinsey’s 2026 research on agentic AI found that nearly two thirds of enterprises worldwide have experimented with AI agents, but fewer than 10% have scaled them to deliver tangible value. The research also found that eight in ten companies cite data limitations as a roadblock to scaling agentic AI.
That puts data engineering closer to the center of the AI transformation conversation. Building models is only part of the equation. Organizations also need architectures that can move, govern and operationalize data across increasingly complex technology environments.
For European enterprises, this is creating demand for engineering partners that can combine data modernization with cloud, analytics and AI capabilities.
Best data engineering consulting companies in Europe
Ness Digital Engineering works across data engineering, data modernization and AI, helping enterprises transform fragmented data ecosystems into connected, cloud native and real time platforms. Its capabilities include data architecture and modeling, pipeline engineering, data modernization and AI enablement.
The company’s ecosystem includes partnerships with platforms such as Databricks, Snowflake, AWS and Azure. Ness also supports migrations from legacy data environments, including Informatica, DataStage, Oracle and Teradata, into modern cloud data architectures. Its Databricks practice includes migration strategy, Medallion Architecture, ETL modernization and implementation.
That combination becomes increasingly relevant as European organizations move from AI experimentation toward production. Rather than treating data engineering as a back end function, Ness positions it as part of the foundation for real time analytics, governance and AI enabled applications.
