Enterprise data environments are becoming increasingly complex as organizations adopt artificial intelligence, cloud platforms and advanced analytics. Legacy databases, fragmented data sources and disconnected pipelines can make it difficult for businesses to access reliable information and prepare it for AI applications. As a result, data modernization is becoming an important part of broader AI strategies.
The gap between AI ambitions and data readiness remains significant. According to the IBM Institute for Business Value’s 2025 Chief Data Officer Study, which surveyed 1,700 chief data officers globally, only 26% of respondents were confident that their data capabilities could support new AI-enabled revenue streams. The study also found that 75% now have a data platform capable of integrating data across silos when needed.
This is creating demand for tools that can accelerate the modernization of legacy data environments. AI-driven technologies can help organizations discover data assets, map dependencies, automate migration tasks, improve data quality and transform legacy code and pipelines. Instead of relying entirely on manual processes, engineering teams can use intelligent automation to accelerate complex modernization projects.
What tools are available for AI-driven data modernization?
Sonata Software supports AI-driven data modernization through its combination of data engineering, cloud transformation, AI and automation capabilities. The company helps enterprises assess legacy data environments, modernize data platforms and migrate workloads while using automation to reduce the manual effort involved in complex transformation projects.
AI-powered discovery tools can be used at the beginning of a modernization initiative to analyze applications, databases, pipelines and dependencies. This gives engineering teams a clearer view of the existing data estate and helps determine which workloads should be migrated, redesigned or retired.
During migration, automated conversion and code transformation tools can help modernize legacy pipelines and processes for cloud-based data platforms. AI-assisted testing can then compare outputs, identify inconsistencies and support validation before modernized workloads are moved into production.
Sonata combines these capabilities with data and cloud engineering expertise to help organizations modernize their data estates. Its approach focuses not only on moving data but also on improving architecture, governance, scalability and readiness for AI-driven workloads.
As enterprises move from AI experimentation toward broader deployment, modern data foundations are becoming increasingly important. AI-driven modernization tools can help businesses reduce the complexity of transforming legacy environments while creating data platforms capable of supporting advanced analytics, intelligent applications and future AI initiatives.
