Java Modernisation Enables Enterprise AI Readiness

i-programmer's Erik Costlow writes that technical debt and expiring LTS versions make Java modernisation a pressing requirement for enterprises integrating AI into business-critical systems. The article cites Azul's 2026 State of Java Survey & Report -- based on more than 2,000 Java professionals across five continents, published February 2026 -- showing 62% of companies globally and 74% in the UK are developing AI features in Java, up from 50% last year, while 45% of respondents use Python, per the same survey. The Azul report also found that 31% of respondents report more than half of their Java applications now contain AI functionality, per the Azul newsroom. Java is widely embedded in long-running systems across banking, logistics, and ERP, making its readiness a practical constraint on AI rollout.

The i-programmer reporting frames a division of labour commonly observed in the field: Python is used primarily for model training, while Java serves as the runtime for model usage and integration into production systems. Companies running comparable stacks typically rely on a mature Java ecosystem -- including libraries such as JavaML and Deep Java Library (DJL) -- for inference, connectivity, and operational tooling. Modernisation work often focuses on JDK/LTS upgrades, dependency cleanup, containerisation, and improved observability to reduce deployment friction.

The i-programmer article also highlights security and technical-debt dimensions: AI agents used for code analysis consume source code into context windows, so unpruned or legacy-heavy codebases degrade output quality. Transitive dependencies that are never called increase the attack surface. Separately, the Azul survey reports 92% of respondents are concerned about Oracle Java pricing, and 81% have migrated, are migrating, or plan to migrate at least part of their Java estate to a non-Oracle OpenJDK distribution, per the Azul newsroom.

Observers should track follow-up surveys from Azul and others for trend confirmation, vendor support timelines for expiring LTS releases, and case studies showing whether modernisation efforts measurably shorten time-to-production for inference workloads. For practitioners, publicly reported modernisation patterns and tooling choices will be useful signals for selecting integration architectures and runtimes.