Java Modernisation Enables Enterprise AI Readiness

Technical debt and expiring LTS releases are forcing enterprises to modernize Java to integrate AI into business-critical systems, with Azul's survey showing 62% of companies worldwide and 74% in the UK developing AI features in Java, up from 50% last year. Python is used primarily for model training, while Java handles runtime model usage and integration into existing architectures.

Java Modernisation Enables Enterprise AI Readiness i-programmer's Erik Costlow reports that technical debt and expiring LTS releases are forcing organisations to modernise Java if they want to integrate AI into business-critical systems, according to the article on i-programmer. The piece cites Azul 's latest survey showing 62% of companies worldwide 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 article also notes a functional split: Python is used predominantly for model training, while Java is used for model usage and runtime integration into existing architectures. Editorial analysis: modernising Java reduces operational friction when adding inference and model-serving capabilities to legacy enterprise stacks. What happened i-programmer's Erik Costlow writes that technical debt and expiring LTS versions make Java modernisation a pressing requirement for enterprises that are integrating AI into business-critical systems. The article cites Azul 's latest survey showing 62% of companies worldwide and 74% in the UK are developing AI features in Java , up from 50% last year, and that 45% of respondents use Python , per the same survey. The piece reports that Java is widely embedded in long-running systems across banking, logistics and ERP, making its readiness a practical constraint on AI rollout. Editorial analysis - technical context The reporting frames a common division of labour observed in the field, with Python used primarily for model training and Java used for runtime model usage and integration into production systems. Companies running comparable stacks typically rely on a mature Java ecosystem for inference, connectivity, and operational tooling; modernisation work often focuses on dependency updates, JDK/LTS upgrades, containerisation, and improved observability to reduce deployment friction. Context and significance Industry-pattern observations: where Java remains the transactional runtime, failing to update the runtime and libraries raises the cost of adding AI-driven features because teams either build parallel Python-based runtime layers or accept brittle integration points. The i-programmer coverage and the Azul survey together underline that Java is not marginal for enterprise AI adoption; instead, it is commonly the platform through which AI features are delivered at scale. What to watch 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 modernization patterns and tooling choices will be useful signals for selecting integration architectures and runtimes. Scoring Rationale The story highlights a widely deployed technology stack Java as a practical bottleneck for enterprise AI deployment, making it notable for practitioners managing production inference and integration. It is not a frontier-model or platform shift, so it rates as a mid-to-high practical importance. Practice interview problems based on real data 1,500+ SQL & Python problems across 15 industry datasets — the exact type of data you work with. Try 250 free problems /problems