# Java Modernisation Enables Enterprise AI Readiness > Source: > Published: 2026-06-19 16:09:35.087843+00:00 # 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)