For years, Python has been lauded as the development language of choice for artificial intelligence (AI) applications, but we are now entering a new period where in partnership with Python, Java is playing a pivotal role.
With the launch of OpenAI’s Frontier and Anthropic Claude Cowork plugins, there is a serious shift in how enterprise CIOs are thinking about AI. It’s no longer just seen as an exciting breakthrough innovation, but one that has the potential to fundamentally reshape how business processes work. Yet, as the technology matures and IT leaders make significant commercial decisions about how to integrate AI into their IT environments, there are key strategy considerations.
Consideration #1: how will AI collaborate with your existing Java-based apps?
If you believe some of the hype, AI-enabled applications will become the central hub and source of intelligence for organisations with traditional business applications being relegated to stores of information and systems of record.
In reality, no enterprise CIO can afford to ignore the role that their core IT infrastructure plays today and will play in the future in collaboration with AI. These systems contain mission-critical information and run mission-critical workflows, such as high volume transactions in financial trading settings. It may well be that AI functionality is layered over the top of these systems, but for the AI to do its magic, unfettered access to critical business data is required.
Furthermore, AI is amplifying the need for performance, reliability, and operational efficiency, and these are domains where Java has excelled for decades. Java supports the massive, always-on systems that AI requires. And in the end, no matter what types of applications and interfaces are built with vibe coding and agentic processes, they will still depend on fundamental programming languages and runtimes that have proven to be enterprise-worthy.
Additionally, it is essential these tools can collaborate with existing Java applications and infrastructure. Hence why Java is going to play an increasingly important role as AI evolves from the experimentation phase into production. If, as some analyst firms suggest, these core applications become systems of reasoning, then the AI tools must be able to collaborate with, and extract information efficiently from, these existing IT environments.
Consideration #2: what’s the best way to integrate AI into Java environments?
In Azul’s 2026 State of Java Survey & Report, it was not surprising to me to find that 62% of organisations globally now use Java to code AI functionality; a figure that has jumped from 50% last year and rises to 74% among organisations in the UK. Around the world, 31% say that more than half of the Java applications they build include some form of AI functionality. Respondents also said they are using a variety of AI code generation tools with the top three being OpenAI (52%), Google Gemini Code Assist (43%) and Microsoft Visual Studio IntelliCode (37%). Anthropic’s Claude came in at the bottom (24%) but since this research was conducted, in just the last few months, Anthropic has made a concerted effort to woo enterprise users with Claude Opus 4.6 and its Claude Cowork plugins.
The bottom line is that enterprise users of Java are exploring the use of AI and integrating it in their development work. This makes absolute sense – in our State of Java Survey & Report, 64% of respondents said that more than 50% of applications or workloads run on a Java Virtual Machine (JVM). With such a dominant role in enterprise applications and infrastructure, it would be far too risky for enterprise CIOs to set aside their Java investments. Likewise, while Python has proven to be fairly soundproof for model building, there are few enterprises that are standing their business-critical applications that access these models in a Python environment. Thus, it should be no surprise that Java users are more likely to use Java to code AI functionality (62%) compared to Python (45%).
Consideration #3: is Java continuing to innovate to remain relevant in the AI era?
The main reason Java is not only relevant in the age of AI but also thriving is because of the vibrancy of the community and the commitment to innovation. There are several Java AI libraries developers can use with the top three listed in the State of Java study listed as JavaML, Deep Java Library (DJL) and OpenCL. As John Smart explained in his article, the benefits of these libraries address some of the key requirements for working with machine learning models. Amazon’s DJL is a framework agnostic interface enabling developers to maintain Java-based systems but still access machine learning models. This reduces the burden of having to learn new languages and allows developers to build machine learning models in environments they know if they so choose vs. needing to do all of this effort in Python for example. JavaML supports machine learning algorithms running natively in JVM environments, which might be crucial for organisations who need to contain data within their IT infrastructures. OpenCL helps Java applications to improve performance compared to Python-based toolchains by allowing them to access GPU acceleration.
There are also various innovative projects to improve integration and performance between Java infrastructure and AI tools. Project Panama is an OpenJDK community project to make it easier to access C/C++ machine learning libraries from Java, while the Vector API is enabling parallelism by using SIMD (Single Instruction, Multiple Data) hardware instructions to improve Java performance when writing code. Project Valhalla is addressing the significant demands on data processing from AI applications by introducing value types to Java that may reduce the burden on memory loads. Most recently, is the announcement of the OpenJDK Project Babylon. The introduction of Code Reflection combined with the Heterogeneous Accelerator Toolkit will enable regular Java code to be converted at runtime to use GPUs, thus greatly accelerating a range of workloads including AI.
It is clear Python has established itself as the language of choice for data scientists and machine learning engineers, but I am convinced that as enterprise CIOs move into production implementations of AI they will want the stability and maturity of Java underpinning their implementations as they always have. Whether it is the Just-in-Time (JIT) compiler in the JVM ensuring code is always converted to the best-suited native code for consistent performance, or the reassurance of having readable, maintainable code for complex scalable applications, or proven security and scale, Java will give developers greater confidence. At a time when AI is upending much of the thinking around enterprise application and infrastructure environments, having this stability underpinning future AI strategy decisions will be crucial.
About Simon Ritter
Simon has been in the IT business since 1984 and holds a Bachelor of Science degree in Physics from Brunel University in the U.K. Simon joined Sun Microsystems in 1996 and started working with Java technology from JDK 1.0; he has spent time working in both Java development and consultancy. Having moved to Oracle as part of the Sun acquisition, he managed the Java Evangelism team for the core Java platform. Now at Azul, he continues to help people understand Java as well as Azul’s JVM technologies and products. Simon has twice been awarded Java Rockstar status at JavaOne and is a Java Champion. He represents Azul on the Java SE Expert Group, OpenJDK Vulnerability Group and Adoptium Steering Committee. He is also the author of “OpenJDK Migration for dummies.”
About Azul
Azul is the trusted leader in enterprise Java for today’s AI and cloud-first world. Its open source-based Java platform empowers organizations to optimize the entire Java lifecycle to accelerate performance, strengthen security, reduce licensing and cloud costs, and boost developer productivity. Azul powers mission-critical systems for 36% of the Fortune 100, 50% of the Forbes Top Ten World’s Most Valuable Brands, and the world’s top 10 financial trading companies. Learn more at azul.com and follow @azulsystems.
