Introducing AI in mature Java systems – a layered approach for scaling engineering practises

A layered way to think about AI

In mature Java systems, AI adoption is not a clear-cut process. The introduction scale depends on the team’s needs and readiness – this works best when implemented in layers, because not every area carries the same risk or cost of change.

The three levels below explain how deeply AI interacts with existing engineering practices and how much responsibility it takes on within the system. Each step increases the impact but comes with bigger responsibility. You don’t need to climb up to the third level to benefit – for many cases, reaching the first level makes a significant difference alone. The point of the tiers below is to help you choose where to start and what fits your situation most, not to push you toward the deepest integration.

Level 1: AI as the engineer’s assistant

Best for teams that:

• work with a mature Java codebase and want faster delivery without touching production
• struggle with onboarding and understanding legacy modules
• lose time on repetitive scaffolding and test updates

On this level, AI exists entirely in the developer’s workflow – nothing enters or runs in the production directly. AI can be embedded in the IDE and take care of repetitive Java scaffolding and suggest test updates, so developers can focus on business logic.

In practice, this supports everyday coding and refactoring tasks common in Spring based systems, where even small domain changes require touching multiple layers of the application. A single change often spreads through controllers, data objects, mappings, validations, and tests. AI can generate this repetitive structure (for example, when extending a JPA entity) in a way that matches how the project is already organized, giving developers insights that would otherwise be discovered only after a wave of errors or failed tests

AI can also help with legacy code comprehension since older Java modules often lack up-to-date documentation. In such a case, AI can summarize class responsibilities, explain method flows, and generate JavaDoc based on the findings. This is especially valuable during revisiting rarely changed parts of the system or navigating large Spring monoliths, where proper onboarding matters more than writing new parts of code.

Because AI operates entirely within the developer workflow, teams can experiment freely without risking production stability, while gaining speed and confidence.

Level 2: AI inside the quality and review pipeline

Best for teams that:

• handle a high volume of pull requests and long review cycles
• experience regressions despite strong engineering practices
• want more predictable quality without slowing down delivery

The second level is where AI starts influencing what actually gets deployed, not by writing production code directly, but by assisting with quality related decisions. This is the stage where mature Java systems typically slow down the most due to the number of reviews, regression analysis, and test maintenance.

In long-lived Java systems, pull requests tend to grow larger over time and even a small feature can touch many interconnected areas. Reviews become longer and more tiring, shifting the focus to spotting risks instead of design improvements. AI can support review practices by highlighting changes in historically fragile packages, shared libraries, or modules with a high regression rate, acting like a second pair of eyes relieving the developer from the mundanity.

At this level, AI can also assist with contextual code review, detecting potential bugs or security issues resulting from the system evolution. Unlike static analysis, AI can correlate changes with previous incidents, recurring regressions, or known weak spots in the codebase.

With changing APIs or domain logic, tests often require manual updates that stall the development. In this area, AI can suggest updates to existing tests or generate missing cases based on what changed. This directly addresses test maintenance debt, which in mature Java systems often slows teams down more than feature development itself.

By reducing mechanical review and test costs, AI allows engineers to focus on design decisions, edge cases, and business impact.

Level 3: AI as a controlled part of the Java system

Best for teams that:

• want to start using AI in production to support real product or operational use cases
• are ready to validate AI through controlled experiments, observability, and clear fallback paths
• operate a mature Java platform where changes must be introduced carefully, not experimentally

On the third level, AI becomes a part of the product – this is also the phase where caution and discipline matter the most. Embedding AI directly into core business logic introduces risks that are hard to predict.

However, this doesn’t mean that AI should never run in production, it means that it should be isolated:

The safest and most scalable way to approach such deep integration is to treat AI as an external component or adapter. Instead of embedding it into the domain core, AI is accessed through clearly defined safe boundaries like a separate module, a service, or an adapter layer integrated via frameworks such as Spring AI or SDKs like AWS.

Such a setup allows teams to control failure modes – AI responses can be validated, monitored, and turned off without rolling back the entire system. Caution is key here, as you need to detect when AI behaves unexpectedly, how often it fails, and what impact it can possibly have on users or downstream processes.

The key challenge on this level is understanding what should happen when AI is wrong, and designing the system accordingly. This is often where teams benefit most from structured guidance rather than figuring it out through trial and error.

What are the real risks of using AI in mature Java systems?

Any discussion about implementing AI wouldn’t be complete without acknowledging the risks behind it. Rest assured, it’s not like AI is the hidden danger here, it’s the adoption without proper boundaries.

The most obvious risk in the process is the data exposure – since mature systems contain sensitive information, they cannot be a part of the flow with the external model (e.g business logic or customer data).

Another ones are hallucination and false confidence – the generated code may look correct and fit existing patterns but still introduce subtle bugs that can build up over time. In systems with complex domain logic like Java, these mistakes are rarely spottable at first glance and resurface under production load, resulting in high maintenance costs.

Example: While updating a Spring based REST endpoint backed by JPA entities, AI may suggest simplifying a validation rule or mapper. The change compiles and passes tests, but removes a domain constraint added years earlier to handle a specific edge case, leading to subtle data inconsistencies under real production traffic.

The risks discussed above are also the reason senior developers tend to be skeptical about AI. If you want to understand that skepticism and learn how to adopt tools like Claude Code without breaking trust or quality, the article below is a good next step.

You might be also interested in the article:

Why your devs say “AI is useless” – an expert take on adopting Claude Code in senior software teams

How to think about AI integration

The most important thing to remember is that AI adoption in Java systems is not about replacing what already works but about removing friction where it affects the day to day work the most. A tiered approach helps teams start with low risk improvements, learn how AI behaves in their context, and increase scope when the organization is ready for it.

It’s worth reiterating that deeper integration increases both impact and responsibility as mistakes become harder to isolate and reverse. When there is uncertainty about where to stop or how far to go, pushing forward blindly often creates more risk than value.

For CTOs and engineering leaders, the hardest part of AI adoption is rarely choosing tools but deciding where AI fits into existing engineering practices without increasing delivery or operational hazards.

That’s exactly what our Claude Code Experts focus on – helping Java teams adopt AI in a way that respects legacy systems, senior expertise, and real production constraints.

And if you want to go deeper into why AI often fails in Java teams, and what actually works – join our upcoming live session: Claude Code Experts: Why does AI fail in Java teams?

FAQ

1. What does “introducing AI in a layered way” mean for Java systems?

A layered approach means adopting AI gradually, based on risk and system impact. Instead of integrating AI directly into production logic from the start, teams begin with low risk use cases such as developer assistance, then move toward quality assurance and review automation, and only later consider controlled production use. This approach allows teams to gain value early while minimizing architectural and operational risk.

2. Can AI be used in mature Java systems without affecting production stability?

Yes. At the first level of adoption, AI operates entirely within the developer workflow, for example inside the IDE. In this setup, AI supports tasks like code scaffolding, refactoring, test updates, and codebase comprehension. Because no AI generated output is executed in production, this level improves delivery speed without introducing runtime risk.

3. How does AI improve code quality and reviews in long lived Java codebases?

AI can assist code reviews and testing by analyzing changes in context, rather than relying only on static rules. It can highlight modifications in historically fragile modules, suggest test updates when APIs or domain logic change, and surface potential regression risks based on past incidents. This helps reduce review fatigue and makes quality assurance more predictable in complex Java systems.

4. Is it safe to use AI generated code in enterprise Java environments?

AI generated code should be treated as a suggestion, not as an authoritative source. While AI is effective at following existing patterns, it may miss domain specific constraints or historical edge cases. Safe usage requires human review, clear boundaries, and an understanding of which parts of the system are suitable for AI assistance. This is especially important in systems with complex business logic and long operational history.

5. When does it make sense to use AI directly in production systems?

AI can be used in production when it is introduced as a controlled and isolated component rather than embedded into the domain core. Common approaches include using AI behind a service boundary, adapter, or external module with validation, monitoring, and fallback mechanisms. Teams should only proceed to this level when they can clearly define failure scenarios and limit the impact of incorrect AI behavior.