AI-Assisted Legacy Modernization: Automating Monolith-to-Microservice Decomposition

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INTERNATIONAL JOURNAL OF NETWORKS AND SECURITY (ISSN: 2693-387X)

Volume 05, Issue 01, 2025, pages 147-173

Published Date: - 07-06-2025

Doi: -

https://doi.org/10.55640/ijns-05-01-09

AI-Assisted Legacy Modernization: Automating Monolith-to-

Microservice Decomposition

Sandeep Reddy Gundla

Lead Software Engineer, MACYS Inc, GA, USA

ABSTRACT

Legacy systems are still critical business operations in many industries

–

but they are becoming roadblocks to

innovation, agility, and scalability. As enterprises increasingly pressure themselves to modernize their aging
infrastructures, strategic implementation of a transition from monolithic to microservices is gaining ground.
Transforming this type of complex monolith into microservices is not a trivial task. It presents technical and
organizational challenges, including bureaucratic service boundaries embedded in legacy codebases that tightly
couple the service's functionality. The topic of this article is how artificial intelligence (AI) can help automate the
decomposition of monolithic systems into decomposed, scalable microservices. By using machine learning, natural
language processing, and clustering algorithms, AI tools can analyze source code, runtime data, and interactions
between system components to determine intelligent service boundaries. A detailed methodology for AI-assisted
decomposition is presented, along with real-world tools such as IBM Mono2Micro and AWS Microservice
Extractor. A practical case study involving a global e-commerce company is included to illustrate applied outcomes.
Additionally, the article addresses key challenges such as data inconsistency, domain misalignment, and
organizational resistance. How it works outlines best practices to support successful implementation, including
incremental migration patterns, domain-driven design, and DevOps integration. The article concludes with
strategic recommendations and a forward-looking perspective on how AI will further change the modernization
process. When done

right, AI improves organizations’ ability to create agile, future

-prepared software ecosystems.

KEYWORDS

Legacy Modernization, Microservices Architecture, Artificial Intelligence, Monolith Decomposition, Software
Engineering Automation

1.

INTRODUCTION

Legacy systems have been the backbone of many critical business operations in the enterprise software world for
a long time now. Frequently constructed with monolithic architectures, these systems took decades to formulate
to fulfill a particular organizational requirement. The existence of a single, unified codebase with all functionalities:
the user interface, the business logic, and the data access are tightly coupled in a monolithic architecture. While
the design approach described above once accelerated initial development while simultaneously simplifying
deployment, it has become increasingly a problem as digital demands have matured. With cloud computing,
continuous delivery, and rapid innovation on tap, monolithic applications are starting to look their age. The
architectures are difficult to scale, hard to maintain, and near impossible to evolve rapidly without putting the entire
system at risk.

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Today's business has two fronts to fight. However, the valuable logic and data embedded in legacy systems must
be preserved. Existing industry practices insist on the organization's need to respond speedily to market changes,
shifting customer expectations, and new technologies. So many organizations are approaching.

This tension is legacy modernization today as a strategic imperative. Moving from monoliths to microservices is just
one of many modernization strategies and is especially compelling. Microservice splits an application into smaller,
independently created, deployed, and scaled independent units. This architecture's agility is greater, the time

–

to

–

the market is faster, and fault isolation improves. There is no easy transition for this, however. This means digging
into tightly knit code, figuring out the best way to carve out logical groups so data remains consistent, and reshaping
teams and corresponding workflow.

There is no such thing as manual decomposition of a monolithic system into microservices; it is a tiresome and
laborious work full of errors. Additionally, it necessitates a deep understanding of an existing codebase, along with
more information on business domains and operational mandates. Also, the human process is not scaleable
because other, more complex applications with millions of lines of code do not lend themselves well to the human
process. It's artificial intelligence (AI), and there is promise. Using machine learning and natural language processing
techniques combined with automated reasoning, AI technology can analyze source code, automatically detect
patterns, and suggest boundaries that are very close to the principles of microservice.

Modernization comes in the form of an AI-assisted automation of all manual tasks. For example, AI static analysis
tools can find code dependencies and bundles, while dynamic analysis plots runtime interactions and usage
patterns. Advanced models utilize historical data, log files, and user behavior to create a mental picture of system
components. It then allows us to propose or execute the ultimate service boundaries, which can shorten the
expected time and effort needed for transition by about an order of magnitude. However, iterative learning,
feedback loops, and accuracy and relevance over time allow AI systems to become more accurate and relevant in
business operations as time goes on, therefore becoming an incredible tool to augment intelligence without
replacing humans. However, this is not theoretical in its potential for AI. Some organizations and tech providers
have started exploring and implementing AI-assisted heritage modernization. Recently, various tools have been
launched by IBM, Amazon, and Microsoft to bust monoliths and adopt the migration to microservices using AI.
These tools consider application architecture, propose candidate microservices, and generate scaffolding code for
new services. While not mature, these technologies show how modernization can be done more confidently and
efficiently.

This article discovers how AI can help automatically decompose monolithic systems into microservices. First, it will
present an overview of monolithic and microservice architectures, followed by the growing need for modernization.
The article will then examine how AI could help with this process in terms of methods and tools. The transport
method of application of AI techniques in practice will be detailed in a separate methodology section. A case study
will illustrate real-world applications of the above methodology, the challenges faced, tools used, best practices,
and strategic recommendations are also discussed. Finally, the article concludes with an outlook on the future of
AI-driven software modernization. In so doing, it aims to create a bridge between traditional modernization
approaches and new opportunities afforded by AI, making it a complete, practical guide for organizations charting
this critical transition.

2. Background and Motivation

2.1 Historical Context of Enterprise Applications

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In the late 20th century, enterprises across industries developed centralized software systems to automate core
business operations (Delsing, 2017). These systems, which were usually monolithic, bundled all functionality into a
single executable or codebase. The languages used tended to be COBOL, Java, C++, and more, and they ran on
mainframes or large application servers. Their structure was simple to deploy and initially developed and stable,
with infrequent changes. As organizations grew and newer demands arose

—

online access, real-time data

processing, mobile integration

—

they became a problem. While these systems are reliable, they have not been

easily adapted to modern computing paradigms. The process took too long, eliminating project one function
without regression testing the entire application. Consequently, monolithic applications ended up as a barrier to
digital progress.

This diagram below shows how critical enterprise functions such as logistics, financial operations, production
planning, and human capital management are interconnected through master data, emphasizing the tightly coupled
nature of traditional monolithic systems.

Figure 1: Enterprise systems core

–

ERP.

2.2 Limitations of Monolithic Architecture.

There are several operational and developmental bottlenecks inherent in monolithic systems (Rüßmann et al.,
2015). Technically, one of the most significant issues is the absence of clear module boundaries, which leads to tight
coupling among components. In such architectures, different parts of the system are highly interdependent, making
it difficult to update or modify individual modules without impacting others. This tight interconnection creates a
fragile development environment where even minor changes can necessitate full application redeployment. As a
result, there is a heightened risk of system downtime and production errors, which hampers agility and
responsiveness. This condition not only reduces modular clarity but also complicates decision-making and increases
system-wide vulnerability during updates. (Goel & Bhramhabhatt, 2024).

Another problem is scale. These systems generally scale vertically by increasing the GPU(s), CPU(s), RAM, or disk
resources to meet larger loads rather than horizontally distributing the loads over multiple instances. This is both
an expensive and inflexible vertical approach. In addition, monolithic systems are also compromised on fault
tolerance. Because everything runs in a shared memory and runtime environment, a bug in one part of the
application (the billing module) could crash the whole system. The issue from a development standpoint is that

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large teams working fro

m the same codebase can produce strains by themselves (Šmite et al., 2017). Version control

can quickly become overwhelming, code review needs to happen, and testing coverage needs to be verified. On
top of that, it is difficult to onboard new developers because understanding a big, interdependent codebase
requires a steep learning curve. These limitations are compounded when these organizations try to align their
development processes with modern practices, like agile development and continuous integration/continuous

deployment (CI/CD). Iterative releases and fast feedback loops don’t come naturally to monoliths. Companies that

want to stay relevant need an architectural solution that readily supports rapid change and scale.

2.3 Drivers for migration to microservices

More and more decisions are not motivated solely by technical grounds; business needs are pushing as well
(McClelland & Burnham, 2017). Today, organizations are pressured to innovate, reduce costs, and respond quickly
to market changes. Customers demand feature updates, a digital experience that is as if it were on a silver platter,
and no downtime. Teams need to be able to build, test, and deploy services independently, which microservices
address by allowing this. For example, an e-commerce platform might have separate microservices for inventory,
payments, user authentication, and order tracking. None of these should be taken as being either inviolable or
unchangeable. Each of them can be updated or scaled independently of the others. This adds modularity to increase
development speed and lessen the chance of systemic failure. In addition, microservices are a very good fit for
cloud-native architectures and container technologies like Docker and Kubernetes, which are built to manage
loosely coupled distributed systems.

AWS, Azure, and Google Cloud, among other cloud service providers, provide abundant support for microservice
deployments (Gohil & Patel, 2024). Offering orchestration, monitoring, logging, and auto-scaling tools, they remove
the operational burden of working on an internal team. Therefore, companies can spend more time creating
business value and less time managing infrastructure. Also, microservices support organizational agility. They result
in better accountability and faster decisions, which deliver better services for customers. This autonomous structure
allows companies to scale their technology and their workforce with very little friction compared to monolithic
development models.

The performance and flexibility limitations of monolithic systems are becoming increasingly evident, particularly as
digital demands grow more dynamic. Traditional architectures often fail to support real-time data processing,
scaling, and system responsiveness. At the same time, market pressures demand rapid innovation while furthering
operational efficiency and cost control. These challenges offer a strong argument for redesigning the system as a
more adaptive model. Going from monoliths to microservices is a strategic move to enhance modularity, scalability,
and reaction speeds. The transition is complex but is often performed incrementally to reduce the risk and the
disruption. Organizations can decompose monolithic architectures into independent services to better address gaps
in performance and consistency and achieve business agility goals. This process is further augmented by AI-driven
tools that automate dependency analysis, suggest optimal service boundaries, and make deployment constant easy.
The performance of these advancements helps close the gap between performance and reliability for modern
applications (Dhanagari, 2024).

3. Understanding Monoliths vs. Microservices

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Table 1:

Key Differences Between Monolithic and Microservices Architectures

Feature

Monolithic Architecture Microservices Architecture

Codebase

Single, unified

Multiple, independently managed

Deployment

One unit

Independent service deployments

Scalability

Vertical

Horizontal, service-specific

Fault Isolation

Low

High

Development Teams

Centralized, large teams Decentralized, smaller cross-functional

Release Cycle

Infrequent, risky

Frequent, incremental

Technology Stack Flexibility Limited

High (language/framework per service)

Testing Complexity

High

Service-specific, easier in isolation

3.1 Defining Monolithic Applications

A monolithic application is an application whose functionalities live in the same logic unit in the same deployable
unit (Tapia et al., 2020). Under this structure, the business logic, database, and user interface layer are interfaced
together as a single unit. This model can help with the early stages of development

—

everything is in one code

base

—

but complicates matters significantly over time. Most enterprises have grown their monolithic applications

to millions of lines of code and thousands of interdependent modules. Such modules tend to share memory, global
state, and closely coupled logic, and therefore, the application is deceptively sensitive to even slight modifications.

A problem with monolithic systems is that anything as small as a minor change can require one to replete and
redeploy the entire system (Newman, 2019). This increases deployment time and the chances of bugs being
introduced. Testing becomes more difficult because the application must be validated to guarantee that innovative
additions or changes to one area will not conflict with the others. As a result, the release cycle is long, and updating
frequently is discouraged. Monolithic architectures can work well for small-scale applications or even startups but
become less sustainable as systems grow and evolve.

This diagram below shows the unified composition of a monolithic application, where the user interface, business
layer, and data interface are tightly integrated and deployed as one unit alongside the central database.

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Figure 2:

Monolith vs Microservices vs Modular Monoliths

3.2 Microservices are a set of key characteristics.

The architectural approach of microservices is fundamentally different. With the microservices model, instead of
placing all functionalities into one application, they are decoupled into loosely coupled services that function
independently. They employ lightweight protocols such as HTTP or messaging queues for communication between
services that handle different business functions, e.g., user management, payment processing, or notification
delivery, where each service is responsible for a specific business function. It allows each microservice to develop,
test, and scale independently. It is also one of the most important characteristics, following domain-driven design
capabilities (Zhong et al., 2024). A service can be bound (in the DDD sense) to a bounded context to own the logic
and data needed to perform a particular domain function. These reasons make for architectural clarity, leading to
higher maintainability and greater agility. It allows development teams to work on different services in parallel,
without intersecting, which means faster innovation and more reliable releases. Scalability is also a microservices
feature by design. The system's stability is achieved by allowing high-traffic services high-traffic services to be
independently scaled. For instance, an order management service within an e-commerce platform will have high
resource requirements during peak sales, while other services like content management may be relatively stable.
This flexibility gives us flexibility to reduce infrastructure costs or to improve performance under load.

3.3 Technical and Organizational Comparison.

Monoliths and microservices need to be compared on both technical and organizational factors. From a technical
point of view, monoliths mean a single über codebase that can make certain aspects of development easier, like
debugging on initial deployment. However, the systems suffer from bad modularity, scalability, and change
management. In opposition, microservices increase the complexity of managing distributed systems but provide
high modularity, fault isolation, and scalability. Monoliths historically cause the code to be distributed among large
centralized teams whose responsibilities are vast. As the team grows, coordination becomes harder, and a
bottleneck can be experienced when multiple developers work on the same codebase. Because of its smaller, cross-
functional teams can own a given service. Autonomy of this nature reduces communication overhead and increases
development velocity. Moreover, microservices work perfectly with Agile and DevOps, making building continuous
integration and deployment (CI/CD) pipelines easy. Microservices are great, but they come with their own set of

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problems. These include robust service discovery mechanisms, strong, secure API gateways, consistent logging and
monitoring, and reliable means of communication amongst the distributed components.

3.4 Real-World Decomposition Challenges

Breaking a monolithic system into microservices is no minor feat. The first thing to do is to discover service
boundaries in an intertwined code base. One often have to duplicate or even spread business logic across multiple
modules. There is no good way to isolate services from one another due to shared databases cleanly. In some cases,
such separation is impossible without rewriting substantial parts of the code and increases the risk of interrupting
key workflows. Data consistency is another major challenge faced in data management applications. Normally,
monolithic systems run over a single relational database using transactions and constraints to maintain data
integrity. In a microservice environment, data consistency becomes complicated and often relies on an eventual
consistency model since each service might have its own database. Reflecting on this requires a different way of
thinking about data ownership and synchronization. Lastly, backward compatibility and interoperability with
current systems must be considered. Most enterprises do not have the luxury of starting over and rewriting
everything. In reality, they'll need to support a hybrid model for a time, during which part of the monolith and new
microservices will reside and collaborate. Managing the transitional phase requires careful planning, robust API
contracts, and thoughtful orchestration strategies.

Any organization aiming at modernization must understand the fundamental differences between monolithic and
microservices architectures (Fritzsch, 2024). This knowledge is critical to navigating the structural and operational
shifts that modernization entails. Transitioning to microservices introduces significant technical and organizational
challenges

—

ranging from distributed data management to inter-service communication and deployment

orchestration. To overcome these hurdles, organizations need a thorough grasp of how such changes impact
infrastructure, workflows, and governance models. As demonstrated in large-scale systems, scalable solutions like
MongoDB are often leveraged to manage real-time data streams efficiently, supporting the modularity and speed
that microservices demand. These systems also highlight that architectural adaptability is a critical feature for
enabling high-volume data and responsiveness, further reinforcing the strategic value of microservices. The
following sections outline how artificial intelligence can assist in the complex transition toward modernization, from
automation to insights into the modernization process (Dhanagari, 2024)

4. Role of AI in Software Modernization

Table 2:

AI Techniques and Their Roles in Modernization

AI Technique

Function in Modernization

Example Tools/Applications

Machine Learning (ML)

Detect

usage

patterns

and

change

frequencies

IBM Mono2Micro, AWS Microservice
Extractor

Natural

Language

Processing (NLP)

Interpret

code

comments

and

documentation

Code2Vec, OpenAI Codex

Deep Learning (DL)

Analyze code structure and behavior at scale

Custom DL models, Microsoft research
projects

Clustering Algorithms

Group related code components into service
candidates

K-Means,

DBSCAN,

Hierarchical

Clustering

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AI Technique

Function in Modernization

Example Tools/Applications

Static Code Analysis

Detect

code

dependencies

without

execution

Sourcery, OpenRewrite

Dynamic Analysis

Map real-time interactions and system
behavior

Runtime log analyzers, tracing tools

4.1 Introduction to AI Techniques (ML, NLP, DL)

Artificial intelligence (AI) has seen strides recently, providing practical solutions to complex and highly data-
intensive problems in various domains (Xu et al., 2021). Legacy system modernization is a problem amenable to AI
techniques, particularly machine learning (ML), natural language processing (NLP), and deep learning (DL), all of
which have the potential to increase productivity in the software engineering domain at large. The techniques
presented here address an urgent need of organizations trying to understand, restructure, and improve large
software systems. Machine learning uses algorithms on historical or structured data so that systems learn patterns
or make predictions. ML models trained on code repositories, commit histories, and runtime data are used to infer
application segments that are frequently used together and commonly change within the same execution context.
These insights help identify cohesive functionality groups that can be structured as independent services.

This process relies on natural language processing of textual data

—

such as code comments, documentation,

naming conventions, and user stories. Several tools (including PyMC, jmmCNN, and distressed ML) have been
developed to analyze collapsed attributes, detect continuous event patterns, and examine complex scenarios that
require expert interpretation of code and version control logs. NLP models can be used to classify functional
descriptions of modules (or find human-readable explanations of legacy components using NLP models); unlike
many conventional machine learning techniques that process input, such as speech commands or waveform
images, deep learning (DL) works through layered neural networks to process more complex data structures.
Similarly, data load models can consume complex inputs, like source code hierarchies, dependency trees, and
system event logs, which is relevant to the issue of software modernization. However, computationally intensive,
such models can uncover hidden relationships in large codebases. Consequently, such tools are critical to
automating sophisticated tasks involving code similarity detection, functional classification, and domain
partitioning, which are highly labor-intensive tasks that require manual review. Automation strongly resonates with
contemporary DevSecOps principles, whereby intelligent tools add to CI / CD pipelines to improve accuracy and
speed delivery while protecting system security and integrity (Konneru, 2021).

As seen in the diagram below, areas within AI relevant to modernization touch on natural language processing,
generative AI, predictive analytics, robotics, and computer vision, which can inform or complement ML, NLP, and
DL techniques in practical implementations of these approaches.

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Figure 3:

Artificial Intelligence (AI)

4.2 AI-based Code Analysis and Service Discovery

Understanding the application marks the starting point of the monolith-to-microservices transition. This phase is
accelerated with AI through automated static and dynamic code analysis (Gu, 2020). In static analysis, the source
code is examined without the need for execution. By parsing class hierarchy, method call, and dependency graph,
AI-driven tools can detect whether a module has tightly coupled modules or common components being accessed.
Using these tools, it becomes possible to identify groups of functions that frequently interact with each other

—

making them strong candidates for microservices. In the context of dynamic analysis, the application is observed at
runtime.

By analyzing logs, usage patterns, and monitoring data as required by AI models, real-world interactions between
system components can be mapped. For example, if a set of functions is consistently triggered together during a
customer checkout process, those functions can be logically grouped into a single microservice. A practical solution
to this question is to apply clustering algorithms such as k-means and hierarchical clustering to chunk together
classes or functions that happen to belong together based on several dimensions

—

frequency of use, semantic

similarity, dependency strength, and runtime co-occurrence. Such clusters are used as a potential basis for services.
In addition to synthesizing candidate service boundaries through visualization of relationships and proposing
modular decompositions, AI can also help form a framework model. These suggestions lower manual effort while
providing a structured starting point for software architects. The automatic insights greatly decrease analysis time
and point out areas that might have slipped through the cracks without them.

4.3 AI-Driven Tooling Landscape: State-of-the-Art

AI is already starting to be used in software modernization, with several real-world tools and platforms already
employing it. A case to mention is IBM's Mono2Micro, which uses AI to scan Java-based monoliths and suggest
possible microservice candidates. The tool maps the application's structure by mixing static and dynamic analysis
and clustering. Next, developers can go through these groupings and turn them into sensible microservice
boundaries. Amazon Web Services (AWS) Microservice Extractor for .NET is a tool for identifying components in
.NET applications that can be separated into microservices. It analyzes source code and dependencies, provides
recommendations, and can even 'scaffold' a new service. This approach makes it easier

—

though not always easy

—

to extract reusable components from an application for deployment. In software engineering, Microsoft has also
looked at AI and how language models can be used to analyze legacy code and suggest targeted modernizations.
These models allow us to identify old patterns, make improvements, and smoothen the refactoring process.
Simultaneously, open-source communities have developed several other AI-based static analysis tools, such as

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Sourcery and Code2Vec. Its purpose is to automate code quality improvements, detect logical inefficiencies, and
suggest refactoring aligned with modern software design principles. By integrating such tools into event-driven
environments and CI/CD workflows, organizations can increase system resilience, reduce manual effort, and
maintain fault tolerance in complex, distributed systems

—

capabilities that are critical in large-scale modernization

efforts (Chavan, 2024). While not perfect replacements for human decision-makers, these tools provide
considerable support around speed, scale, and consistency (Jarrahi, 2018). They ease engineers ' cognitive burden
and ensure that modernization efforts are motivated by data-driven insights rather than intuition. Essentially, what
was once a theoretical concept has transformed into a suite of practical tools that can enable improvement in
virtually every stage of modernization. By taking us through the process involving legacy systems to architectural
changes, AI tools supplement many human experts and make large-scale transforms workable.

5. Methodology

Transiting to a microservice architecture from a monolithic system is yet another complex process. Though
methodical and usually complete, traditional manual methods are also time-consuming and seldom scale with the
size and complexity of enterprise-level applications. These challenges are mitigated with an AI-assisted approach of
precise, automated, adaptive modernization. Herein, a structured methodology is presented for applying artificial
intelligence to support the decomposition of monolithic systems into microservices. The methodology is developed
through six interlocking phases, achieving meaningful efficiencies and insights along the overall journey of
transformation.

As a foundational step to modernization, comprehensive data must be collected from the monolithic system. This
consists of its full source code, architectural diagrams, version control logs, application performance metrics, and
runtime data (user activity traces and system logs). The goal is to develop a unified dataset characterizing
application structural and behavioral characteristics. Static analysis of the source code is per-formed for the logical
structure and depen-dencies and from the runtime metrics and logs to understand the interaction of different
components in the production environments. However, suppose the documentation is incomplete and outdated.
In that case, there is still little that can be done apart from using natural language processing (NLP) tools to extract
semantic meaning from inline comments, variable names, and function identifiers. The reasoning behind this
enriched analysis is especially important in determining appropriate service boundaries for microservices in that
this provides the means to infer the roles of different modules. Accurate boundary definition ensures that services
are cohesive, loosely coupled, and aligned with real-world business domains

—

an essential requirement for

successful system decomposition and long-term maintainability (Chavan, 2022).

Once data is collected, AI models analyze static and dynamic dependency (Kibria et al., 2018). The static analysis

looks at the application code and doesn’t execute any of it to

reveal class hierarchies, method calls, import

dependencies, and library linkages. Constructing such a map from this analysis of code components gives insight
into how different components fit together systemically. In parallel, dynamic analysis monitors the application at
runtime to infer how the application is used. AI systems infer which components tend to be used jointly, when,
under what circumstances, and how data flows through the system. The combination of static to grow structural
insight and dynamic to grasp behavioral understanding is critical for realizing a fuller picture of interdependencies
and operational relationships to resolve the question of deciding which components are to remain coupled and
which can be uncoupled.

Machine learning algorithms are applied to these to group related functions and components whose dependencies
are well-defined. Source files, classes, or methods are grouped by clustering techniques (K-means, DBSCAN, or

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hierarchical clustering) according to (for example) interaction frequency, semantic similarity, and shared data
access. The first candidates for microservices are these clusters. Where arbitrary separation fails, the clustering is
rooted in empirical usage and business logic. For instance, order processing-related code functions may be located
together because they always work together and are in the same transactional sandboxes. They may also be spread
throughout the original codebase.

The clusters are presented in AI-generated form, dependency, graphs, heat maps. These become vehicles for
development teams to review and interpret the proposed service boundaries. At this stage, AI tools suggest clusters
that could be used as microservices by assessing cohesion within modules and coupling between them. They flag
those boundaries with high internal cohesion and low external dependencies as strong candidates. The tools help
the architects validate visually if these suggestions match their system knowledge or what they know regarding the
business domains of the organization. Human review at places where the outputs of the AI are ambiguous or at
odds with operational knowledge helps keep critical logic from being unintentionally broken up. Although the work
starts from an AI decomposition, the human in the loop must subsequently validate it. In partnership, domain
experts, enterprise architects, and development leads assess AI-generated service candidates to determine if they
are technically feasible and business-relevant. Depending on user roles and compliance requirements, and may
need to adjust interdepartmental workflows or operational constraints. This phase ensures the microservices serve
both the logical structure, organizational context, and domain expertise, translating into higher functionality and
maintainability.

Automated code transformation and scaffolding constitute the final phase of the AI-assisted modernization process.
Once service boundaries are validated, AI tools can generate foundational code for the proposed microservices.
The output of this is often service templates, RESTful APIs, data access layers, and integration points. In more
advanced situations, classes in the monolith are automatically chopped up into microservices, individually
deployable, each with its own data store and interface. Also, these tools provide capabilities to generate
deployment-ready artifacts like Dockerfiles, Kubernetes manifests, CI\CD configurations, to ease the journey to
production. Human developers are still needed for validation and fine-tuning. However, automation reduces the
amount of manual work. The development of some of these capabilities, particularly around modeling legacy code
structure and the generation of corresponding service interfaces, depends largely on principles that exist in natural
language inference

—

where the objective is to infer logical relationships from structured inputs

—

and show that AI

is an important fundamental tool in contemporary software engineering (Raju, 2017). These six phases are executed
as a holistic, scalable AI-assisted legacy modernization approach. Organizations that bring intelligent analysis,
collaborative validation, and automated transformation together can accelerate microservices move with minimal
risk, keeping technical and business objectives in balance.

6. Case Study / Real-World Example

It is no longer a theoretical model or experimental research but is becoming a practical enabler of enterprise
transformation. This has been done by many organizations that have successfully leveraged AI to transform legacy
systems to gradually move from rigid monolithic architectures to agile, scalable microservice architectures. These
are no longer speculative transitions but generate operational improvements and business value in various sectors.
A case study is presented based on a global retail company that modernized its core application infrastructure
across the stack using end-to-end AI-enabled modernization. The initiative used an approach that combines AI-
assisted code analysis, service decomposition, and deployment automation to turn a legacy system into a
microservices architecture. Predictive analytics and intelligent tooling played in cases to achieve faster deployment

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cycles, lower system downtime, and enhanced DevOps efficiency; this was a clear expression of the synergy
between AI and Business intelligence and scalability (Kumar, 2019).

Below is a diagram that describes the four key dimensions organizations must consider if they want to embark on
AI-assisted transformation: their operational workflows, legacy I.T. systems, when data is available, and how ready
they are for AI.

Figure 4: Element of current state assessment.

6.1 Modernization Scenario Overview

The company selected for this case study operated in the e-commerce space and managed one of the largest digital
retail platforms in its region, serving millions of active users and processing numerous transactions daily. The core
system had been developed over a decade earlier using Java EE and was structured as a monolithic application with
approximately 8 million lines of code. It handled key operations such as inventory management, order processing,
customer service, and promotional campaigns

—

all within a single deployment package.

Such an architecture created significant operational bottlenecks (Baldwin, 2015). Deployment cycles extended over
several weeks due to extensive testing and quality assurance requirements. For instance, even when only a single
component

—

such as payment processing

—

required additional capacity, the entire application had to be scaled by

provisioning large server instances. Updates were often delayed due to the high risk of unintentionally impacting
unrelated parts of the system. These constraints directly limited business agility, particularly during high-demand
periods like Black Friday and seasonal promotional events. Realizing the need for change, the company continued
with its modernization program. To avoid the high-risk, multiyear effort of rewriting the application from scratch,
they chose to decompose the application with AI-assisted tools in phases.

6.2 AI Tools and Approach Taken

This Modernization journey started with a period of discovery, during which engineers used IBM Mono2Micro to
examine a Java-based application. To identify dependencies and code interaction patterns, the tool combined static
code analysis with runtime log inspection. Over the course of over twenty days, it covered every service call, method
call, and class dependency to produce a dependency graph. By adding built-in clustering algorithms, the AI proposed
groupings aligned with functional domains such as order fulfillment, customer account management, and discount
application (Akerkar, 2019). These groupings were visualized on an interactive dashboard, enabling software

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architects to revi

ew, validate, and refine the AI’s logic based on real

-world usage. In parallel, the engineering team

employed NLP-based tools to scan the source code for business-related terms and logic patterns embedded in
method names and comments. This process enhanced the mapping of technical components to business domains,
ensuring better alignment and reducing the risk of logic fragmentation. The clustering approach shares conceptual
similarities with techniques used in modern telematics systems, where intelligent grouping and real-time analytics
optimize asset management and operational communication

—

demonstrating how data-driven models can

enhance decision-making across domains (Nyati, 2018). Since then, the team has reviewed and refined the
groupings and selected three clusters to start with microservice extraction. The services chosen were clear
separation, low interdependency, high business value user authentication, product catalog, and shopping cart
management.

6.3. Outcomes and Measured Benefits

The outcomes of the AI-assisted decomposition were tangible in a matter of months. Mono2Micro scaffolding code
was used to develop the three first microservices, which were independently deployed using Docker containers
orchestrated through Kubernetes. Load testing showed that under stress, these microservices were handling five
times the traffic independently compared to the old monolith. With these services, development time on new
features within them fell by more than 40% since teams could deploy changes without waiting for full application
builds or for other unrelated teams to coordinate. With the adoption of CI/CD pipelines, these services were
updated every week versus monthly release cycles on the monolith. The analysis of the operational metrics revealed
a decrease in downtime and deployment-related incidents. On the other hand, the modernization effort did not
disrupt business operations (Harris & Krueger, 2015). These new services used internal APIs to kick the monolith to
the curb and use remaining functions while the legacy monolith continued to operate. This hybrid architecture
provided the company an opportunity to modernize incrementally, shrinking both the technical and financial risks
involved.

6.4 Challenges Confronted

Although the process had successful outcomes, it was not without challenges. The main challenge was ensuring the
data stayed consistent throughout every system. But at first, parts of the database were still shared between the
monolith and the new microservices, which led to synchronization issues. To solve this, the team gradually switched
over to a data store per microservice and enabled asynchronous event-driven communication for updates between
them. Another problem was team alignment. Even though AI tools offered better suggestions for service
boundaries, human validation ultimately needed deep business knowledge so that product owners, QA analysts, or
even customer service representatives answered the question of what makes sense. To feed off this new model of
independent service ownership, teams had to increase their communication with each other dramatically. Security,
too, needed careful planning. When these services were externalized, new access controls and API gateways had
to be erected (Suzic & Latinovic, 2020). This entailed configuring token-based authentication, putting forward rate
limits, and making certain that communication between services was encrypted.

7. Tools and Frameworks

For AI-assisted modernization to succeed, the tools and frameworks employed for code analysis, microservice
identification, and architectural transformation are critical (Naeem Syed et al., 2018). Over the past few years, major
cloud vendors and specialized software platforms have introduced robust solutions for decomposing monolithic
systems. These tools blend artificial intelligence with domain-specific insights to help organizations automate some

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of the most intricate aspects of modernization. Their capabilities range from source code clustering to runtime
behavior analysis and scaffold generation.

As with any transformation strategy, a deep understanding of each tool’s

functionality, required inputs, and output limitations is essential for success. Just as advanced generative models
are used to synthesize highly complex and layered 3D environments in digital design, similarly sophisticated AI
systems are now being applied to manage the architectural complexity of enterprise applications

—

highlighting the

transformative potential of AI across technical domains (Singh, 2022).

Table 3:

Tools for AI-Assisted Monolith Decomposition

Tool Name

Supported
Language/Platform

Key Features

AI Capabilities

IBM Mono2Micro

Java

Static/dynamic

analysis,

clustering

AI-driven

grouping,

scaffolding

AWS

Microservice

Extractor

.NET

Code analysis, extraction, AWS-
ready

Rule-based + AI boundary
detection

Microsoft IntelliCode .NET, C#

Refactoring suggestions

AI-assisted recommendations

OpenRewrite

Java (open-source)

Rule-based

code

transformations

Limited AI integration

Service Cutter

General

Use-case

&

entity-based

decomposition

Minimal AI, strong structural
output

7.1 Leading Tools Overview

Several tools are currently available in the market to assist with AI-driven monolith decomposition. Each tool has a
unique feature targeting a certain development stack, programming language, or cloud ecosystem. One of the most
well-known platforms in this space is IBM Mono2Micro, which is specifically designed for modernizing Java-based
monolithic applications (De Santis et al., 2016). The tool uses artificial intelligence to analyze class-level
dependencies, interpret runtime behavior, and extract insights from application logs. Based on this data,
Mono2Micro recommends domain-aligned partitions of the codebase, presenting them through interactive
visualizations that assist developers in evaluating and refining architectural boundaries. While the tool provides
automated suggestions, these recommendations are intentionally nonfinal, enabling engineers to manually adjust
service groupings before generating scaffolding code for each microservice. Similar to how large language models
interpret context in visual question answering systems to generate accurate responses, Mono2Micro applies
structured reasoning across software components to produce interpretable, actionable outputs for modernization
(Singh, 2023).

AWS Microservice Extractor for .NET is focused on splitting .NET technology applications. It provides static code
analysis on top of heuristics to find the business logic and technical dependencies. The output can be deployed
directly to AWS's ecosystem (such as Lambda functions, ECS, and EKS) and can extract functions into services
without code change. The AI provided within Microsoft Visual Studio IntelliCode and other related tools offers
suggestions for refactoring and finding reusable components across developers' .NET and C# applications. Isolating
these patterns is useful, though. While not specifically aimed at monolith decomposition, it does provide great value

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in terms of decomposing code.

Open Rewrite is an open-source framework that aims to improve automated code refactoring. It's especially useful
when enforcing structural code transformations on a large codebase (Antal et al., 2016). While it lacks advanced
clustering or AI-driven microservice extraction, it supports rule-based logic that can work well with AI tools in
isolation of microservices. Service Cutter, another open-source tool, decomposes service micros by calculating
architectural coupling from use cases, domain models, and entity relations. Although it doesn't depend on heavy
AI, it gives structured outputs that can be used as inputs to AI techniques or combined with other tools.

7.2 Input Requirements and Output Capabilities.

The AI assistant tools used for decomposing monolithic systems typically require multiple types of input to function
effectively. At a minimum, they need access to the source code to perform static analysis. However, tools such as
IBM Mono2Micro or AWS Microservice Extractor are significantly more effective when combined with runtime data,
including log files, execution traces, and application performance metrics. This enriched data allows the tools to
identify real-world usage patterns and dependencies that may not be obvious through static code analysis alone.
Much like how image captioning models benefit from contextual visual and semantic cues to generate meaningful
descriptions, these AI-driven decomposition tools depend on comprehensive contextual inputs to suggest reliable
and business-aligned service boundaries (Sukhadiya et al., 2018).

Some of these tools can be used without sophistication and generate different outputs. These visualizations offer
code dependency, interaction graphs, and suggested service grouping representations. More powerful tools
produce artifacts ready to use: scaffolding for microservices, Docker configurations, Kubernetes manifests, and
RESTful API templates. These significantly decreased the time needed for the hands-on development and
deployment stages following the analysis phase. For example, each microservice that Mono2Micro identifies can
generate Java Spring Boot projects, preconfigured to route to and from the API, and make calls to external services.
Also, the AWS Microservice Extractor creates corresponding directly integrated Code Pipeline instantiating
refactored services for pushing to production environments.

The diagram below visualizes how AI systems process a variety of inputs

—

including questions, voice commands,

text, and images

—

to generate actionable outputs such as decisions, predictions, suggestions, and even creative

work. In software modernization, a similar process is followed, transforming source data into deployable assets.

Figure 5: artificial-intelligence

7.3 Evaluation Criteria and Tool Limitations

Choosing the right AI-powered tool to perform monolith-to-microservices decomposition is driven by an

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organization’s technological stack, infrastructure maturity, and internal technical prowess. Although each
organization’s needs may d

iffer, several factors must be considered in the evaluation process. The first issue is that

there is no language and framework support; most tools are developed with particular programming languages in

mind. For example, IBM’s Mono2Micro works very well f

or Java applications, while AWS Microservice Extractor

targets the .NET ecosystem. Then, the transparency of AI models is necessary. Unlike black boxes that are hard to
understand or validate, explainable outcomes such as visual clustering diagrams, interactive dashboards, and
editable recommendations help developers trust and allocate budgets better.

Integration capabilities are important. A robust modernization tool must seamlessly integrate with CI/CD pipelines,
version control systems (such as Git), and container orchestration platforms like Kubernetes to support a
streamlined development lifecycle. Scalability is equally critical

—

users should be able to work with enterprise-scale

applications, often encompassing millions of lines of code, without encountering performance lags or crashes
(Crookshanks, 2015). Just as important is the degree of user control these tools provide. The ability to fully edit AI-
generated recommendations ensures that architects and developers can apply domain-specific expertise and make
necessary adjustments to align with project goals. This mirrors how AI-powered platforms in other domains

—

such

as personalized career coaching

—

enable users to engage dynamically with recommendations, tailoring outputs

based on individual context and feedback. These parallels highlight that, regardless of domain, AI systems achieve
the greatest impact when they support human agency rather than replace it (Karwa, 2023; Karwa, 2024).

Although these are strong points, most current AI tools are not fully autonomous. It is their job to supplement, but
not replace, the decision-making of software architects. There are issues in highly heterogeneous and legacy
environments (such as using an outdated programming language such as COBOL or Delphi) with some of the tools.
Scenarios of this type may lie outside the realm of modern AI solutions and necessitate more specialized
customization or even manual intervention. AI tools are very effective at modernizing, but only when juxtaposed
with human oversight, aligned strategy, and a realistic understanding of the limits of what it can do.

8. Challenges and Risks

AI-assisted modernization has many advantages in reducing complex and time-intensive tasks that can be
automated (Kim & Lim, 2021). However, the journey from the monolithic system to the microservices architecture
is not smooth sailing. For a modernization effort to be successful, an understanding of and a way to address both
technical and organizational risks is required. Though powerful, AI tools cannot solve every problem alone, and
unwieldy AI tools might cause more problems than they solve. This section looks at the most common challenges
and risks that heritage modernization with AI-assisted solutions encounters.

Table 4:

Common Risks in AI-Assisted Modernization

Risk Type

Description

Recommended Mitigation

Incomplete

Dependency

Mapping

Static/dynamic data may miss key
relationships

Combine methods, extend observation
windows

Domain Misalignment

AI suggests groupings lacking business
context

Human validation by domain experts

Over-Decomposition

Excessive

microservices

increase

operational overhead

Set service granularity thresholds

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Risk Type

Description

Recommended Mitigation

Team Resistance

Cultural pushback against AI and new
workflows

Training,

communication,

change

management

Tool Limitations

Poor support for legacy languages or edge
cases

Manual

intervention,

hybrid

modernization

8.1 Missing or unclear dependency information

One of the greatest risks in decomposing monolithic applications is the use of incomplete or inaccurate system
information. For many years, many legacy systems have been in use without comprehensive documentation or up-
to-date architectural diagrams. These things make it hard for AI tools to grasp application structure and
relationships completely. However, applications that use dynamic code loading, reflection, or a configuration file
external to the application to modify program logic can often defeat static analysis, missing important runtime
behaviors. Moreover, dynamic analysis might only see a portion of the behavior during the observation window,
depending on the inputs or usage patterns used during analysis. When those captured logs do not show all the
scenarios of usage in a system, the resulting service boundaries are likely built on a partial view of the system
(Pasquier et al., 2017).

8.2 Automatically decomposing into domain contexts

AI systems are good at taking large datasets and identifying patterns and correlations, but they don’t have business

domain knowledge. Consequently, automated decomposition can produce technically sound recommendations
that are at odds with the functional reality of the business. For example, an AI tool might bring product and pricing
modules together simply because both have data models, even though these are managed by separate
departments and governed by different business rules. This becomes more acute in highly regulated industries,
including healthcare, banking, and insurance, where service separation is not only about code modularity but also
a requirement for compliance and data privacy. AI-assisted decomposition without domain-specific validation may

result in service designs that violate an organization’s policies or create legal risk.

Human-in-the-loop processes should always be integrated from scratch. AI-generated outputs need to be reviewed
by subject matter experts to ensure that business logic is constrained, boundaries are accurate regarding real-world
workflow, and sensitive data is protected. As illustrated below in Figure 6. Just as a biologist interprets machine-
generated insights in the context of biological systems, software architects must ground AI outputs in organizational
realities to achieve meaningful and sustainable transformation.

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Figure 6: An example of AI for Science

8.3 Risks of Prior Decomposition

Another danger is over-decomposition

—

creating too many microservices. Although the concept of modularity is

desirable, overly fine subdivision results in additional complexity, greater maintenance expense, and more
degraded performance. This leads to each microservice being demanded to have its own deployment pipeline,
monitoring tools, logging configuration, database or data access interface, and security controls (Ahmadvand et al.,
2018). As the number of services grows beyond a manageable level, the overhead of maintaining and coordinating
these services outweighs the benefit of scale. Another name for this is the microservice sprawl problem. When
misused, the risk is that AI tools can help find even weakly interacting components as possible candidates for
independent services. Organizations may find it difficult to manage, monitor, and troubleshoot services without
thoughtful consolidation or prioritization of these suggestions.

8.4 Cultural and Process Resistance

But it’s not a one

-sided conversation. Furthermore, legacy modernization

—

helped by AI

—

brings a lot of

organizational change. Years of development teams working with monolithic systems may resist the shift to
microservices because they are unfamiliar with or scared of being made redundant. Especially in the architectural
field, skepticism about AI-generated and AI-generated decisions, especially for those with experience as architects,
because they value manual analysis and decision-making. Besides this, switching to microservices requires changes
to team structure, workflows, and operational tooling. Today, teams must change to the latest deployment models,
API contracts, or even decentralized service ownership. Modernization efforts can run aground on internal friction
unless change management, training, and stakeholder engagement proceed properly.

9. Best Practices and Guidelines

The process behind the transition from monolithic to microservice architecture, especially when there's help from
the AI, plays a much bigger role than the tools used. With a weak foundation in methodology, governance, and
team coordination behind it, even the best technologies might not perform better than expected. This section
summarizes a collection of best practices and practical guidelines based on experiences to help ensure that AI-
assisted legacy modernization is efficient and sustainable. Based on industry experience and case studies, these
recommendations provide a roadmap for dealing with complexity, maintaining business value, and reducing risk.

9.1. Domain-Driven and Business-Centric Planning

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It is important to understand the business domains that the software supports before initiating technical changes.
The domain of this principle is rooted in Domain Driven Design (DDD), where the key idea revolves around creating
a software architecture that fits our real-world business model. While, once again, AI tools can help cluster and
suggest service boundaries, they need to be validated against domain knowledge. First, define which business
functions, such as order processing, inventory management, user accounts, or payment systems, must talk to each
other (Rainer et al., 2020). Include business analysts, product owners, and domain experts early during
decomposition. With their insights, AI-generated service grouping would respect functional boundaries and not
create logic across teams or departments. It reduces the complexity of inter-service communication and later
defines service ownership. Domain models, workflow diagrams, and user stories all help drive and fine-tune AI
suggestions. Domain-driven reasoning should be enhanced by AI, not substituted.

As illustrated below in Figure 7, highlights how continuous communication with domain experts reinforces and
improves software architecture. This cycle ensures that architecture evolves iteratively through expert feedback
and real-world development experience, making it both technically robust and business-aligned.

Figure 7:

Domain Driven Design

9.2 Incremental decomposition patterns

It's high risk because decomposing an entire monolithic application is far too complex to do in one phase. The use
of incremental decomposition patterns, such as the Strangler Fig Pattern, represents a more practical and low-risk
approach. This strategy consists of gradually replacing parts of the monolith with new microservices, doing so
without disabling the rest of the system. A relatively isolated module with high business value and low
dependencies

—

such as user authentication or product catalog management

—

should be selected as a starting point

(Tahir et al., 2021). Interactions within the application can then be mapped using AI tools to support the generation
of scaffolding code. Once the microservice is built and deployed, service calls should be rerouted from the monolith
to the new microservice through an API gateway or proxy layer. This process should be repeated iteratively for
additional modules to enable a gradual and controlled migration. This pattern minimizes the disruption caused by
the release and allows time to validate system behavior, train staff, and tune the monitoring tools on the new
generation of servers. In addition, it allows early benefits to be realized until transformation is complete.

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9.3 Continuous Testing Integration and DevOps

Development and operational practices need to change to adapt to microservices' shift. Ensuring quality and
performance during modernization requires integrating DevOps from the start. Microservices should be
independently testable, deployable, and observable. CI/CD pipelines should be established for each service early in
the process. This ensures that teams can develop against the AI-generated scaffolding code and immediately test
and deploy within an automated workflow. Services should be containerized using Docker and deployed using
Kubernetes for orchestration. This approach supports scalable deployment practices and maintains consistency
across environments. Automated testing must be embedded at different levels, including unit, integration, and end-
to-end testing. Observability is important in microservices, especially given their distributed nature, and
Prometheus, Grafana with Dashboard Builder, and distributed tracing solutions (e.g., Jaeger) are essential tools to
support that. These provide insights into system performance and the detection of problems across services.

9.4 Organizational collaboration and governance.

Modernization is not solely a technical endeavor; it also involves significant cultural and organizational changes. AI-
assisted decomposition can only succeed when supported by cross-functional collaboration and well-defined
governance structures. The initial step involves establishing small, autonomous teams that take full ownership of
individual services. These teams should include developers, testers, product owners, and operations staff (Poth et
al., 2020). Clear responsibilities must be defined

—

indicating who owns each service, who manages deployments,

and who provides operational support. This decentralization fosters both agility and accountability throughout the
development lifecycle. Create governance standards that cover service versioning, API documentation, security
policy, and error-handling protocols. Although teams work independently in principle, they should strive to comply
with shared guidelines to promote systemic reliability and consistency. Create open communication channels where
people share AI-generated insights, decomposition plans, and test results with transparency across teams. This will
help build trust in AI tools and organize collective decisions.

10. Recommendations

Moving to microservices is a large technical and organizational leap. Artificial intelligence, however, can be
approached with well-informed strategies to make it faster, more accurate, and more scalable. This section
synthesizes the insights of the time and offers practical and strategic advice for organizations considering or
transitioning to AI-assisted modernization. These recommendations are classified under four key categories:
strategic alignment, technical execution, organizational enablement, and long-term sustainability. Figure 8
illustrates this architectural constraint with the unified structure presented in monolithic applications.

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Figure 8: monolithic-application

10.1 Strategic Recommendations are given.

Modernization should not be viewed merely as a technical upgrade; rather, it must be recognized as a strategic
initiative aligned with clearly defined business outcomes. The first recommendation is to explicitly define the
business goals driving the modernization effort. Whether the objective is to improve scalability, reduce time to
market, or support the delivery of new digital services, these goals should inform the selection of tools, the
identification of decomposition targets, and the choice of appropriate deployment strategies. Leadership should
treat AI-assisted modernization as a multi-phase transformation, not a one-time project (Geetha et al., 2019). These
phases should include assessment, planning, initial pilots, iterative rollouts, and performance reviews. A one-week
sprint should produce some measurable value. Setting up key performance indicators (KPIs) early in the process
makes tracking progress easier and justifies further investment. Moreover, strategic buy-in from executive
leadership is required. From an operational standpoint, top-down support helps provide the teams with the
resources, time, and training to participate in transformation activities without impacting ongoing business
operations.

10.2. Technical Implementation Guidelines

From a technical perspective, AI tools should not be seen as autonomous agents but as decision-support systems.
Developers, architects, and domain experts must stay engaged in reviewing AI-generated recommendations against
their business needs and architectural standards. Start with small, low-risk things. Pick up relatively independent
services that the development team understands well. The behavior of the component is analyzed using AI, which
attempts to propose boundaries and scaffold. This initial rollout provides much-needed feedback to fine-tune AI
configs and decomposition strategies for the more complex modules. Make sure there is infrastructure in place.
This encompasses CI/CD pipelines, service mesh configurations, containerization standards, and observability
tooling. Therefore, AI-generated code should fit naturally into current development workflows. It is preferable to
have tools that make visualizations and are capable of editing groupings and logs so that they are transparent and
trustable, making easy validation.

10.3 Organizational readiness and training

Technical change must be accompanied by organizational change. Cross-functional team training is one of the most
effective ways to improve product velocity and quality. In addition to understanding the fundamentals of

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microservice design and DevOps practices, teams must also grasp how AI tools function, what inputs are required,
and how to accurately interpret the outputs. Secondly, set up multidisciplinary squads of developers, QA, ops, and
business analysts. This will improve decision-making regarding service boundaries and service deployment
strategies. Within a well-defined governance framework, each team should be autonomous in managing its own
services. It is all about communication. Regular briefings, documentation updates, and opportunities for open
feedback reduce resistance and guarantee alignment across departments. Consider designating some internal
modernization champions

—

people who serve as bridges between IT and business leadership, getting things going

and clearing expectations.

10.4 Long-term Maintenance and Continuous Evolution

The ultimate recommendation is to view modernization as a continuing process rather than as a one-time aim. After
the first set of services are deployed and stabilized, continuous improvement can commence. Assess the service
health, measure the deployment frequency, and analyze use patterns to discover areas of savings. This should
happen in parallel to continue refining AI models (Lu et al., 2022). As the organization collects more inputs (logs,
user behavior, development activity), that information can be used to help improve future decomposition accuracy.
This allows smarter automation to take place over time.

It is important to take a forward-thinking approach to managing technical debt. When designing services that are
expected to grow and scale, care must be taken to avoid unintentionally creating distributed monoliths

—

systems

that appear modular on the surface but remain heavily interdependent beneath. True modularity and service
independence are achieved through regular architectural reviews, thorough dependency audits, and continuous
refactoring cycles. Living Artifacts are artifacts whose maintenance is ongoing after creation. They are extremely
important for new developers to onboard, for debugging production issues, and for facilitating cross-team
collaboration.

11. Future Outlook

AI in Software Modernization is expected to become more sophisticated and deeply integrated. Most current tools
are focused on supporting analysis and scaffolding; however, upcoming innovations are likely to represent context-
aware AI systems capable of reasoning not only about code structures but also about business objectives,
compliance requirements, and user behavior. In time, even entire microservices may be written autonomously by
Generative AI models with their own test suites, database configuration, and deployment scripts. These tools will
generate selectively tailored architectural patterns for specific industries, substantially reducing design overhead.
These models could be integrated into development environments to suggest real-time decomposition solutions
for teams while building and maintaining legacy systems.

One promising additional development is the prevalence of autonomous modernization pipelines (Dickerson &
Worthen, 2024). Without waiting for the start of a formal modernization project, these systems could continuously
scan live applications, recognize monolithic patterns or performance bottlenecks, and propose/recommend
changes or carry out changes. Such pipelines, when paired with AI-driven observability platforms, could create self-
optimizing systems that adapt in real time to usage trends and resource constraints. Modernization in this future
will never be done; it will simply occur invisibly, as an operating principle, not an isolated effort. Today's legacy
systems will incrementally evolve with AI and be controlled by teams that curate and validate change, not manually
running every detail. This will rewrite the enterprise software lifecycle and how organizations think about technical
debt, software aging, and architectural design (Martini et al., 2015). Organizations need to prepare for this shift by
building internal capabilities, including AI tools, change management, cross-disciplinary collaboration, and long-

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term platform strategy. Early adopters of this evolution will have modern, ever-evolving systems ready for any
challenge the future may hold.

As illustrated below in Figure 10, AI is being applied across diverse domains

—

from customer analytics and supply

chain management to personalized service delivery and risk mitigation. These expanding use cases underscore the
potential of AI to become a central force in the next era of intelligent, adaptive enterprise systems.

Figure 9: artificial-intelligence-ai-in-software-development

12. CONCLUSION

Legacy system modernization is no longer the domain of the 'technology' company only. Access to data has become
a necessity in virtually every industry. Organizations across the spectrum

—

from finance and healthcare to retail

and manufacturing

—

are under growing pressure to upgrade their aging digital infrastructure to meet evolving

customer expectations, security standards, and market conditions. In terms of this, the drift from monolithic
architectures to microservices is a technical enhancement and a strategic need to gain operational agility,
scalability, and long-term resiliency.

The world has evolved past monolithic applications, once the standard in enterprise environments, to now imposing
some major challenges. These systems tend to be large, complex, and deeply entangled, with scaling, updating, and
integration with modern cloud-native services being hard. A microservices architecture (or microservices
programming model) provides an architectural alternative, breaking rigid systems into smaller, inseparable, discrete
components that enable faster development, independent scalability, and better fault tolerance. That transition is
not easy, especially in systems that have evolved over a few decades and house the critical business logic. This
transformation has become a critical enabler of artificial intelligence. The article outlines various aspects of AI-
assisted modernization, beginning with data collection and dependency analysis and culminating in the
identification of microservice candidates and the use of code scaffolding tools. Practical examples and tool
evaluations demonstrate that AI can significantly reduce manual effort, accelerate project timelines, and enhance
the quality of architectural decisions.

Relying solely on the automatic application of AI does not guarantee success. Challenges such as incomplete
dependency data, overreliance on a single data source, and resistance to organizational change underscore the
importance of intentional deployment planning and human oversight. To maximize effectiveness, AI tools must be

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combined with best practices, like incremental refactoring strategies, robust DevOps pipelines, and clearly defined
governance models. Cross-functional collaboration and sufficient training of teams to adopt such new technologies
and adjust to changing development procedures are just as important. AI is not an alternative to human expertise
but an amplifier for modernization. Modernization becomes a responsible, collaborative, business-aligned strategy
and becomes structured and scalable. Organizations adopting digital transformation with a balanced blend of both
aspects lay the foundation for long-lasting digital growth through modern, future-ready systems that combine to
form adaptable, intelligent, better-enabling business outcomes in years to come.

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