What technologies and architectures enable application modernization?

• Technologies used in application modernization
• Application modernization strategies and examples
• How microservices support app modernization
• The role of containers and Kubernetes in app modernization
• How DevOps and CI/CD enable modernization
• How data modernization supports application modernization
• What an AI-ready application architecture looks like
• How to choose the right application modernization technologies

Application modernization typically combines multiple technology domains rather than relying on a single platform or tool.

Common modernization technologies include:

• Cloud platforms such as AWS, Microsoft Azure, and Google Cloud
• Microservices and API architectures
• Containerization technologies such as Docker
• Kubernetes and cloud-native orchestration platforms
• DevOps and CI/CD automation
• Infrastructure as Code (IaC)
• Modern data platforms and data engineering tools
• Event-driven architectures
• AI-assisted development and modernization tooling
• Security, observability, and platform engineering capabilities

The objective of application modernization is to create software and operating environments that are easier to deploy, scale, secure, maintain, and evolve as business needs change. And the need is real:

• 86% of organizations still have at least 25% of core business apps running on legacy platforms.
• 70% of IT teams spend up to 25 hours per week patching them.
• 60% of senior developers are considering leaving due to legacy system frustration.
• 77% of companies are investing in modernization.
• +50% of enterprises will use cloud platforms to accelerate modernization and other business initiatives.

Organizations rarely modernize every application in the same way. Different workloads require different migration and modernization approaches. The right modernization strategy depends on business value, technical complexity, operational risk, and future strategic importance.

Common strategies include rehosting, replatforming, refactoring, and repurchasing.

When you rehost (or lift and shift), organizations move applications to the cloud with minimal changes. This strategy is best for reducing infrastructure costs, exiting a data center, and for speed-to-cloud initiatives. While modernization benefits may be limited in the near-term, it can be a fast kickstart to modernization and start momentum for high-value change.

When organizations replatform, they move applications while making targeted improvements.  

What changes? A regional bank migrates its loan origination application from an aging on-premises infrastructure to a cloud-native managed database and application platform.  

What stays the same? They preserve most of the existing business logic and customer workflows like “Applicants with a FICO score above 720 and a debt-to-income ratio below 40% qualify for Preferred Tier pricing.”  

What gains happen behind the scenes?

• Elastic infrastructure
• Automated scaling
• Modern DevOps pipelines
• Improved disaster recovery
• Lower infrastructure costs
• Faster releases
• Better observability and monitoring

After replatforming, the same loan application, underwriting, approval, and funding workflows run on cloud-native infrastructure with managed databases and automated deployment pipelines, reducing release cycles from weeks to days, improving resiliency and scalability, and lowering infrastructure costs—all while preserving the underwriting rules, approval processes, and customer experience that the business already trusts.

Refactoring means creating a new application that delivers the same core business capabilities as the existing system applications—and usually incorporating some value-add code. Refactoring is valuable for modernizing strategic business capabilities, addressing severe technical debt, and making ready for AI and digital transformation initiatives. It’s a big investment, but it offers organizations the greatest long-term flexibility.  

A property and casualty (P&C) insurer decides to refactor its commercial underwriting platform. Rather than preserving the 20-year old legacy infrastructure, the insurer redesigns and refactors the platform around cloud-native services, APIs, event-driven workflows, and modern data products.  

Why? The existing platform was designed for agent-submitted business, batch processing, and highly manual risk assessment. Now, after refactoring, the insurer can automatically ingest third-party data, apply AI-assisted risk scoring, route exceptions to underwriters, and generate quotes in minutes while maintaining governance and human oversight for complex risks.  

While refactoring requires significantly more investment and organizational change than replatforming, the insurer has fundamentally changed and significantly improved how underwriting, pricing, and policy issuance operate—they can operate with capabilities that were not possible within the constraints of the legacy system.

Repurchasing involves retiring a custom-built or legacy application and adopting a commercial SaaS platform or industry solution that provides the same business capability. Organizations typically choose this path when the process is largely a commodity function, the cost of maintaining custom software exceeds its strategic value, or modern SaaS platforms can deliver capabilities faster, cheaper, and with less operational complexity.

A wealth management firm replaces its internally developed client onboarding and account opening platform with a SaaS-based solution. The existing platform has 12 years of custom code, requires specialized support resources, and consumes significant development capacity to keep up with regulatory changes and integrations.

Because account opening, document collection, know your customer (KYC) verification, and workflow routing are (largely) standardized processes, the firm can see these capabilities don’t create competitive differentiation. By replacing the custom platform with SaaS, the organization simplifies operations, reduces technical debt, lowers maintenance costs, and allows technology teams to focus on higher-value areas like portfolio analytics and building more personalized client loyalty experiences.

Microservices break large monolithic applications into smaller, independently deployable services. Microservices are particularly valuable when organizations need to support frequent business changes, digital product innovation, or large-scale growth.

Each service typically owns a specific business capability, plus its own deployment lifecycle, scalability requirements, and APIs.

Microservices help organizations move from application-centric thinking to business-capability-centric architectures. The benefits of modernizing around microservices include include:

• Faster release cycles
• Improved scalability
• Greater resiliency
• Independent team ownership
• Easier adoption of new technologies

Containers and Kubernetes work together to make modern applications easier to build, deploy, and manage at scale. Containers simplify application portability. Kubernetes enables applications to operate reliably at enterprise scale.

• Containers package applications and everything they need to run (like code, libraries, settings, and dependencies) into portable units, ensuring they behave consistently across different environments.
• Kubernetes then manages those containers by automating deployment, scaling, monitoring, and recovery.

In application modernization, this combination helps organizations move away from rigid, hard-to-maintain infrastructure toward more flexible, cloud-ready architectures. These modern architectures support faster releases, improved reliability, and changing business demands without requiring teams to manually manage every application component.

DevOps is an operating model that brings together software development, IT operations, security, and business teams to improve how applications are built, tested, released, and maintained. For organizations modernizing applications, DevOps helps create the speed, agility, and accountability needed to keep pace with changing business and customer demands.  

A key component of DevOps is Continuous Integration and Continuous Delivery (CI/CD). CI/CD uses automated pipelines to move software from development into production by continuously integrating code changes, running automated tests, validating security requirements, deploying updates, and managing releases. This reduces the need for manual processes that often slow down development and introduce errors.

Together, DevOps and CI/CD help organizations:

• Release new features faster
• Reduce deployment risk
• Improve software quality
• Increase developer productivity
• Create more reliable customer experiences

As application modernization and AI-assisted engineering keep evolving, CI/CD pipelines will be foundational for delivering software safely, consistently, and at enterprise scale.

Data modernization is often the critical dependency that determines whether application modernization succeeds or falters. Application modernization and data modernization tend to go hand-in-hand.  

Why? Modern applications depend on a set of foundational data capabilities that enable scalability, agility, analytics, and AI adoption:  

• Trusted data foundations
• Real-time data access
• Scalable cloud data platforms
• Strong governance and security controls
• AI-ready data architectures

To deliver these foundational capabilities, organizations should invest in data modernization technologies, platforms, and practices that improve how data is managed, integrated, governed, and consumed across the enterprise.

Common data modernization capabilities include:

• Cloud data platforms
• Data lakes and lakehouses
• Real-time integration pipelines
• Data observability
• Master data management
• Metadata and governance frameworks

Without modern data foundations, modernization efforts often struggle to scale AI, analytics, and digital experiences.

A common AI-ready application architecture includes six foundational layers:

1. The experience layer engages users.
2. The business services layer delivers capabilities.
3. The platform layer enables scale.
4. The data layer provides context.
5. The governance layer manages risk.
6. The intelligence layer drives automation and decision-making.

The experience layer is where customers, employees, partners, and other users interactwith the business through web applications, mobile experiences, conversational interfaces, and AI assistants. The experience layer determines how people access information, complete tasks, and engage with products and services.

Why it matters: As organizations modernize, this layer makes it easier to deliver better experiences across channels while adapting to changing customer expectations.

The business services layer contains the core capabilities that power the organization. APIs, microservices, event-driven services, and workflow orchestration technologies enable business functions to operate independently while remaining connected.

Why it matters: This layer helps teams make changes where they're needed without creating unnecessary complexity or risk across the broader technology environment.

The platform layer provides the operational foundation that allows applications to be built, deployed, scaled, and managed consistently. Technologies such as containers, Kubernetes, platform engineering practices, and observability tools help organizations improve reliability, accelerate software delivery, and reduce operational complexity.  

Why it matters: This layer helps organizations deliver technology faster, reduce operational disruptions, and support growth without constantly rebuilding their systems.

The data layer ensures that applications, analytics, and AI systems have access to trusted, governed, and accessible information. It brings together operational data, data products, real-time pipelines, and modern data foundations that allow information to move efficiently across the enterprise.

Why it matters: As organizations scale AI across the enterprise, the data layer is the linchpin between ambition and outcomes.

The governance layer establishes the controls, policies, and safeguards that allow innovation to scale responsibly. Security, identity management, compliance capabilities, and AI governance frameworks help organizations manage risk while maintaining trust with customers, regulators, and stakeholders.  

Why it matters: Good governance inspires trust. When people trust the data, systems, and controls, they can move faster with less risk and fewer surprises.

The intelligence layer transforms data and workflows into actions, recommendations, and decisions. AI copilots, AI agents, knowledge systems, and orchestration capabilities enable organizations to augment human work, automate routine activities, and create more adaptive business processes.

Why it matters: This is the layer that puts AI to work. It helps organizations use AI in daily tasks, workflows, and decisions instead of isolating it in tool-use or experiments.

The best modernization strategy starts with business outcomes, not technology preferences. Technology leaders should evaluate modernization decisions against five questions:

1. Does it reduce technical debt? Will it simplify architecture, reduce maintenance effort, or eliminate legacy constraints?
2. Does it increase delivery speed? Will teams be able to release changes faster and with less risk?
3. Does it improve resilience and security? Will it strengthen reliability, governance, compliance, and operational visibility?
4. Does it create AI readiness? Will it improve access to trusted data, workflow automation, and future AI adoption?
5. Does it create long-term adaptability? Will the organization be more capable of responding to future market, customer, and regulatory changes?

For many organizations, answering these questions isn't the hardest part. Building confidence across financial, security, compliance, governance, and workforce considerations is often where modernization becomes difficult.