In today’s rapidly evolving competitive landscape and with the quick rise of AI, modernization has become an urgent priority. However, modernizing outdated applications remains a complex challenge. In this context, AI helps overcome these hurdles with greater depth and lower risk, accelerating the understanding of existing systems, freeing time for more strategic decisions, and driving improvements in speed and quality.
Move faster & better with AI
Application modernization programs are often constrained by tight timelines, limited budgets, and increasing business expectations. Generative AI directly addresses this pressure by accelerating structured, repetitive, and documentation-heavy tasks that traditionally consume significant developer capacity. Activities such as code documentation, boilerplate generation, refactoring, unit test creation, and interface mapping can be executed significantly faster with AI assistance. Research shows that developers can complete certain modernization tasks up to 50% faster when supported by AI tools.Beyond task automation, AI reduces friction during system exploration. Legacy environments often lack comprehensive documentation, which forces development teams to spend weeks understanding dependencies and embedded logic. Meanwhile, AI models can quickly summarize large codebases, highlight key components, and surface integration points. According to Gartner, AI-native development platforms will result in 80% of organizations evolving large software engineering teams into smaller, more agile teams augmented by AI.
Improve coding experience
Application modernization places significant cognitive and technical demands on development teams. Engineers are required to analyze complex legacy logic, manage architectural transitions, and adapt to modern frameworks and technologies while ensuring system stability and business continuity. Thus, generative AI can offer contextual explanations, suggest alternative implementations, and help teams adapt code to modern environments.
As a result, developers report higher engagement and satisfaction when repetitive work is automated and attention can shift to higher-value problem solving. AI-assisted code generation, when paired with human review and iterative prompting, has demonstrated improvements in readability, maintainability, and early defect detection. This is particularly valuable in modernization programs, where poor-quality refactoring can introduce long-term instability.
To further improve developers’ experience, AI solutions such as FPT’s GenAI Suites integrate AI into both software development and business workflows. Specifically, tools such as CodeVista supports intelligent code generation and modernization use cases, including legacy migrations such as COBOL to Java, while IvyChat enables knowledge retrieval, data interaction, and process automation across business units. Specifically, CodeVista is estimated to accelerate development timelines by approximately 48% on average, helping organizations accelerate release cycles and bring modernized applications to market faster. Additionally, FPT also introduced EMT - a unified AI-enabled solution to support writing & rebuilding approaches to modernize legacy applications to open and cloud native architecture.
Fill the talent gap
Even when modernization accelerates, the availability of skilled talent remains a major obstacle. Expertise in cloud-native architecture, cybersecurity, data engineering, and legacy platform migration is limited, and demand continues to outpace supply. According to research, 64% of tech leaders identified lack of talent as a significant obstacle in adopting new technologies and modernizing legacy infrastructure.
AI helps alleviate this pressure by enabling less experienced developers to navigate complex systems with greater confidence by providing contextual guidance and summarizing unfamiliar codebases. AI also preserves institutional knowledge by automatically generating documentation and capturing business logic that might otherwise reside only in the minds of senior engineers. Additionally, this technology supports migration from aging platforms such as COBOL-based systems to modern technology stacks. By amplifying human expertise, organizations can modernize at scale without proportionally increasing headcount, creating a more sustainable transformation model.
Avoid the current pitfalls in application modernization
The life and shift trap
One of the most common mistakes in application modernization with AI is technical debt. Years of incremental fixes, outdated design patterns, and undocumented dependencies create systems that are fragile and difficult to scale. Simply translating legacy applications into modern languages or migrating them to the cloud without redesigning underlying architectures risks recreating the same inefficiencies in a new environment. This “lift-and-shift” approach may deliver short-term progress but often preserves technical debt, operational complexity, and long-term cost burdens.
Therefore, businesses must take a deliberate approach to identifying, prioritizing, and reducing technical debt before or alongside modernization efforts. This means evaluating which systems should be refactored, re-architected, replaced, or retired instead of automatically migrated.
While generative AI can support this process, it must be used strategically. Rather than maximizing code conversion output, organizations should leverage AI to analyze legacy artifacts, map system dependencies, clarify embedded business logic, and surface high-value modernization opportunities. As highlighted by McKinsey, a clear, value-driven roadmap that connects modernization initiatives to measurable business outcomes is essential to avoid superficial transformation.
Upskilling is still necessary
AI can accelerate modernization, but it does not eliminate the need for skilled professionals. Thus, upskilling remains critical in areas such as AI engineering, cloud architecture, cybersecurity, data engineering, and modern software development. Without internal capability development, organizations risk becoming overly dependent on external providers and losing strategic control over their technology evolution.
Workforce planning should be continuous and aligned with modernization objectives. Despite fluctuations in the broader technology job market, access to high-quality technical talent remains a top executive concern. According to Deloitte, nearly 90% of technology leaders report that recruiting and retaining skilled talent is a moderate or major challenge. Businesses should manage technology talent as a strategic asset—distinguishing between roles that require deep specialization and those that can be supported through cross-trained, adaptable teams.
Accelerating Application Modernization with FPT
With more than a decade of experience in cloud solutions and application modernization, FPT has developed its proprietary Cloud Modernization Factory (CMF), a comprehensive framework built on proven best practices to streamline and optimize large-scale modernization programs. The CMF enhances business agility, improves service quality, and drives measurable cost efficiencies - accelerating development cycles by up to 40%, reducing implementation costs by 30%, and mitigating as much as 60% of common modernization risks to ensure a smooth and effective transformation journey. Supported by a global delivery model spanning onshore, nearshore, and offshore teams, flexible 24/7 support, and a workforce of more than 1,000 AI engineers alongside a vast pool of software development experts, FPT provides scalable, end-to-end capabilities to help organizations modernize with confidence and speed.
