Services
Enterprise Solutions
Accelerate Enterprise Digital Transformation for Sustainable Change
Highlights
- Legacy application platforms are increasingly unable to meet business demands, making artificial intelligence (AI)-driven modernisation—and not just an IT rewrite or refresh—a strategic imperative.
- The convergence of AI, generative AI (GenAI), and low-code create an integrated modernisation ecosystem capable of compressing timelines by 40-50% while reducing manual effort by 30-40%.
- A phased blueprint—built from our experience from over 20 such engagements—can help transform the modernisation journey for companies from a one-time project into a continuous enterprise capability.
In this article
In this articleinpage
The modernisation imperative
Many modernisation programmes fail, not necessarily because of the technology in use.
They fail because organisations treat them as mere IT milestones and not as a key peg for business transformation.
Businesses today operate in deeply complex application environments built over decades. These environments are frequently a hybrid of legacy, packaged, custom-built applications and emerging cloud services, all heavily integrated to deliver fundamental business processes. Even if these applications remain functional, they are often constrained by outdated architecture, lack of scalability, and escalating maintenance costs. The result is organisations running increasingly fragile systems, poorly suited to deal with evolving business demands, such as customers’ need for seamless digital experiences, regulators’ push for quicker compliance, and markets’ penchant to reward rapid innovation.
This landscape has driven a fundamental change in businesses' attitudes towards modernisation. Rather than viewing modernisation as a collection of point upgrades or isolated technology refreshes, organisations are increasingly considering it as a strategic imperative. The emphasis is shifting away from system replacement to re-engineering applications for more intelligent, adaptive, and strategic long-term business outcomes.
Many traditional methods for modernising applications are built around linear, project-based models. They commonly involve time-consuming manual analysis and long development cycles with extensive customisation, which makes progress slow and outcomes uncertain. These approaches don’t work well in large-scale modernisation and are often ineffective in addressing years of accumulated technical debt across the enterprise.
Artificial intelligence (AI) is bringing in the next wave in application modernisation. It provides organisations with the ability to assess application landscapes at scale, automate decisions, and optimise systems over time using real-world usage and performance data. This enables modernisations to evolve from a one-time transformation project into an adaptive, continuously improving system that drives sustained business growth.
GenAI and low-code as transformation catalysts
GenAI unlocks legacy understanding at scale and then low-code closes the last-mile delivery gap, thus accelerating delivery.
GenAI solves the legacy comprehension problem. Many enterprise software systems were built on older technologies with little documentation and critical business logic deeply embedded within the code. A GenAI platform purpose-built for enterprise modernisation (not a general-purpose coding assistant) analyses legacy codebases, including mainframe and COBOL environments, extract undocumented business rules, and generate human-readable technical documentation (see Figure 1). This replaces weeks of manual reverse engineering with a structured, fact-based foundation.
Fig. 1
Further, the AI-driven transformation platform automates the execution phase, performing language-to-language conversion, including mainframe to Java, COBOL to cloud-native, and similar legacy-to-modern migrations, along with code refactoring and architecture restructuring. This allows organisations to modernise incrementally so that major part of legacy applications can be fast-tracked for transition, development, testing, and production deployment.
The same platform also generates end-to-end test suites and test cases for the application, including edge case scenarios, to reduce risk and ensure functional equivalence between legacy and modernised systems.
Low-code platforms and rapid delivery
Even when the core systems are successfully modernised, enterprises often struggle to rebuild user interfaces, workflows, and integrations to deliver business value faster. A platform-agnostic approach to low-code and no-code delivery and the right platforms for each workload as against forcing a single-vendor solution can bridge the gap. This will enable teams to redesign user experience, automate workflows using AI agents, and stand-up multi cloud and cloud to on-prem integrations in a fraction of time taken in traditional development (see Figure 2).
Fig. 2
Equally important is how low-code platforms transform business and IT collaborations. Business users participate in the entire journey of an application—from discovery to design and configuration, while IT retains governance, security and architectural oversight. This model increases delivery throughput and ensures that modernised applications closely reflect the actual business requirements.
Technology convergence
The full value of enterprise modernisation is realised when such engines for AI-driven assessment, GenAI transformation, and low-code delivery—work as a single integrated pipeline rather than as independent workstreams as is currently pursued by different organisations with different vendors. Assessment insights directly align to transformation priorities. And transformation outputs feed directly into low-code delivery. The result is not a one-time migration project but a continuously adaptive modernisation engine.
A phased blueprint
A structured, return on investment (ROI)-focused approach ties business priorities to modernisation at every stage.
A highly disciplined, process-led approach to strategy, execution, and governance, and a focus on continuous improvement are essential for the success of any large-scale, AI-driven modernisation programme.
Here is a blueprint of how a business can align its priorities to the modernisation goals in four phases.
1. Discover and assess
The transformation starts with an AI-driven assessment of the overall application portfolio—evaluating business criticality, risk profile, and cloud readiness to identify and prioritise modernisation candidates. An AI engine then helps analyse individual application codebases, scanning source code, dependencies, and runtime behaviour to identify technical debt and transformation readiness, giving teams a fact-based foundation for planning.
2. Strategise and design
Organisations define a modernisation strategy aligned to business objectives—agility, cost optimisation, reliability, and customer experience. This phase establishes governance structures, key performance indicators (KPIs), and risk frameworks, and designs the integration of AI services, GenAI, cloud-native approaches, and low-code platforms within a scalable architecture.
3. Modernise and accelerate
AI models generate pathway recommendations informed by historical data, system use, application architecture and enterprise constraints. AI-driven automation can then accelerate refactoring, testing, upgrades and re-platforming while reducing human error. Low-code platforms handle frontend and workflow modernisation in parallel, compressing overall transformation timelines.
4. Operate, optimise, and scale
Post deployment, AI-enabled operations drive predictive monitoring, automated incident response, and continuous performance tuning. Operational insights feed back into the modernisation cycle, enabling ongoing application and process refinement. This model scales across business units and geographies, transforming modernisation from a project into a sustained enterprise capability.
A real-world example
End-to-end AI-ready transformation in a fraction of traditional timelines.
TCS was engaged to modernise an enterprise applications portfolio across multiple platforms for a major insurer, as part of one of the largest application modernisation programmes in the insurance sector. The existing applications were built on ageing technologies with little documentation, deeply embedded business logic, and significant technical debt – making traditional manual approaches impractical for the pace and scale of the modernisation.
Applying the four-phase blueprint, TCS deployed its proprietary AI-powered modernisation platform across the application portfolio, compressing legacy comprehension from weeks to days, reducing testing effort by 40-50%, and shortening delivery cycles by over 40% per application with measurable improvements in code quality, test coverage, and time to production. The modernised applications were delivered through a combination of low-code platforms and custom full-stack, cloud-native development, deployed across multi-cloud environments. This enabled the business to have quicker incident turnaround times by 30%, a leaner maintenance support team, and IT savings from better monitoring, lesser maintenance and lesser dependencies on legacy tech.
The way forward
The true measure of a modernised enterprise is not whether a transformation gets completed but whether the organisation can continuously adapt.
Sustaining the outcomes, including performance, reducing downtime, and maintaining service availability requires more than just technology. Organisations must invest in talent transformation, redesign operating models for closer collaboration between business and technology teams and foster a culture of continuous learning and adaptation.
Enterprise application modernisation will be defined by increasing levels of intelligence and autonomy, with modern full-stack architectures and multi-cloud deployment models providing the flexibility to scale across any environment. Platforms will serve as the connective tissue between applications, data, and ecosystems. Organisations that treat modernisation as a continuous, AI-powered capability—rather than a finite programme—will be best positioned to sustain competitive advantage over time.