Making the AI-powered case for legacy modernization
For years, legacy technology has been a problem companies knew they needed to solve, but one they often struggled to tackle. The cost, complexity, and risk of replacing business-critical systems could make modernization feel like a disruption to manage instead of an opportunity to pursue.
Legacy technology refers to outdated software or systems that a company still relies on for critical operations. These systems often lack vendor support, meaning the company that created them no longer provides updates, security patches, or fixes for bugs. For Bupa, a global healthcare organization serving seven million customers in Asia-Pacific, their mobile app My Bupa was built using Xamarin, a framework whose support ended in 2024. While Bupa extended support temporarily, they recognized this was a short-term solution. The risks of relying on end-of-life technology include security vulnerabilities, as no new updates are released to protect against cyber threats, and a shrinking talent pool of experts who can maintain the system. Additionally, as operating systems like iOS and Android evolve, legacy apps struggle to meet new requirements, leading to a gap between customer expectations and the app’s capabilities. Bupa’s CIO, Asifa Sherazi, described this as a 'risk that compounds quietly, and then arrives all at once,' emphasizing that ignoring modernization can lead to sudden, severe problems.
Modernization involves updating legacy systems to newer, more efficient technologies. For Bupa, this meant migrating My Bupa from Xamarin to native platforms Swift (for iOS) and Kotlin (for Android). The benefits were significant: the app’s rating improved from 3.7 to 4.7 out of 5, and user-perceived crash rates dropped by 24 percentage points on Android and 8 points on iOS. Login success rates doubled to 77% on Android, reducing failed attempts where users couldn’t access their coverage. The team achieved 100% feature parity in a single release, meaning all existing functions were preserved, and 90% of active customers adopted the new version within a short period. These improvements translated to a more reliable and user-friendly experience, especially during critical moments when customers needed access to their health insurance. Sherazi noted that customers ‘will never think about any of this as a technology change, and they shouldn’t have to either.’
Artificial intelligence (AI) is changing how companies modernize legacy systems by reducing the effort, risk, and time required. Sanjeev Tripathi, an executive at Infosys, explained that AI shifts the economics of modernization by automating complex tasks. Infosys used AI to assist in two key phases: reverse engineering and forward engineering. Reverse engineering involves analyzing legacy code to extract business rules, processes, and logic, preserving critical functionality while avoiding risks of large-scale migrations. Forward engineering then re-architects the solution to improve customer experience and scalability. For Bupa, this approach delivered the modernization project in approximately 60% less time than would have been possible without AI. Tripathi emphasized that AI tools now make modernization more feasible, turning it from a costly disruption into a strategic opportunity. The time to act is now, as customer expectations and technological advancements continue to accelerate.
Modernizing technology is not just about systems but also about people. Bupa faced three human challenges during their modernization. First, the scarcity of expertise: Xamarin’s declining popularity meant few specialists could maintain the app, and much of the operational knowledge existed only in individual experience. Second, capacity constraints: business analysts were already fully committed to ongoing feature delivery, leaving little time for learning new technologies. Third, knowledge dependency: critical insights were held by a small group, creating a risk if those individuals left. To address these, Infosys used AI-assisted reverse engineering to map nearly 1,500 regression scenarios to native platforms, preserving business logic by design rather than relying on memory. This approach distributed knowledge across the team, freeing employees to focus on innovation rather than holding institutional memory. Sherazi highlighted that the goal was to give teams ‘the confidence, the capability, the clarity to embrace a different future,’ ensuring the change succeeded beyond just the technology.
Modernized platforms are not just about replacing old systems but building flexibility for future innovations. Tripathi explained that modern platforms will serve as foundations for *AI-driven ecosystems*, where AI is integrated into design and operations, enabling more personalized and predictive customer experiences. For Bupa, this shift changed how they approach decisions: instead of asking if their platform can support a new feature, they now ask if it is the right thing to do for their customers. Sherazi noted that the modernization allowed Bupa to move beyond transactional interactions, such as managing insurance coverage, to more proactive and personalized care through complementary services like *Blua*, a digital health platform. The long-term goal is to create systems that are scalable, maintainable, and capable of supporting future advancements without requiring another costly overhaul. This positions companies like Bupa to adapt quickly to evolving customer needs and technological changes.
