Making the AI-powered case for legacy modernization

Legacy modernization has taken on new urgency as businesses increasingly rely on artificial intelligence (AI) to reshape their technological strategies. A recent case study involving Bupa, a leading health insurer, illustrates how AI-driven approaches are transforming the traditional challenges of upgrading outdated systems. By leveraging AI tools during its modernization efforts, Bupa successfully upgraded its My Bupa mobile application from the Xamarin framework to native Swift and Kotlin, significantly improving performance and user satisfaction. The transition was driven by the growing recognition that legacy systems pose not only technical risks but also strategic limitations. According to Asifa Sherazi, Bupa’s Chief Information Officer for health insurance, the gradual obsolescence of end-of-life technologies can lead to sudden operational disruptions. She emphasized that treating modernization as a business transformation, rather than merely a technical overhaul, can yield substantial benefits. Following the upgrade, Bupa saw a marked improvement in app ratings, with the score rising from 3.7 to 4.7. User-reported crash rates also dropped dramatically, decreasing by nearly 24 percentage points on Android and eight points on iOS. These improvements underscored the tangible impact of modernizing infrastructure to meet evolving customer demands. Sanjeev Tripathi, senior vice president and region head of BFSI, healthcare, and public sector for Australia, New Zealand, and Southeast Asia at Infosys, added that AI is reshaping the economic landscape of modernization projects. He noted that AI-assisted methodologies are drastically reducing the time, effort, and risk typically associated with such initiatives. In Bupa’s case, integrating AI-enabled reverse engineering with forward engineering allowed the team to complete the transformation in roughly 60% less time compared to pre-AI methods. This efficiency gain highlights how AI is becoming an essential tool in overcoming the complexities of legacy system upgrades. Beyond technical gains, both Sherazi and Tripathi highlighted the importance of maintaining institutional knowledge and fostering adaptive work environments. Modernization, they argued, should not only focus on replacing old systems but also on empowering teams to identify and address emerging issues proactively. This approach ensures that the long-term value of modernization extends beyond immediate performance improvements to include enhanced agility and resilience within the organization. Looking ahead, both experts envision modernized platforms as foundational elements for developing more intelligent, AI-integrated ecosystems. Tripathi described a future where AI is deeply embedded in every stage of system design and operation, enabling highly personalized and predictive customer experiences. He suggested that such platforms will serve as the backbone for next-generation services, allowing organizations to respond dynamically to market shifts and consumer preferences. For Sherazi, this evolution means rethinking the types of questions organizations should prioritize. Instead of focusing solely on whether a platform can support a feature, she emphasized the need to evaluate whether that feature aligns with customer-centric goals. The broader implications of Bupa’s success extend beyond the company itself. It represents a growing trend where AI is not just enhancing existing processes but fundamentally altering the way businesses approach modernization. As more organizations recognize the strategic advantages of integrating AI into their digital transformation journeys, the role of legacy systems is likely to diminish further. With modernization viewed through the lens of innovation and adaptability, the path forward appears increasingly clear, and more promising.