AI reduces time and risk of modernizing legacy business systems
Bupa's mobile app overhaul, aided by AI, cut project time by about 60% and improved user ratings from 3.7 to 4.7 while reducing crashes. The case shows how AI-assisted reverse and forward engineering can lower the cost and risk of replacing legacy systems. Experts argue that waiting for systems to fail is riskier than modernizing with AI support.
Bupa's migration of its My Bupa application from Xamarin to native Swift and Kotlin produced measurable gains: user ratings climbed from 3.7 to 4.7, and perceived crash rates dropped by nearly 24 percentage points on Android and eight points on iOS. The project combined AI-assisted reverse engineering with forward engineering, cutting delivery time by roughly 60% compared with pre-AI approaches.
The case illustrates a broader shift in modernization economics. Asifa Sherazi, Bupa's CIO of health insurance, warns that end-of-life technology risk "compounds quietly, and then arrives all at once." Infosys's Sanjeev Tripathi notes that AI is fundamentally altering the cost-benefit balance of legacy replacement, with modern platforms serving as foundations for predictive, AI-driven customer experiences.
This modernization approach could reshape how organizations handle aging infrastructure across healthcare, finance, and public services. If AI-assisted migration reduces cost and risk as demonstrated, more companies may pursue proactive upgrades rather than waiting for system failures, potentially improving reliability and user experience for millions of customers. However, outcomes may vary by organization, and the long-term durability of AI-assisted engineering remains unproven at scale.