Legacy systems are no longer just an old-technology problem. They sit at the center of a business decision: how can companies gain speed, improve customer experiences, and control infrastructure spend without exposing operations to unnecessary risk?
AI-assisted modernization is changing that calculation. By combining AI-assisted reverse engineering with forward engineering, businesses can reduce the time and complexity of transforming legacy systems while building a foundation for faster innovation. Bupa’s My Bupa mobile application offers a clear example of what that shift can deliver.
Bupa Turns Modernization Into Measurable Results
Bupa, a global healthcare organization serving 7 million customers in Asia-Pacific, modernized its My Bupa mobile application with AI-assisted methods. The result reached customers through two numbers that are easy to understand: the app rating climbed from 3.7 to 4.7, and the user-perceived crash rate fell by nearly 24 percentage points on Android.
iOS users saw the user-perceived crash rate fall by eight points. Those changes show how modernization can move beyond technical upgrades and improve the experience people have with a core digital service.
The transformation also moved at a different pace. Bupa delivered it in approximately 60% less time than in the pre-AI era by combining AI-assisted reverse engineering with forward engineering. Reverse engineering helped examine the existing system, while forward engineering supported the work of creating the modernized application. Together, these approaches reduced the effort needed to move from legacy technology to a stronger platform.
That time difference matters because customer expectations keep rising. People expect digital services to work smoothly, and they notice when older systems create friction. AI has also changed the economics of software development, making the case for modernization stronger when companies measure both customer outcomes and delivery speed.
The Risk of Waiting Keeps Growing
Modernization is not only a question of speed. It is also a question of exposure. Asifa Sherazi, CIO of health insurance at Bupa, captured the danger in one sentence: “The end-of-life technology is a risk that compounds quietly, and then arrives all at once.”
That risk can remain hidden while an old system continues to support business operations. Technology does not automatically become obsolete simply because it is older, and long-established systems can continue to contribute positively. The difficult decision comes when an aging platform no longer supports the speed, scalability, or customer experience the business needs.
A CIO may present a modernization proposal built around leaving the mainframe, gaining speed and scalability, and reducing infrastructure spend by 30% or more. Yet the board often still says no because of risk concerns. The financial case can be strong, but decision-makers must also trust the path from a familiar system to a modern platform.
This tension explains why modernization strategies now need both ambition and evidence. Bupa’s app rating, crash-rate improvements, and shorter delivery time create a practical picture of what an AI-assisted transformation can achieve. They connect modernization work to outcomes that business leaders and customers can see.
Modern Platforms Become AI Foundations
The next stage reaches beyond replacing old software. Modern platforms will become the base for far more intelligent AI-driven ecosystems, with AI integrated into design and operations. A company that modernizes its technology foundation can create room for AI to shape how products are designed and how systems operate.
Sanjeev Tripathi, senior VP, region head of BFSI, healthcare, and public sector at Infosys, described the economic shift this way: “The emergence of AI is fundamentally shifting the economics of modernization.”
That shift gives leaders a new reason to examine systems that once seemed too costly or risky to change. AI-assisted work can reduce the time and complexity of transformation, while modern platforms can support faster innovation after the initial project ends. The value is not limited to one application or one technology stack; it reaches into the business’s ability to respond.
Marco Santos, Global CEO of GFT, is also part of the wider modernization conversation, alongside Sherazi and Tripathi. The discussion spans customer expectations, development economics, infrastructure costs, and the risks that build inside aging technology.
These issues were in focus across discussions dated August 27, August 31, and September 1, 2026. Together, they point toward a clear decision for enterprise leaders: modernization cannot be judged only by the age of a system. It must be judged by the value the system creates, the risks it carries, and the speed the business needs next.
The strongest modernization plans will preserve what still works, address what creates risk, and use AI to shorten the path forward. Bupa’s results show the opportunity in concrete terms, while the wider strategy points to something bigger: legacy modernization can become the launchpad for faster, more intelligent business operations.
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