Infosys TechCompass

Modern apps

Trend 1: Cloud-native modern apps gain wider acceptance

Cloud-native apps drive digital transformations across enterprises. This fuels the growth of open-source technologies across all layers of the IT stack, including infrastructure, platform, middleware, application, data, and operations. Backed by the cloud, the concept of MODERN APPS microservices gained traction with containers and docker. Infosys Cloud Radar 2021 highlighted that companies that moved 60% of their workload to the cloud achieved significantly better performance than peers. It has become the foundation of the entire cloud-native architecture, with Kubernetes further pushing this trend. Modern API design and implementation are based on open-source protocols such as REST and gRPC. Enterprises are further exploring microgateways and other concepts powered by open-source.

An Australian retailer wanted to improve its customer connects across channels. It partnered with Infosys to modernize its mainframe systems and migrate workloads to the cloud. This was achieved using Infosys' accelerate-renew-translate framework for mainframe modernization. With the migration to the cloud and the modernization of native apps, the company achieved faster business growth and unlocked potential from its data.

Modern apps

Trend 2: AI/ML usage increases in modernization and app development

AI and ML enhance reliability, automation, and efficiency across data analytics, LCNC, quality assurance, and digital experience (speech, vision, gestures, etc). Infosys Modernization Radar 2022 highlighted AI and ML as the third most popular investment area by enterprises, with 96% of businesses leveraging these technologies for their modernization goals. Both technologies have already made their mark on avenues like churning massive volumes of customer data to identify opportunities. Now, they also help businesses accelerate backend applications and development processes. Developers can leverage this opportunity to identify patterns of issues from a complex set of code much earlier in the development process. Combining these patterns with open-source tools like GitHub can help developers use someone else's experience with similar issues to solve their problems. Further, open-source communities for ML have also become instrumental in automating coding tasks.

Subsidiary of a European financial services giant, worked with Infosys to improve its customer experience. The bank wanted to translate documents into data points and usable information. The bank modernized its traditional data management system using Infosys Mortgage Solutions, which provides business process automation for the mortgage industry. Built on open-source, the technologies and tools employ state-of-art computer vision and natural language processing. They also include data correlation, predictive analytics, and classification.

Modern apps

Trend 3: Phased approach proves least disruptive during modernization

Businesses typically modernize their applications using three approaches: big-bang, phased, and coexistent. Big-bang can be a cheaper option, but it can cause serious risks of rewriting legacy systems and disrupting ongoing services. This approach is more viable for small and easy-to-replace applications. For large-scale modernizations, phased or coexistent methods are better options, as they offer minimal disruption to existing services. However, enterprises need to shell out extra money for additional cloud storage and manage two processes parallelly for the coexistent method. Our Modernization Radar research highlights that the phased approach causes least disruption. It helps gradually migrate to new systems with substantially lower risks than big-bang.

A property and casualty insurance company wanted to modernize its legacy system with zero disruptions for 23,000 agents. The existing system had over 50,000 business rules and over 10 million lines of code. To meet service level agreements (SLAs), the company used microchange management to drive the project with customer-centricity. Legacy workloads were migrated to the cloud in a phased manner, which shortened the implementation cycle by ~30%. The project resulted in ~70% reduced ticket inventory and ~10% productivity improvement for maintenance teams. It also improved agent productivity by 20%.