Learn best practices for modernizing your on-premises or IaaS solutions to intelligent apps in the cloud.
What We'll Cover:​
- Traditional vs Intelligent Apps
- On-Premises/IaaS vs. Cloud-Native Platforms
- Modernization strategy for building Intelligent Apps
Preparing the Path for Intelligent Apps: Transitioning from On-Premises/IaaS to Cloud-Native Applications​
The proliferation of generative AI is paving the way for intelligent applications, software that leverages AI capabilities to deliver unparalleled functionality and user experience. As we covered in a previous article, “Demystifying Intelligent Applications: Leveraging AI in App Development,” intelligent Apps aren’t just products of advanced coding. They interact, learn, and evolve thanks to modern AI and machine learning (ML) breakthroughs. 
The paradigm shift from traditional to intelligent apps demands that we change our approach to the technical challenges of software and architecture design. It also necessitates adopting new ways of thinking and operating within an organization, which we explore in greater depth in “Cultivating a Culture for Intelligent Apps: Organizational Readiness and Change Management.” 
This article explores the technical infrastructure powering intelligent apps, providing a roadmap to transition traditional on-premises or Infrastructure as a Service (IaaS) solutions to intelligent apps deployed to cloud native platforms and services. We’ll discover the differences between conventional and intelligent apps, options for re-architecting existing applications, and critical strategic considerations integral to modernizing the application-building approach. 
Understanding the Shift: Traditional vs. Intelligent Applications​
Intelligent apps are more than just an incremental evolution from traditional apps. They involve a fundamental shift in software solution design, development, and deployment. A clear understanding of how each paradigm differs is crucial for comprehending the impact of this transition. 
Functionality and User Experience​
Traditional applications, often rule-based and rigid, rely on preprogrammed operations. A traditional weather app, for example, typically presents structured, location-based forecasts using a predetermined data source. Meanwhile, an intelligent app leverages AI for data-driven decision-making and personalization. Rather than merely displaying the forecast, it integrates ML with additional data sources—like a user’s calendar and fitness data—to determine their favorite outdoor activities, provide relevant clothing suggestions, and even automatically design ideal vacations and times based on the user’s optimal weather preferences. 
Similarly, while traditional applications tend to offer a one-size-fits-all UI, intelligent apps use AI to create tailored interactions. For instance, an intelligent banking app might use generative AI to provide a voice-enabled UI that lets customers use natural language to ask about transactions and receive personalized financial advice.