An exclusive conversation with Snigdha Gaddam on AI-native systems and software evolution
Introduction: When Software Starts Thinking for Itself
Imagine a world where your smartphone doesn’t just follow programmed instructions but actually learns from your behavior, adapting its performance in real-time without needing constant updates from developers. This isn’t science fiction anymore. Independent researcher Snigdha Gaddam has published groundbreaking work making this vision reality, demonstrating how artificial intelligence can be woven into the fabric of software systems rather than bolted on as an afterthought.
Think about the apps you use daily. Most operate on rigid rules: if you click this button, perform that action. But what if software could observe, learn, and improve itself? Gaddam’s research, recently published in the Journal of Information Systems Engineering and Management, introduces AI-native systems—software that doesn’t just use AI tools but thinks with them from the ground up. Her work achieved a remarkable 97% accuracy in predicting optimal system decisions, proving machines can make intelligent choices almost as reliably as humans.
From healthcare platforms adjusting to patient needs, to financial systems detecting fraud patterns, Gaddam’s vision transforms technology development. We sat down with her to understand how this breakthrough will reshape software development across industries.
The Conversation: Rethinking Software from the Ground Up
Q: Most of us use apps every day, but we don’t think about how they’re built. What’s the fundamental difference between traditional software and AI-native systems?
Think of traditional software like a recipe—you follow exact steps. AI-native systems are like experienced chefs who taste as they go, adjust seasonings based on available ingredients, and learn from past meals. These systems observe data, recognize patterns, and evolve their behavior without needing programmers to manually update every rule. We’re moving from rigid instruction-following to genuine learning and adaptation.
Q: Can you give us a real-world example of how this changes things for everyday users?
Consider a hospital monitoring system. Traditional software alerts nurses when heart rates exceed fixed thresholds. But an AI-native system learns each patient’s normal patterns, factoring in medications, activities, and time of day. Instead of generating false alarms overwhelming staff, it intelligently decides when something truly concerning is happening. My research showed that analyzing user behavior and performance metrics together enables systems to make these nuanced decisions with 97% accuracy.
Q: What did your research specifically demonstrate?
I developed a system using a Random Forest classifier—a method that creates multiple decision-making trees and combines their wisdom for smarter conclusions. Testing with variables representing user interactions and system performance revealed both factors are equally critical. The confusion matrix showed very few errors, proving AI-native systems can work reliably in practice, not just theory.
Q: For someone running a business or working in technology, how would adopting AI-native systems change their daily operations?
It fundamentally shifts the maintenance burden. Today, teams constantly push updates to fix issues or add features. With AI-native systems, software updates itself based on what it learns. Customer service chatbots learn from every conversation, improving understanding of nuance and intent. Inventory systems observe sales patterns, seasonal trends, and supplier reliability, optimizing automatically. This frees teams for innovation rather than constant firefighting. However, it introduces new responsibilities ensuring systems learn ethically and remain transparent in decision-making.
Q: What are the biggest challenges in making this vision a reality across industries?
The technical challenge is data drift—over time, real-world patterns change and AI models become less accurate without continuous retraining. We need systems that recognize when they’re outdated and self-update. The deeper challenge is organizational and ethical. We must ensure learning systems don’t perpetuate biases, that decisions can be explained and trusted, especially in healthcare or criminal justice. My research highlighted these gaps—we have the technology, but need stronger frameworks for responsible deployment.
Looking Ahead: A New Era of Intelligent Software
Snigdha Gaddam’s work represents more than incremental improvement in software engineering—it’s a fundamental reimagining of how we build digital systems. By demonstrating AI can be embedded at software’s core rather than layered on top, she’s opening doors to previously impossible applications. From manufacturing floors where machines coordinate autonomously to optimize production, to educational platforms adapting teaching methods in real-time, implications span every modern sector.
The 97% accuracy her research achieved proves we’re ready to trust machines with complex decisions. Yet as Gaddam emphasized, power comes with responsibility. The software engineering community must develop robust continuous learning practices, establish ethical guidelines, and ensure systems remain accountable to humans they serve.
As organizations grapple with digital transformation, Gaddam’s framework offers a roadmap. The future isn’t about writing better code—it’s building systems that learn, adapt, and evolve alongside us. For engineers, business leaders, and anyone depending on technology, understanding AI-native systems isn’t optional; it’s the essential next step in staying relevant.