The Future of AI in Predictive Analytics: A Conversation with Josson Paul Kalapparambath

For decades, enterprise networking has been a reactive process—teams would troubleshoot slowdowns, diagnose failures, and address security breaches only after they occurred. But with AI-driven predictive analytics, that model is changing. Instead of waiting for failures, networks are now anticipating them, preventing outages, reducing downtime, and optimizing performance in real time.

At the forefront of this transformation is Josson Paul Kalapparambath, a Senior IEEE Member, award-winning leading expert with over 20 years in AI-driven networking solutions. His work at Cisco DNA Center’s Network Data Platform and Assurance has been instrumental in developing predictive analytics models that allow businesses to forecast network failures before they happen, ensuring smooth and uninterrupted operations.

We sat down with Josson to explore how predictive analytics is reshaping enterprise networking, its benefits, and the challenges organizations must navigate in this AI-powered future.

Hi Josson, thanks for joining us today. How is AI-driven predictive analytics changing enterprise networking?

Hi, good to be here. Good question. I think Predictive analytics is revolutionizing network management by shifting from reactive troubleshooting to proactive issue prevention. Traditionally, IT teams waited for network issues to arise, whether it was sudden bandwidth drops, hardware failures, or security breaches. With AI, we can now anticipate these issues before they happen and take corrective action in real time.

For example, at Cisco DNA Center, our AI-driven predictive models continuously analyze millions of data points to detect anomalies and performance bottlenecks. If the system notices a gradual increase in latency on a critical switch, it doesn’t wait for a failure—it alerts IT teams or even suggests optimizations automatically.

This means fewer outages, better efficiency, and more secure networks. AI is not just enhancing performance; it is redefining how networks function, making them self-diagnosing and self-optimizing. The move toward predictive analytics is one of the biggest shifts in networking in years.

What are the biggest benefits of predictive analytics in networking?

I think the adoption of AI-powered predictive analytics is delivering major advantages for businesses, especially those operating large-scale enterprise networks are minimized downtime & improved reliability, so AI detects performance issues before they escalate, reducing unexpected outages. Second would be optimized resource allocation which is when AI ensures bandwidth, computing power, and network resources are dynamically adjusted based on demand, preventing inefficiencies. The third is cost savings where predicting failures before they happen means fewer emergency repairs and less manual intervention, reducing overall operational costs.

For companies running mission-critical operations, AI-driven predictive analytics can reduce unplanned network downtime by up to 40%, significantly improving business continuity and performance.

How does Cisco DNA Center leverage AI for predictive analytics?

At Cisco DNA Center, we’ve built an AI-powered predictive analytics engine that monitors network behavior in real time. The system can detect emerging issues, unusual traffic patterns, and hardware performance degradations long before they disrupt operations.

The process begins with AI-driven anomaly detection. Our machine learning models analyze historical network behavior, learning what normal traffic and performance look like. When something deviates—whether it’s an unexpected spike in latency or an increase in packet loss—the system flags the anomaly and suggests corrective measures.

For example, if the system detects gradual performance degradation on a critical router, it can recommend preemptive maintenance or reroute network traffic to balance loads dynamically. This ensures that businesses are always running at peak efficiency without waiting for something to break first.

We’re moving toward a world where networks can self-diagnose, self-optimize, and even self-heal, reducing IT workload while improving overall performance.

What challenges do enterprises face when adopting predictive analytics in networking?

While AI-driven predictive analytics offers game-changing benefits, enterprises still face significant hurdles in adoption. The most pressing challenges include data Privacy & Compliance where AI models need access to vast datasets to make accurate predictions, but enterprises must also navigate strict data privacy regulations like GDPR and HIPAA and Integration with Legacy Systems where many businesses still rely on older networking infrastructure, making it difficult to seamlessly integrate AI-driven predictive models without major overhauls and AI Transparency & Explainability – Predictive AI models make real-time decisions, but businesses need to trust these insights before automating mission-critical functions.

The transition to AI-driven networking isn’t just about adopting new technology—it requires a strategic shift in how IT teams manage infrastructure. Companies that successfully overcome these hurdles will gain a major competitive edge.

What excites you most about the future of AI in networking?

The most exciting part of AI-driven networking is the move toward fully autonomous, self-healing networks. We are already seeing AI-powered network slicing, real-time compliance enforcement, and zero-trust security models emerge as key trends.

In the near future, I believe that networks will be fully adaptive, meaning they will dynamically adjust performance based on workload fluctuations, and automatically detect and neutralize cyber threats in real time, and also self-optimize to reduce latency, improve bandwidth efficiency, and lower operational costs.”

With leaders like Josson, who have published articles as an author on DZone, writing about and pioneering predictive analytics in networking, it can be stated that businesses are entering a new age of intelligent, secure, and self-optimizing networks—where AI ensures seamless connectivity, robust security, and operational efficiency at an unprecedented scale.