CORE Media, in association with HPE Networking, brought together a select group of enterprise technology leaders for an exclusive closed-door session on ‘Building the future with AI-native self-driving networks’ in Mumbai and Bengaluru.
The session featured an engaging exchange of perspectives led by Anoop Mathur, Founder, CORE Media Group, in conversation with Sajan Paul, General Manager, HPE Networking India.
Opening the discussion, Sajan emphasized that as enterprises become increasingly AI-driven, the network can no longer remain a passive layer of connectivity. The key question for technology leaders, he noted, is whether their networks are intelligent enough to continuously adapt, optimize, and support AI-powered operations in real time. He invited the group to reflect on their AI-driven networking journeys, exploring the extent of adoption, the operational challenges they continue to face, and the capabilities required to build resilient, autonomous, and future-ready network environments.
A CIO from a leading renewable energy company highlighted that network reliability has become mission critical as manufacturing operations grow increasingly automated. At the organization's greenfield plants, shop floor logistics are managed through Automated Guided Vehicles (AGVs), making uninterrupted network performance directly linked to production output. As the company progresses toward highly automated, "dark factory" environments with minimal human intervention, it is re-evaluating its entire network architecture to improve reliability, resilience, and operational continuity, recognizing that every automated process depends on a robust and dependable network.
Expanding on the evolution of enterprise networking, Sajan highlighted that the ultimate objective is to build self-driving networks that can continuously learn, orchestrate, and optimize operations with minimal human intervention. He emphasized that intelligent network assurance is equally critical, requiring continuous monitoring across several operational parameters to proactively identify and resolve issues before they impact the business.
He also introduced the concept of an agentic mesh, where AI agents are not static but continuously learn from the behaviour of connected assets such as AGVs. These agents can identify normal and anomalous patterns, simulate AGV behaviour even during non-operational periods, and provide predictive insights that strengthen operational resilience. Complementing this capability is HPE's real-time wireless assurance solution, which delivers continuous visibility into network performance and helps enterprises maintain seamless operations in highly automated environments.
A CIO highlighted a common enterprise networking challenge where meeting rooms experience a sudden surge in device density during presentations and collaborative sessions, leading to noticeable drops in network throughput and user experience. Such unpredictable demand spikes continue to impact productivity despite robust network deployments.
Responding to the concern, Sajan explained that HPE's AI-powered AIOps capabilities are designed to address these dynamic environments by continuously monitoring network conditions, predicting congestion, and automatically optimizing performance in real time. By leveraging intelligent automation and proactive analytics, the platform helps maintain a consistent user experience even during periods of high network density.
The conversation turned to the mining sector, where a CIO emphasized that remote operations powered by AI-based excavation and automation technologies place unprecedented demands on enterprise networks. Delivering predictable performance while optimizing costs and maintaining a sustainable total cost of ownership remains one of the sector's most pressing infrastructure challenges.
From a technology solution standpoint, Sajan explained that HPE's AI-driven networking platform continuously tracks network efficacy and leverages just-in-time (JIT) packet capture for rapid diagnostics. When the system identifies issues such as poor channel quality or signal degradation, it can automatically trigger self-healing actions by switching to optimal channels. This helps enterprises maintain predictable network performance, reduce manual intervention, and optimize operations even in demanding remote environments.
