CORE Media, in association with Hewlett Packard Enterprise (HPE), hosted an exclusive closed-door discussion on ‘Unifying the Hybrid IT Landscape with Intelligent AI-Driven Operations’ on July 24, 2026, in Mumbai.
The session was moderated by Anoop Mathur, Founder, CORE Media, and featured insights from Mayank Chaturvedi, Director – Hybrid Cloud Solutions, and Soni Bhagwan, Digital Advisor – Hybrid Cloud from Hewlett Packard Enterprise.
The discussion opened with a common theme — AI has become one of the most disruptive technologies enterprises have encountered, but scaling it remains far more complex than adopting it. CIOs agreed that the biggest hurdles today are no longer limited to AI models, but extend to infrastructure readiness, fragmented data, governance, costs and proving business value.
A recurring concern across industries was the challenge of managing distributed data. As enterprises operate across hybrid cloud environments, edge locations and legacy systems, valuable information remains scattered across multiple repositories, making it difficult for AI to access a unified source of truth.
Addressing this challenge, Soni observed that while data is distributed, intelligence should not be. He explained that enterprises need an intelligent data fabric capable of bringing AI closer to enterprise data rather than continuously moving large volumes of data into AI platforms. Such an approach not only improves accessibility but also helps optimize costs while creating a universal catalogue that enables AI agents to discover, observe and reason across enterprise information.
Technology leaders from the financial services sector acknowledged that while AI is a strategic priority, many organizations are still identifying high-value business use cases before making significant infrastructure investments. GPU adoption continues to be evaluated carefully, with enterprises seeking stronger business justification before scaling deployments.
Participants also highlighted opportunities to leverage AI for predictive infrastructure management—detecting component failures before they occur, automating incident responses and improving operational resilience across enterprise environments.
Across sectors, CIOs identified cost optimization as one of the defining challenges of enterprise AI. AI workloads require significant investments in compute, storage and networking, making financial governance increasingly important as organizations expand their AI initiatives.
Responding to this concern, Mayank explained that organizations are focusing on optimizing GPU utilization while developing multilingual AI platforms and cloud-native operating models that reduce unnecessary infrastructure dependencies. He also highlighted the importance of measuring AI consumption across compute, storage and network resources, enabling enterprises to establish transparent cost models while maximizing infrastructure efficiency.
Participants widely agreed that hybrid cloud continues to offer the right balance between flexibility, performance and investment protection. Many organizations are seeking to modernize without abandoning existing infrastructure, particularly as AI workloads introduce new demands on enterprise data centres.
The discussion emphasized that hybrid architectures provide enterprises with the ability to scale AI while preserving earlier investments in servers, storage and networking infrastructure.
Manufacturing leaders shared that their focus is shifting towards building centralized AI ecosystems capable of supporting multiple factories through a common platform. Standardizing AI models, consolidating operational data and understanding the cost implications of scaling AI across plants remain key priorities as organizations progress from isolated pilots to enterprise-wide deployment.
Representatives from the textile sector noted that while AI presents significant opportunities to improve manufacturing operations, many organizations remain cautious. Security, governance and trust continue to influence adoption decisions, with enterprises preferring to observe the maturity of AI technologies before integrating them into core production environments.
Healthcare leaders highlighted the enormous challenge of managing vast volumes of structured and unstructured clinical data. While cloud adoption has accelerated digital transformation, understanding cloud economics remains an ongoing learning process for many organizations.
Participants also pointed out that public large language models may not always be suitable for enterprise workloads involving sensitive information. Instead, organizations are increasingly exploring smaller, domain-specific language models trained on proprietary enterprise knowledge to improve accuracy, governance and operational efficiency.
Throughout the discussion, Soni stressed the need for enterprises to rethink how AI interacts with data. Rather than moving data to AI, organizations should enable AI to securely access enterprise data through intelligent fabrics capable of governance, observability and interoperability. He also highlighted innovations such as HPE GreenLake Intelligence, designed to provide greater visibility, control and intelligence across distributed enterprise environments.
Complementing this perspective, Mayank emphasized that successful AI adoption requires more than infrastructure investment. Enterprises need robust consumption models, optimized GPU utilization, data readiness and operational visibility to ensure AI delivers measurable business outcomes without escalating costs.
