How HPE Is redefining hybrid cloud for the AI-driven enterprise

As artificial intelligence reshapes enterprise IT, hybrid cloud has emerged as the foundational architecture enabling scalable, data-driven innovation. In this interview, Nilotpal Kumar Dutta, Industry Vertical Sales Leader at HPE India, explains how HPE’s GreenLake platform, strategic acquisitions, and AIOps-led approach are helping organisations simplify complexity, optimise costs, and prepare their infrastructure for large-scale AI adoption in a rapidly evolving hybrid world.

Looking at the current industry landscape, especially how the ICT industry is evolving with artificial intelligence, we have moved rapidly from discussions around agentic AI to actual enterprise deployment. With this pace of advancement, how is AI acting as a catalyst for the hybrid cloud ecosystem, particularly for enterprises?

If you look at any large enterprise and analyse how it is structured, you will see factories running applications, applications hosted on public cloud, applications in data centres, and applications running at dealer outlets or branch locations. Anything outside the core data centre effectively becomes the edge.

Applications are spread across the organisation. Today, if you want to run an AI use case, you will either run it at the edge, on the shop floor, or where the customer is interacting with the business for real-time analytics, or you will run it in the data centre on core enterprise data. In essence, AI use cases need to run across the organisation, and they need access to data that is distributed across the organisation.

If you want to run an AI use case in a specific area, you still need to leverage data residing across the enterprise. This requires a robust hybrid infrastructure.

You need to break down silos and integrate everything into a single platform. This makes a strong hybrid strategy essential. That is precisely what we are delivering. In an AI-driven world, we help eliminate silos, provide a unified view of data, and bring the entire ecosystem together.

Could you elaborate on HPE’s hybrid cloud portfolio and how it helps organisations simplify operations? From a business perspective, enterprises expect technology investments to drive revenue growth. How does your hybrid cloud strategy support that objective?

Let us extend the discussion from the earlier points. Today, workloads are distributed across multiple environments. This creates a fundamental challenge. When you need to deploy a new workload, where should it reside, on-premise or on public cloud? Once that decision is made, there is often a need to cross-leverage resources.

Can public cloud resources be used for workloads running on-premise? Can on-premise resources be used for workloads running on public cloud? In such a complex environment, management becomes critical. When an issue occurs, identifying its origin becomes difficult. Is the problem coming from an application running on public cloud, from the data centre, or from a colocation facility?

From a resiliency standpoint, backup strategies become essential. If there is a failure, do you have an alternate copy of the workload? If an application is running on public cloud, do you have an on-premise backup, and vice versa?

These challenges create three clear requirements: cross-environment resource utilisation, comprehensive manageability and orchestration, and resiliency. HPE addressed these challenges through a series of strategic acquisitions to build a single, unified platform.

We acquired Morpheus Data, a cloud management platform that enables orchestration and provisioning across public cloud and on-premise environments. This addressed workload provisioning challenges.

For observability and operations, we acquired OpsRamp. OpsRamp provides hybrid observability, helping customers identify where problems originate and correlate issues across environments. It includes built-in AIOps and is integrated into the HPE GreenLake Cloud platform.

To address the data challenge, we previously acquired MapR. Data is distributed across environments, and AI requires a unified view of that data. MapR enables the creation of a global data namespace, which is critical for AI initiatives. In 2020, HPE introduced HPE Ezmeral, a complete portfolio spanning AI and machine learning, data analytics, cost control, IT automation, AI-driven operations, and security.

For resiliency, we acquired Zerto. Zerto enables data protection and disaster recovery across public cloud and on-premise environments. It ensures that workloads running in one environment can be recovered in another.

These acquisitions were made sequentially and deliberately to solve specific customer problems in a hybrid world.

The next challenge is management. Earlier, when applications were hosted in a single data centre, management was relatively straightforward. Today, workloads are distributed across environments, and skills cannot be centralised at every location. This requires remote management through tools and processes.

HPE operates remote management centres in Bangalore and Pune, serving English-speaking markets globally. Through these centres, we manage hybrid environments remotely, centralising expertise while delivering consistent operations. The overall strategy has been to acquire the right capabilities, integrate them into a unified platform, and manage hybrid environments through centralised tools and processes.

Focusing specifically on the Indian market, which is highly cost-sensitive and has unique requirements, what impact is private cloud having in India, and how are you addressing these specific needs?

One of the primary reasons cloud adoption increased, whether private or public, is its ability to spread investment and manage unpredictability.

Indian organisations are growing, both organically and inorganically. Our private cloud offering allows customers to adopt a pay-as-you-consume model. There is no requirement for large upfront capital investment. Customers can build infrastructure based on their current needs and scale as they grow, paying only for what they consume.

From a cost perspective, this helps rationalise IT spending. A significant portion of our global delivery and support operations are based in India. As a multinational organisation with a large India-based workforce, we are able to pass this cost advantage on to our customers.

Which industries are showing the strongest interest in hybrid cloud or private cloud deployments, particularly HPE GreenLake and private cloud AI?

We have a large and diverse customer base in India using HPE GreenLake across multiple verticals. Manufacturing has been a strong adopter.

Manufacturing organisations are inherently hybrid, with shop floors, consumer interaction points, and distributed operations. This includes utilities, oil and gas, and related sectors.

We are also seeing strong adoption in banking. We have built large private cloud environments for banks. Pharma has shown rapid adoption due to the scale and nature of its operations.

Interestingly, we are also seeing strong adoption in the public sector, including both state and central government organisations, particularly where hybrid cloud is being used to deliver citizen-centric services.

You have spoken extensively about acquisitions. Is it fair to say that HPE’s growth strategy prioritises acquiring organisations that have already innovated, rather than building everything from scratch?

You can build solutions when you clearly understand where the market will be several years ahead. However, the hybrid and AI landscape is evolving very rapidly.

Take hybrid observability or workload orchestration between on-premise and public cloud. These needs have existed for some time. When a customer problem is identified, it needs to be solved quickly. If you undertake ground-up development, by the time the product is ready, the customer’s challenges may have evolved further.

The business imperative is to solve customer problems quickly by identifying organisations that already address those challenges, possess the right technical skills, and align culturally.

Our approach has been to identify companies that solve specific hybrid challenges, integrate them effectively, and build upon their capabilities. While ground-up development is possible, the pace of change makes acquisitions a more effective strategy.

With the acquisition of Juniper Networks, how does this enhance HPE’s full-stack portfolio, particularly for enterprises facing bottlenecks in AI development and training?

Prior to Juniper, HPE had a strong presence in wireless, basic switching, and SD-WAN, particularly through the Silver Peak acquisition, where we are market leaders.

However, AI-driven organisations require much more. They need robust security, routing, data centre switching, and end-to-end networking capabilities. With Juniper, we now offer a complete networking portfolio, from access points and switching to data centre switching and routing.

In a hybrid environment, observability is critical. Juniper brings over a decade of legacy data that has been used to build AI-driven operations. Our philosophy is now “network for AI and AI for network.”

The Juniper acquisition completes our networking portfolio and significantly strengthens our AIOps capabilities for network operations.

AIOps is often discussed less compared to agentic AI or regulations, yet operations are critical. How important is AIOps in reducing operational complexity?

AIOps is critical because it significantly reduces downtime. Problems that would traditionally take a long time to identify can be traced to their origin much faster.

AIOps enables root-cause analysis and, in many cases, self-healing through automated remediation. This improves uptime and reduces downtime. Reduced downtime directly impacts production time, which has a direct correlation with revenue.

Additionally, AIOps reduces the dependence on manual intervention and manpower.

As we move into 2026, what trends do you expect to see in the hybrid cloud ecosystem?

We will see rapid deployment of AI and a growing number of use cases. At the same time, organisations will face challenges because their existing IT architectures may not support their AI ambitions.

This will create a strong need for re-architecting hybrid infrastructure. Companies like HPE will play a key role in helping customers redesign their environments to support AI initiatives.

What is HPE’s approach to addressing this opportunity?

Our approach is to remain close to our customers. Every customer is different, and solutions cannot be generalised.

We focus on understanding each customer’s specific challenges. We then evaluate our portfolio, including cloud operations stacks and manageability tools, to design solutions that address those challenges in a contextualised manner.

Is there anything you would like to add?

Cost remains a significant concern for customers. Beyond pay-as-you-go models, we focus on reducing overall IT costs.

We have conducted detailed cost analyses for customers running workloads on public cloud and demonstrated double-digit cost savings through optimised hybrid models.

There are also rising costs associated with virtualisation. Through the Morpheus Data acquisition, we introduced HPE VM Essentials, our own virtualisation solution. It serves as an alternative hypervisor and offers significant cost reductions compared to traditional virtualisation stacks.