SINGAPORE, Aug. 28, 2026 /PRNewswire/ — Chris Kelly, Senior Vice President of Data Center Solutions, Asia Pacific, Japan and Greater China (APJC), Dell Technologies, discusses why legacy infrastructure is no longer enough to support today’s hybrid cloud and AI-driven enterprise. As organizations face growing pressure to modernize while controlling costs, meeting governance requirements, and avoiding vendor lock-in, he explains how a disaggregated private cloud approach can help CIOs build a more agile, automated, and future-ready IT foundation.

For most CIOs, the question is no longer whether to adopt hybrid cloud. It is how to make it work as business demands keep shifting. The practical shift is not away from cloud, but toward workload-optimized placement across public cloud, private infrastructure, and the edge. The focus is on building an IT foundation that can absorb change without slowing the organization down.

AI adoption, changing cost models, sustainability pressures, and disruption in the virtualization market have turned infrastructure into a strategic lever, one that shapes how fast a business can respond, scale, and compete.

The Infrastructure Squeeze – Why Old Architectures Struggle to Keep Up

Enterprises now run a complex mix of traditional systems alongside AI, containerized apps, and distributed edge workloads. Architectures built for the older environment simply can’t keep up.

The gap is real. As of 2025, only 30% of organizations have proactively built modernization into their IT strategy, and less than one-third have a clear plan to retire older systems[1]. Leaving this unaddressed carries a cost. A recent IDC report found that excessive technical debt and the cost of keeping older apps running were cited as the second-biggest driver of digital infrastructure overspending in APJC, with 47.8% of organizations identifying this as a factor[1]. Rigid IT stacks, whether traditional three-tier or tightly coupled hyperconverged systems, struggle to adapt without triggering expensive, disruptive refresh cycles.

The answer lies in rethinking the architecture itself. Disaggregated infrastructure takes a different approach: rather than bundling compute, storage, and networking into fixed, tightly coupled systems, it separates these resources so each can scale independently, but combines the management of shared compute, networking, and storage resources with software-driven automation. It brings the flexibility of traditional three-tier architecture together with the operational simplicity of a unified, automated experience. That flexibility is exactly what modern enterprises need, and it raises an important question for CIOs deciding how to put it into practice.

Disaggregated Infrastructure: Practical Choices for CIOs

Put simply, disaggregated infrastructure lets compute, storage, and networking scale independently. CIOs are no longer locked in fixed ratios that force them to overbuy in one area just to get what they need in another. That has a meaningful impact on total cost of ownership by allowing organizations to refresh at the component level instead of replacing the whole stack.

Enterprises can then adapt as needs shift without the architectural and financial lock-in that has long held agility back. An open ecosystem supports multiple virtualization, cloud, and AI stacks, helping organizations preserve choice and reduce dependence on a single proprietary vendor.

Automation is what makes this manageable at scale. When lifecycle automation runs from initial design through daily operations, deployments become standardized, manual effort drops, and existing IT skills stretch further, a real advantage given the ongoing skills shortage facing infrastructure teams. The Dell Automation Platform provides the orchestration layer, using validated blueprints and lifecycle automation to make private-cloud deployment and ongoing operations more consistent. The result is a private cloud that moves with the business, letting CIOs modernize on their own terms without losing control or adding complexity.

How Omega Healthcare Modernized Its Global Service Infrastructure

Consider Omega Healthcare, which delivers enterprise solutions to leading U.S. hospitals and healthcare providers across 13 global delivery centers. Its IT estate had become fragmented across hyperconverged and standalone servers, making operations harder to manage and less adaptable. The team needed to simplify operations, gain control over virtualization licensing and vendor choices, and build a standardized, repeatable architecture it could deploy anywhere. 

Omega selected Dell Private Cloud, delivered through the Dell Automation Platform, as the foundation for a standardized, automated global environment. The platform provides orchestration, automation, and lifecycle management. Its disaggregated architecture, built on Dell PowerEdge servers and Dell PowerStore storage, lets Omega scale compute and storage independently while improving reliability, operational efficiency, and global continuity for 24/7 patient-care operations.

Private Cloud’s Role in the AI Journey

A common barrier in the AI journey is data readiness, including data proximity, quality, governance, and controls. Moving large data volumes to centralized systems is complex and costly, and it can slow down AI projects before they prove their value. Concerns like security, data privacy, and regulatory compliance add further pressure.

A strategic private cloud lets organizations deploy AI closer to where their data already lives: on-premises, at the edge, or connected to public cloud, depending on workload needs. That means faster insights, lower latency, stronger security, and better resource utilization, while supporting the governance requirements of sensitive data.

What makes this more urgent is where AI is heading. As AI moves from experimentation into production, infrastructure must support different requirements for training, inference, data movement, and governance across on-premises, cloud, and edge environments. Agentic AI adds even more complexity. Infrastructure must evolve quickly, not just run reliably. A private cloud built on disaggregated components offers a practical and flexible foundation for making this possible.

Why 94% of APJC Organizations Are Rethinking the Public Cloud

The region’s growth and appetite for innovation are significant, and so is the technical debt. Legacy systems across APJC were never designed for modern hybrid cloud. Today, 38.7% of APJC organizations are actively building a robust hybrid cloud environment[2].

This means organizations are not simply layering new technology onto old foundations. They are combining application modernization with stronger data governance and making more deliberate decisions about where each workload should run. Hybrid cloud integration ranks among the top cloud challenges, alongside integrating with legacy systems and managing multi-cloud environments.

This is fueling a clear cloud repatriation trend. IDC research found that 94%[3] of APJC organizations plan some form of cloud repatriation, with Southeast Asia at 97%[4] and India at 96%[5]. These moves are driven by the need for more controllable, flexible environments, with concerns around governance, compliance, cybersecurity, and performance. Cybersecurity is the top driver for repatriation in India and ANZ, while power and space availability leads in Greater China and Southeast Asia.

Three Must-Knows for CIOs Building Tomorrow’s Infrastructure

Start with architecture, not applications. A shift to disaggregated, open ecosystems helps CIOs avoid the overprovisioning trap and vendor lock-in that fuel technical debt. It lets businesses scale components independently, match resources precisely, and change workloads without constant overhauls.

Get data proximity right. Many AI ambitions run aground when data strategies overlook proximity, leading to stalled projects, poor data quality, weak controls, and rising costs. Getting proximity right can reduce those risks, strengthen security, and improve performance.

Finally, invest in automation as a strategic move, not just an efficiency play. When IT teams are freed from manual work, the private cloud stops being a cost center and becomes a platform for continuous innovation. Automation that spans the lifecycle, from design through operation, standardizes deployments, cuts manual effort, and helps teams focus on the work that matters most. That’s the mindset shift that separates infrastructure keeping pace with the business from infrastructure holding it back.

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SOURCE Dell Technologies

MERSEBURG, Germany, Aug. 28, 2026 /PRNewswire/ — CNTE has provided energy storage system solutions for BESS Leuna 1 and BESS Leuna 2, these energy storage projects located in Merseburg, Saxony-Anhalt, Germany.

Each project has a capacity of approximately 50MW/100MWh, representing a total deployment of 100MW/200MWh across the two sites. BESS Leuna 1 and BESS Leuna 2 were commissioned in March 2026.

The systems are primarily deployed for market-based price arbitrage, charging during periods of lower electricity prices and discharging when market prices are higher. By responding to electricity market price signals, the projects support flexible energy trading and more efficient utilization of energy storage assets.

As renewable energy deployment continues to grow in Germany, utility-scale battery storage is playing an increasingly important role in enhancing power system flexibility and supporting market-oriented energy operations.

CNTE previously provided an energy storage system solution for the 50MW/100MWh BESS Krumpa I project in Germany. BESS Leuna 1 and BESS Leuna 2 further expand CNTE’s utility-scale energy storage deployment and project experience in the German market.

View original content:https://www.prnewswire.co.uk/news-releases/cnte-provides-energy-storage-system-solutions-for-merseburg-bess-leuna-1-and-bess-leuna-2-in-germany-302862718.html

At Overchat AI, we analyzed anonymized usage data to answer a question: when given equal access to all models, which do people pick? The research below is based on our data from Jul 9, 2026 through Aug 23, 2026 that covers 1.14 million users, 1.28 million sessions, and 28.2 million AI interactions

TALLINN, Estonia, Aug. 28, 2026 /PRNewswire-PRWeb/ — Visual AI now accounts for 70% of deliberate model choices

Overchat AI users can switch between text, video, and image-generation models within a single AI session. Across the 28.2 million AI interactions analyzed during the period, Overchat AI found that when people deliberately switched to another model mid-session, they chose an image-generation model in 53.7% of cases and a video-generation model in 16.5% of cases. People chose another text model in 29.8% of cases. Together, media-generation models were chosen in an overwhelming 70.2% of cases.

Category – Share

Image – 53.7%

Video – 16.5%

Text – 29.8%

In 92% of cases, AI sessions began with a text model. However, people who did switch typically moved into media-generation workflows and, in 72% of cases, did not return to a text model before ending the session. This suggests that in a clear majority of these situations, people preferred to continue creating media rather than return to chatting or asking questions.

Most popular AI models

The table below summarizes the models people switched to most often. In other words, the sessions did not begin with these models; users selected them later in the conversation:

Model family – Share of deliberate choices

Seedream 5 Pro – 16.9%

Grok Imagine 1.5 – 15.9%

Nano Banana 2 – 12.6%

Kling V3 – 8.9%

Gemini 3.7 Flash – 8.2%

GPT-5.6 – 7.5%

Claude Opus 4.8 – 6.6%

Other models – 23.4%

It’s interesting to see that Seedream 5 Pro led deliberate model choices despite being a lesser-known model. Seedream is an advanced family of image-generation and editing models developed by ByteDance, known for precise image-editing capabilities, rich detail, and fast generation times. This could suggest that when people choose media models, they are less interested in who made the model and more focused on quality and user experience.

It’s also interesting that, among text-based models, Gemini overtook both GPT-5.6 and Claude Opus 4.8. Overchat AI observed a recurring pattern in which a chat would begin with GPT-5.6 before the user switched to Gemini. By comparison, users switched back to the GPT family less often.

For initial model choices, sessions most frequently began with Nano Banana 2 or GPT Image 2 for images and GPT-5.6 for text. This suggests that people initially gravitated toward familiar names but quickly moved on to explore other options—and often stayed with them.

Multimodal AI use is becoming mainstream

About 84% of active users worked across more than one modality during the research period, combining text with image or video generation rather than staying within a single workflow.

While 92% of AI sessions began with a text model, users who moved into media generation rarely returned to text during the same session. In 72% of those cases, the session ended in the image or video workflow.

Mobile users gravitate even more strongly toward visual AI

Mobile accounted for 76.4% of users and 75.3% of sessions during the period. Visual models accounted for 72.1% of deliberate choices on mobile, compared with 64.7% on desktop.

Device – Visual share of model choices

Mobile – 72.1%

Tablet – 69.4%

Desktop – 64.7%

Smartphones are becoming an AI creation environment, and image and video generation appear particularly well suited to mobile-first behavior.

The first model switch reveals the user’s actual task

Although 92% of sessions began with a text model, the first transition most often took users into image generation. Moving from one text model to another accounted for only 21% of first switches.

First transition – Share

Text to image – 54%

Text to video – 17%

Text to another text model – 21%

Media to another media model – 5%

Media to text – 3%

People often changed models more than once

Among sessions that involved more than one model, users switched models 1.8 times and interacted with 2.6 different models on average. Around 41% of multi-model sessions included at least two separate model changes.

Model changes – Share of multi-model sessions

1 – 59%

2 – 21%

3 – 10%

4 – 5%

5 – 3%

>5 – 2%

Most model switching happens early in the session

Overchat AI found that people usually changed models before becoming deeply invested in a conversation. In 58% of multi-model sessions, the first switch happened within the first third of the session. Only 14% of first switches occurred near the end.

First model switch – Share

First third of the session – 58%

Middle third – 28%

Final third – 14%

Most users finish with a different model than they started with

Only 15% of multi-model sessions ended with the same model that started the conversation. In 57% of cases, the session ended in a different AI modality altogether.

How the session ended – Share

In a different modality – 57%

With another model in the same modality – 28%

With the original model – 15%

Multimodal sessions are substantially deeper

Sessions became substantially longer when users combined different AI modalities. Text-only sessions averaged 14.2 interactions, compared with 25.7 interactions for sessions that combined text and image generation.

Session workflow – Average AI interactions

Text only – 14.2

Text and image – 25.7

Text and video – 31.8

Text, image and video – 42.4

The deepest sessions were those that included text, image, and video models, suggesting that people are using several models to develop, revise, and transform an idea within the same session.

Methodology

The research covers anonymized, aggregate activity on Overchat AI from Jul 9, 2026 through Aug 23, 2026. It is based on product behavior patterns. “Deliberate model choices” refers to occasions when users actively selected a different model after starting a session with another one. The multimodal-use, switch-frequency, transition, session-depth, and device figures were derived from aggregate session behavior. No individual accounts, prompts, uploads, or generated content were reviewed.

Key findings

Visual models accounted for 70.2% of deliberate model choices.About 84% of active users worked across more than one AI modality.While 92% of sessions began with text, 72% of sessions that moved into media generation ended there.Multi-model sessions included 1.8 model switches and 2.6 different models on average.Users averaged about 22 AI interactions per session.Seedream 5 Pro was the most popular AI model among deliberate switches.About 76% of users accessed AI on their phones.In 58% of multi-model sessions, the first switch happened within the first third of the session.Text-to-image transitions accounted for 54% of first model switches.Only 15% of multi-model sessions ended with the model that started the conversation.Sessions combining text, image, and video averaged 42.4 AI interactions, compared with 14.2 for text-only sessions.Visual models accounted for 72.1% of deliberate choices on mobile and 64.7% on desktop.

About Overchat AI

Overchat AI is an all-in-one AI app that gives users access to leading text, image, video and audio models from OpenAI, Anthropic, Google, xAI, ByteDance and other providers in one place. With one account, people can compare models, switch between them and use more than 150 purpose-built AI tools for writing, research, image creation, video generation and everyday tasks.

Learn more at overchat.ai.

Media Contact

Ekaterina Hohlova, Overchat AI, 372 53236240, ehohlova@overchat.ai, https://overchat.ai/

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SOURCE Overchat AI