Red Hat: Purpose built AI models to take center stage in 2026

“In 2026, openness, flexibility, and collaboration will remain the principles that help organizations move from potential to real, measurable outcomes. With no single model suited to every enterprise context, open source will continue to underpin the freedom and innovation needed to build what comes next,” says Guna Chellappan, General Manager for Singapore at Red Hat.

A recent IDC study found that 70% of APAC organizations expect agentic AI to disrupt their business models within the next 18 months. But have businesses really understood what AI models can truly deliver to their operations?

According to Guna Chellappan, General Manager for Singapore at Red Hat, 2026 will be the year AI becomes practical with fit-for-purpose models taking center stage. He feels that enterprises have already seen the excitement of GenAI over last two years and now realizing that the future of AI is not in the models that attempt to do everything, but in specialized, right-sized, and explainable systems designed for specific industries and workflows.

“Business leaders will need to rethink their infrastructure strategies to support more diverse and demanding AI workloads. We will see growing interest in unified inference layers that can support a wide range of AI models without compromising performance and cost efficiency. At the same time, there is strong momentum around connecting enterprise application platforms with cloud-based AI accelerators, giving organizations a more seamless way to operationalize AI at scale. By pairing flexible platforms with specialized computing, enterprises can accelerate the shift from pilots to producing measurable business impact,” said Chellappan.

For Chellappan, AI is reshaping how enterprises think about infrastructure. This means traditional virtualization approaches, built for predictable and uniform workloads, are now being stretched by the needs of modern AI. The modern AI infrastructure demands higher performance, lower latency, and far more flexibility.

“In 2026, enterprises will increasingly adopt virtualization strategies that bring together virtual machines, containers, and specialized compute under a single operational model. This helps platform teams modernize at their own pace while supporting both existing applications and new AI-driven workloads. The result is an infrastructure foundation that is flexible enough to run traditional applications and intelligent systems side by side — without sacrificing governance or control,” explained Chellappan.

Hybrid cloud underlined by governance, becomes the default architecture for modern AI

Given the need for modern AI infrastructure, Chellappan believes as AI models increasingly rely on real-time data, distributed systems, and specialized computing resources, enterprises need architectures that allow them to run workloads as close to their data as possible, while still maintaining scalability and resilience.

“The demands of AI require the hybrid cloud. And in 2026, hybrid cloud will solidify its position as the standard operating model for intelligent enterprise systems. Organizations will prioritize platforms that help them maintain control over sensitive workloads on-premises, scale using public cloud capabilities, and bring intelligence closer to where data is generated at the edge,” he added.

Underlining the hybrid infrastructure will be the governance frameworks that will reshape the digital strategy. In APAC, governance is expected to become one of the most defining forces shaping digital strategy.

Chellappan pointed out that stronger governance frameworks will influence how AI is adopted across the region. Organizations want systems with greater security, transparency, and alignment with local regulations — and increasingly expect their technology platforms to support these requirements across hybrid and multi-cloud environments.

“In 2026, enterprises will increasingly prioritize AI systems that can be audited, monitored, and governed across hybrid environments, ensuring that decisions remain traceable and models behave as expected. This governance shift will also influence architectural choices, vendor selection, and skill priorities. Enterprises will seek open, trustworthy solutions that allow them to examine how models are built, how data is used, and how decisions are made. In regulated industries like financial services, these capabilities will become non-negotiable,” said Chellappan.

Skills, communities, and collaboration become the real accelerators

As the demand for cloud-native, AI, and cybersecurity talent continue to outpace supply across APAC, Chellappan pointed out that in 2026, the gap will only widen unless organizations invest in a skills-first approach to build, operate, and optimize modern digital systems.

“Open source communities will play a central role in this shift. They provide shared knowledge, transparency, and a global ecosystem rooted in collaboration. Tools and frameworks are also made available to everyone, instead of just a few. As more enterprises contribute back to these communities – by building on ideas quickly and responsibly – APAC will strengthen its position in digital innovation, not just as a consumer but increasingly as a creator,” he added.

For Chellappan, the right model, in the right environment, on the right architecture will define the next era of enterprise AI. Chellapan believes that the success of agentic AI will hinge not only on powerful models, but on the infrastructure, governance, and skills that support them.