From Bengaluru to Seoul, businesses are converging on hybrid AI, writes Lenovo ISG’s Sumir Bhatia. Here’s why 2026 rewards frameworks over flashy demos.
What’s happening: Organisations across Asia Pacific are transitioning from AI experimentation to responsible, scalable execution in 2026.
Why this matters: AI has moved from proof-of-concept to production, creating an inflection point where successful organisations will be those treating AI as a trusted, human-centric system embedded into operations rather than a standalone initiative.
For organisations across Asia Pacific entering 2026, the AI conversation has shifted dramatically. The question is no longer whether to adopt AI but how to scale it responsibly, efficiently and with clear business outcomes.
Sumir Bhatia, President of Asia Pacific for Lenovo’s Infrastructure Solutions Group, observes a common thread in customer conversations spanning fast-growing digital natives to highly regulated banks and healthcare providers. “AI has moved from experimentation to execution,” he says. “As we look to 2026, the organisations that lead will be those that treat AI not as a single project or model, but as a trusted, human-centric system embedded into their operations.”
Trusted AI over hype
In 2025, many enterprises proved that AI can work. In 2026, the focus shifts to proving it can be trusted.
“Trusted AI starts with grounding models in secure, high-quality enterprise data and aligning them to clear business outcomes, not viral demos,” Bhatia explains. “It also means preserving what makes human judgment unique: context, empathy and accountability, instead of replacing it.”
Across APAC, leadership teams demand AI that can explain its recommendations, respect customer privacy and reflect local nuance. For example, a bank designing hyper-personalised engagement wants not only accurate insights but also the ability to trace why a certain offer was made to a specific customer segment.
“Trusted AI gives them that transparency whilst keeping the relationship human at the centre,” Bhatia says.
Hybrid becomes default
From Bengaluru to Seoul, customers are converging on a similar architectural answer: hybrid AI.
“Some workloads belong in the public cloud, majority at the edge or on-premise/data centre, often in the same workflow,” Bhatia observes. “This is driven by data sovereignty, latency needs, cost predictability and, increasingly, sustainability.”
In 2026, AI infrastructure will be more distributed than ever. Training might happen in a core data centre whilst real-time inference runs at the edge, bringing AI to the data where it’s generated.
“Enterprises are shifting from oversizing centralised environments to right-sized hybrid architectures and as-a-service models that allow them to scale up or down with demand,” he notes. “This flexibility is critical in APAC, where regulatory requirements, connectivity and growth profiles can vary significantly by market.”
Power shapes ambition
AI’s rapid growth brings a very real challenge: power. Many CIOs across the region view energy availability and efficiency as strategic constraints on their AI ambitions.
“Designing for sustainability is no longer only about corporate responsibility; it is a prerequisite for continued innovation,” Bhatia says.
Advanced cooling technologies, like warm-water systems, and denser, more efficient systems help customers achieve more AI performance per watt, per rack and per square foot. Simultaneously, moving inference closer to where data is generated reduces the need to move large volumes of information back and forth, lowering both latency and energy use.
“Organisations that embed sustainability into their AI roadmaps will be better positioned to scale, comply with emerging regulations and meet their own net-zero commitments,” he notes.
Responsible AI matters
Every AI conversation today eventually returns to trust. Boards and regulators want assurance that AI systems are fair, secure and accountable. Employees want to know how AI will change their roles. Customers want to feel that their data is protected and used appropriately.
“Responsible AI needs to be designed in from the start, not added as a final check,” Bhatia explains. “That includes clear governance frameworks, robust data protection, explainability and human oversight. It also means investing in skills and culture so that teams understand both the potential and the limits of AI.”
In APAC, where many markets are advancing their own regulatory approaches, a strong responsible AI foundation allows enterprises to adapt quickly whilst maintaining a consistent standard of ethics.
People drive innovation
Perhaps the most exciting shift for 2026 is how AI changes who can participate in innovation.
“Natural-language interfaces and agentic AI allow domain experts, doctors, plant managers, supply-chain leaders, to design and orchestrate AI-driven workflows without needing to be AI specialists,” Bhatia says. “When combined with secure, well-governed infrastructure, this unlocks rapid experimentation and faster time to value.”
The role of leadership then is to create the right conditions: modern, hybrid infrastructure, sustainable design, robust governance and an inclusive culture that empowers people to use AI confidently.
“In Asia Pacific, where diversity of markets and talent is a strength, this human-centric approach can be a powerful differentiator,” he observes.
As organisations enter 2026, AI is no longer a standalone initiative. It is becoming part of how businesses design products, run operations and serve communities.
“The leaders will be those who build AI that is trusted, hybrid by design, sustainable at scale and guided by clear principles of responsibility and ethics, always putting people at the centre of the transformation,” Bhatia says.
For Australian businesses navigating 2026’s AI landscape, the imperative is clear: move beyond experimentation to responsible execution, embrace hybrid architectures that balance performance with sustainability, and empower people across organisations to drive innovation with confidence. The organisations that master these fundamentals will lead the next phase of intelligent transformation.
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