Healthcare organisations across Asia-Pacific continue to prioritise AI tools that improve operational efficiency and reduce administrative workload, as interest grows in more advanced clinical applications.This trend aligns with findings from an upcoming HIMSS report on AI adoption in APAC healthcare, which identifies workflow optimisation, medical documentation and administrative efficiency as the most common areas where organisations seek value from AI. The study, based on a survey of 200 healthcare professionals across APAC providers, is scheduled for release next month.From operational gains to clinical integrationFor IHH Healthcare, one of the region's largest private healthcare groups, AI tools focused on hospital operations are being adopted more quickly because they face fewer barriers and deliver clearer near-term returns."Operational AI scales faster because it comes with lower regulatory complexity, quicker deployment cycles and clearer returns," said Kwok Quek Sin, Group Chief Business Technology Officer at IHH Healthcare.IHH operates 190 healthcare facilities, including 89 hospitals, across 10 countries. According to Kwok, the organisation has deployed AI-enabled tools such as nurse rostering optimisation and revenue cycle automation across its network. These deployments have contributed to efficiency gains equivalent to up to 800,000 hours saved annually.Clinical AI, however, requires a much higher bar for validation, clinician trust and regulatory alignment, Kwok said."At the same time, clinical AI is increasingly embedded into medical devices and clinical products themselves," he said. "This is significant because it enables adoption to scale organically within the tools clinicians already use, rather than through standalone systems."Kwok added that IHH is also seeing practical impact from generative AI in areas such as ambient clinical documentation, AI-generated discharge summaries, and coding and claims review workflows.Governance as a scaling strategyThe findings also indicate that AI adoption is advancing faster than workforce readiness and governance structures in many healthcare organisations.Kwok argued that governance should not be viewed as a barrier to innovation, but as a prerequisite for scaling AI safely across healthcare systems."In practice, speed is not something you optimise directly; it is an outcome of trust," he said. "When clinicians and users trust the data, the models and the governance, adoption naturally accelerates."Kwok said IHH views governance as an "enabler of scale and trust," supported by a structured data and AI governance framework. The organisation formally registers and assesses AI use cases across the group to ensure governance remains proportionate to clinical risk: lightweight where appropriate, but rigorous for applications that affect patient care.He added that IHH embeds governance across the full AI lifecycle, from design and validation to deployment and continuous monitoring, while requiring clinical sponsorship, clear accountability and ongoing model revalidation as systems evolve.To maintain consistency across hospitals and markets, the organisation also anchors governance around shared platforms and standards, he said.Balancing local realities with regional scaleThe report also identifies fragmented AI deployment as a recurring challenge, with many organisations implementing tools in isolated departments rather than as coordinated enterprise systems.Kwok said one of IHH's biggest challenges is balancing standardisation across the group with the realities of operating in diverse healthcare systems and regulatory environments."We respect that healthcare is inherently local, but at the same time, we are building group-level capabilities in technology, data and AI to create consistency and scale," he said.According to Kwok, scaling AI across hospitals and markets increases pressure on data fragmentation, regulatory diversity and long-term operational sustainability, particularly as organisations manage model lifecycle, performance and governance over time.To address this, IHH has focused on building shared digital infrastructure rather than standalone AI applications."Rather than building isolated AI solutions, we invest in shared foundations, such as our Unified Data Platform and a common agentic AI layer," he said.Kwok emphasised that AI adoption succeeds when tools are integrated directly into clinical and operational workflows rather than deployed as standalone applications."It needs to be integrated directly into systems like the EMR or HIS at the point where decisions are made," he said. "The closer AI is to the workflow, the higher the adoption."Despite ongoing challenges, he said AI will become part of healthcare delivery once it is embedded into clinical and operational workflows rather than treated as a separate innovation initiative."AI becomes part of the operating fabric of healthcare delivery when it is integrated into clinician workflows, not as a separate tool, but within the systems and processes where clinicians and administrators work," he said.