Singapore, October 7, 2026 — More than one in two businesses across Asia Pacific and Japan (APJ) say generic artificial intelligence (AI) tools fail to adequately address their industry’s specific needs, highlighting a growing challenge as companies move from AI experimentation to wider deployment. The finding comes from the second edition of the Infor Enterprise AI Adoption Impact Index, which surveyed more than 2,000 business decision-makers across seven markets, including 789 respondents in Australia, Japan and Singapore.
The research shows a sharply divided AI adoption landscape across APJ. Singapore and Australia are translating stronger AI foundations into wider deployment and efficiency gains, while Japan continues to face gaps in operational capability, data readiness and governance.
Some 92% of Singaporean and 85% of Australian enterprises are confident in their ability to manage AI rollouts without disrupting daily operations, compared with just 53% of Japanese businesses. Singapore and Australia also rank second and third among the seven markets surveyed for full-scale AI deployment, at 43% and 42%, respectively. Japan trails at 27%, below the 34% global average.
Data readiness shows a similar divide. While 87% of Singaporean and 82% of Australian businesses say their data is mature enough to support reliable AI, only 38% of Japanese businesses agree, compared with 74% globally. Governance ownership also varies considerably. Only 4% of Singaporean and 6% of Australian businesses report having no owner for AI governance, risk or compliance, compared with 21% in Japan.
Globally, only 10% of businesses have appointed a Chief AI Officer, while executive-level ownership of AI governance in Singapore stands at 13%. However, the research suggests that greater AI maturity may also make businesses more aware of the limitations of generic tools.
Some 75% of Singaporean and 70% of Australian businesses say off-the-shelf AI does not adequately address their industry’s specific needs, compared with 48% in Japan. Across six of the seven markets surveyed, at least two in three businesses share the same view. The issue is particularly pronounced in industries with complex operating environments. Across the seven markets, 73% of manufacturing respondents say off-the-shelf AI does not fit their needs, compared with 76% in distribution and 67% in retail.
Despite the challenges, AI investment remains strong. Some 64% of APJ businesses plan to increase AI investment over the next 12 months, the highest proportion of any region surveyed and above the global average of 59%. The findings come as enterprises increasingly move toward AI systems capable of executing business processes rather than simply supporting individual tasks. Globally, more than half of business leaders are now comfortable with autonomous agents fully executing critical business processes without human input at every step.
Against this backdrop, enterprise software provider Infor has unveiled its Infor Industry AI architecture and the latest evolution of its Infor Velocity Suite. The company says the architecture is designed around industry-specific context, interoperability, adaptive user experiences and embedded governance, with the aim of addressing challenges that generic AI tools can face when applied to specialized workflows and industry requirements.
Infor’s architecture is organized around four areas: industry-specific AI agents designed to deliver precise outcomes; an open and connected platform that works across enterprise systems; adaptive user experiences; and governance, risk and compliance capabilities built into the architecture. “APJ is moving at very different speeds on AI, but every market still has foundations to strengthen,” said Geoff Thomas, Senior Vice President and General Manager, Asia Pacific and Japan, Infor.
“Governance cannot be deferred as AI assumes greater responsibility, and generic AI will not deliver the precision complex industries require,” he added. The research points to a broader shift in enterprise AI adoption: as businesses move beyond experimentation, the challenge is increasingly about applying AI within the context of specific industries, processes and governance requirements and turning investment into measurable operational value.
