Infor has unveiled its Industry AI architecture and the latest evolution of its Infor Velocity Suite, as new research highlights significant differences in artificial intelligence adoption across the Asia Pacific and Japan (APJ) region.
The announcement is based on 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 from Australia, Japan and Singapore. The research indicates that while AI investment is accelerating, organisations continue to face challenges around data readiness, governance and the limitations of generic AI tools.
According to the research, 64% of APJ businesses plan to increase AI investment over the next 12 months, compared with 59% globally. However, AI maturity varies considerably across markets.
Singapore and Australia lead the region in confidence around AI deployment, with 92% and 85% of businesses respectively confident they can implement AI without disrupting operations. The figure falls to 53% in Japan. Full-scale AI deployment stands at 43% in Singapore and 42% in Australia, compared with 27% in Japan.
Generic AI faces industry-specific limitations
One of the key findings is the growing concern that generic AI tools are not adequately suited to specialised business environments.
More than half of APJ businesses surveyed believe off-the-shelf AI does not sufficiently address their industry’s requirements. The concern is particularly strong in Singapore and Australia, where 75% and 70% of respondents respectively reported limitations with generic AI.
The issue is even more pronounced in manufacturing. Across the seven markets surveyed, 73% of manufacturing respondents said generic AI does not adequately meet their needs, while the figure rises to 76% in distribution and stands at 67% in retail.
Infor said its Industry AI architecture is designed to address these challenges by embedding industry-specific knowledge, workflows and context into AI agents rather than relying solely on generic models.
Kevin Samuelson, Chief Executive Officer, Infor, said enterprises need AI that understands the operational realities of individual industries and can translate that understanding into measurable business outcomes.
Geoff Thomas, Senior Vice President and General Manager, Asia Pacific and Japan, Infor, said differences in AI maturity across APJ highlight the need to strengthen both governance and industry-specific capabilities as organisations increase their reliance on AI.
Four pillars of Infor Industry AI
Infor’s Industry AI architecture is structured around four areas: Precise Outcomes, Open & Connected, Easy to Use, and Governed.
The architecture includes industry-specific AI agents built using Infor’s Industry CloudSuites, Industry Process Catalogs, and domain-specific language models. The company said customers have reported shipments being processed up to 60% faster using the technology.
The platform is also designed to connect with non-Infor applications and existing orchestration and analytics tools. Its Infor IQ semantic layer provides a common business context for AI agents, with more than 350 value-driven use cases available out of the box.
Infor’s Adaptive UX is intended to consolidate relevant information into role-specific views, while the company’s governance capabilities provide approval workflows, permissions and audit trails for AI-driven actions. Infor said customers have reported up to 90% time savings across selected procurement, supply chain, manufacturing and sales workflows and up to a 90% reduction in auditing costs associated with access management.
Governance remains a critical challenge
The research also points to significant differences in AI governance maturity across APJ.
While only 4% of businesses in Singapore and 6% in Australia reported having no designated owner for AI governance, risk or compliance, the figure rises to 21% in Japan. Globally, just 10% of organisations have appointed a Chief AI Officer.
Infor said the findings underline the need for enterprises to establish accountability, permissions and auditability as AI systems take on increasingly critical business processes.
The research found that more than half of business leaders globally are now comfortable with autonomous agents executing critical processes without human intervention at every stage, highlighting the importance of governance as enterprises move towards greater AI autonomy.
The findings suggest that APJ organisations are progressing at different speeds, but the transition from AI experimentation to enterprise-scale deployment is creating a common requirement: AI systems need to be closely aligned with industry processes, business context, and governance frameworks.