Infor unveiled its Infor Industry AITM architecture and the next evolution of Infor Velocity Suite, which includes personalized adaptive user experiences, new governance models across customers’ entire enterprise, and more Industry AI agents.
This next phase of Infor's evolution responds to needs that generic ERP and AI cannot meet. Backed by the second edition of the Infor Enterprise AI Adoption Impact Index, proprietary research surveying more than 2,000 business decision-makers across seven markets, including 789 across Australia, Japan and Singapore, finds that most businesses believe generic AI tooling cannot deliver on their AI ambitions. In APJ, this challenge is playing out across a sharply divided landscape: Singapore and Australia are translating stronger AI foundations into wider deployment and greater efficiency, while Japan remains constrained by gaps in operational capability, data readiness, governance ownership and cost clarity.
Despite these differences, more than 1 in 2 APJ businesses say generic AI falls short of their industry’s needs. The finding points to a common post-adoption challenge: as businesses move from experimenting with AI to deploying it across core processes, generic tools struggle to accommodate specialised workflows, industry data and regulatory requirements. Infor Industry AI reinforces Infor's commitment to developing industry-specific solutions built for the specific complexities and operational realities of a defined set of industries. The platform architecture is built to help close the value void, the gap between what technology can promise and what companies achieve, and power every business to become an agentic enterprise, where people and agents work as one coordinated team.
As AI deployment accelerates, businesses need expert agents they can trust to act, and trust only scales when those agents are coordinated across the enterprise rather than operating as isolated point solutions. Infor’s Industry AI agents are built with industry-specific context already in place, reducing the errors and guesswork that come with generic AI, using tokens efficiently, and shortening the path from deployment to value.
"At Infor, we don't think our job ends when we hand a customer an AI tool — it starts there," said Kevin Samuelson, CEO, Infor. "Our research shows what customers have been telling us directly: generic AI doesn't understand what a factory floor needs, or what a distributor needs, or what a food and beverage manufacturer needs. Because our platforms are built industry by industry, our agents start with that context already in place, which means fewer errors, less guesswork, and a faster path to real outcomes for our customers. As a Koch company, we take the long view. This isn't a cost-cutting exercise for us — it's an investment in helping our customers do more, and we intend to prove it with outcomes, not promises."
“APJ is moving at very different speeds on AI, but every market still has foundations to strengthen. Governance cannot be deferred as AI assumes greater responsibility, and generic AI will not deliver the precision complex industries require. Bridging the region’s maturity gaps means addressing both from the outset so businesses can translate AI investment into greater impact, efficiency and measurable value,” said Geoff Thomas, Senior Vice President and General Manager, Asia Pacific and Japan, Infor.
Infor Industry AI is organized around four platform pillars:
- Precise Outcomes— An expanded suite of Industry AI agents with true micro-vertical AI expertise grounded in deep industry logic. Rather than reasoning from a generic, horizontal model, Infor's Industry AI agents draw on Industry CloudSuites, Industry Process Catalogs, and industry-specific domain language models built from decades of in-house expertise. Customers using this layer see shipments processed up to 60% faster. Paired with Industry AI Agents, the Infor GenAI Knowledge Hub provides customers with the depth of Infor’s application and industry knowledge to build custom AI Agents, now open to general availability.
- Open & Connected — An interoperable architecture that extends across the customer's full ecosystem, not just Infor. Infor's modular, open platform connects to non-Infor applications and existing orchestration and analytics tools, so customers are not required to standardize on a single vendor's stack. Agents coordinate as one system through Infor IQ, the semantic layer that gives every agent a consistent understanding of the customer's business, with a catalog of more than 350 value-driven use cases available out of the box.
- Easy to Use — Adaptive UX includes a personalized, AI-assembled experience that meets people in the tools they already work in. Infor's Adaptive UX pulls what a decision requires, such as the bill of materials, quoted price, and delivery date, into a single role-aware view instead of ten screens across multiple applications, so users review and act in one step. Customers can work through the Infor GenAI Assistant or through the AI assistants they have already adopted, with no requirement to standardize on one. Customers are seeing up to 90%-time savings across procurement, supply chain, manufacturing, and sales workflows.
- Governed—Enhanced Infor Governance, Risk, & Compliance capabilities bolster enterprise-grade security, governance, and auditability from the ground up. Human-approval workflows, agentic permission structures, and audit trails run throughout Infor's orchestration layer, enabling customers to have critical benefits like accountability and traceability natively built into the architecture. Every agent action runs through a governance, risk, and compliance layer built into the core of the Infor Industry Cloud Platform with explainable AI logic and verifiable, immutable logging of every action taken. Customers see up to a 90% reduction in auditing costs tied to access management.
Infor is also releasing the second edition of the Infor Enterprise AI Adoption Impact Index, surveying business decision-makers across seven markets, including Australia, Japan and Singapore. The data found that businesses’ investment in AI is outpacing efficiency gains, which Infor credits to the post-adoption gap often created by traditional AI and ERP solutions. Key findings include:
- APJ shows the sharpest disparity in AI confidence, deployment and efficiency
APJ is experiencing some of the widest differences in AI maturity of any region surveyed. Businesses in Singapore (92%) and Australia (85%) are highly confident in their ability to deploy AI without disrupting operations, compared with just 53% in Japan. That confidence gap reflects deeper structural differences: while most organisations in Singapore (87%) and Australia (82%) believe their data is ready for AI, only 38% of Japanese businesses agree. These stronger foundations are translating into wider deployment and greater efficiency. Singapore (43%) and Australia (42%) rank second and third among the seven markets surveyed for full-scale AI deployment, compared with 27% in Japan. They also report average efficiency gains of 36% and 34% respectively, both at or above the 34% global average, while Japan reports 29%. The disparity is becoming more consequential as AI moves from supporting work to executing it.
- Businesses keep hitting the same wall: generic AI doesn't speak their industry's language
When asking business leaders why their AI initiatives haven't delivered the way they hoped, a familiar frustration emerges again and again: the tool wasn't built for how their industry actually works. That's no longer a fringe complaint; it's the majority view across six of the seven markets surveyed where at least two in three businesses said off-the-shelf AI doesn't adequately address their industry's needs.
In APJ, the markets leading in AI adoption are more keenly impacted: 70% of businesses in Singapore (75%) and Australia (70%) indicate that off-the-shelf software fails their sector needs. In contrast, only 48% of Japanese enterprises state that off-the-shelf software fails their sector needs, the lowest net agreement globally. The results suggest that as adoption matures, businesses more clearly encounter the ceilings horizontal AI imposes on specialized workflows, regulatory requirements and industry data.
- Governance ownership varies sharply across APJ, but executive-level accountability remains uncommon everywhere
Even as adoption accelerates, businesses still face a fundamental question: who is accountable when AI gets something wrong? Most enterprises have assigned some responsibility for AI governance, risk or compliance, but dedicated executive leadership remains uncommon. Globally, only 10% have appointed a Chief AI Officer, and even in Singapore, the leading APJ market, just 13% have executive-level ownership of AI governance. Within APJ, Singapore and Australia are further along in establishing accountability structures, with only 4% and 6% of businesses respectively reporting no owner for AI governance, risk or compliance. In Japan, that figure rises to 21%, the highest of any market surveyed. These findings suggest APJ businesses are entering different stages of the AI maturity curve, but governance remains a universal priority. More mature markets must ensure accountability scales alongside deployment, while others still need to establish the foundations for broader adoption. In both cases, weaknesses in governance often become visible only after deployment accelerates and the cost of missteps rises.
- Manufacturers feel the industry-fit problem more sharply than almost anyone else
Pooled across all seven markets, 73% of manufacturing respondents said off-the-shelf AI doesn't fit their needs, a sign that the complexity of real production environments, from shop-floor processes to supply-chain nuance, is exactly where generic AI tends to fall short. Distribution felt the gap even more acutely, at 76%, while retail trailed at 67%, together forming a consistent pattern: the more variable and hands-on the operating environment, the less generic AI delivers.
