Infor’s next-generation agentic architecture is built with industry-specific context, security, and auditability that generic AI doesn’t have
Infor, a leading enterprise provider of cloud software solutions specialized for how industries actually work, today 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, new proprietary research surveying over 2,000 business decision-makers across seven markets finds that most businesses believe generic AI tooling can’t deliver on their AI ambitions.
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 help businesses become agentic enterprises, 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, designed to reduce the errors and guesswork that come with generic AI, use tokens efficiently, and shorten the path from deployment to value.
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 are seeing 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 are seeing up to a 90% reduction in auditing costs tied to access management.
Enterprise AI Adoption Impact Index: What Businesses Are Telling Us
Infor is also releasing the second edition of the Infor Enterprise AI Adoption Impact Index, surveying business decision-makers across seven markets. 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:
- Finding 1: 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. This was the majority view across six of the seven markets surveyed where two in three (68%) businesses said off-the-shelf AI doesn’t adequately address their industry’s needs.
- Finding 2: Some regions are pulling ahead on AI deployment while some regions are lagging, even as leaders grow comfortable handing critical decisions to autonomous agents.
Comparing the same questions from the first iteration of the Enterprise AI Adoption Index in April 2026, the markets surveyed both times, a clear divide opened up. Businesses in the US are accelerating deployment and seeing more efficiency gains than they were a few months ago. Germany held steady on both measures, favoring methodical pilot expansion over a rush to full scale. The UK’s picture is mixed: efficiency gains fell back while deployment edged forward. Overall, 59% of businesses globally expect AI investment to increase over the next 12 months. Now more than half (54%) of global leaders are comfortable with autonomous agents fully executing critical business processes without human input at every step. Just 11% of leaders prefer humans to make high-stakes decisions without any AI input.
- Finding 3: Almost nobody has figured out who’s actually accountable for AI, and that’s a problem hiding in plain sight.
Even as adoption accelerates, most businesses still haven’t answered a basic question: who’s responsible when AI gets something wrong? Across every market surveyed, accountability is scattered rather than centralized: 23% point to the CEO or executive leadership, 22% to the CIO or CTO, 15% to an AI committee or governance group, and 10% to individual department heads. Fifteen percent say no one person has primary responsibility, or it’s unclear who does. Chief AI Officer roles are still the exception rather than the rule everywhere, sitting at just 10% globally. That vacuum shows up in what leaders say they need most: 33% cite data security, sovereignty, or compliance as their single greatest barrier to advancing their AI strategy.
- Finding 4: 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 69%, together forming a consistent pattern: the more variable and hands-on the operating environment, the less generic AI delivers.
Quotes
“Everyone has the same AI models now. What matters is what those models know about your business,” said Kevin Samuelson, CEO, Infor. “Customers keep telling us that general-purpose AI doesn’t get the details of their world, like how a food manufacturer traces a bad lot back through its suppliers, or how a distributor has to reprice when freight costs jump. Our agents have access to our deep industry context to deliver precise and valuable outcomes.”
“AI only matters when it creates real value,” said Alicia Thompson, CTO, Team Air Distributing. “Infor has kept pace with our ambitions, pairing Infor Industry AI Agents with Forward Deployed Engineers who understand our industry and work as an extension of our team. Together, we’re reducing manual work and turning operational challenges into practical improvements, building trust one process at a time.”
“The next phase of enterprise AI will be defined not by access to models, but by how effectively organizations apply AI within the context of their industry and business processes,” said Shashi Bellamkonda, Principal Research Director at Info-Tech Research Group. “Infor’s focus on industry-specific intelligence, interoperability, and embedded governance addresses several of the practical barriers organizations face as they move from AI experimentation to trusted, measurable outcomes.”
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