Infor Unveils Its Expanded Infor Industry AI™ as New Global Research Finds Off-the-Shelf AI Falls Short for Two Out of Three Businesses

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Infor

Oct 06, 2026, 09:15 ET

Infor's next-generation agentic architecture is built with industry-specific context, security, and auditability that generic AI doesn't have

ORLANDO, Fla., Oct. 6, 2026 /CNW/ -- Infor, a leading enterprise provider of cloud software solutions specialized for how industries actually work, today unveiled its Infor Industry AI™ 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.

Experience the full interactive Multichannel News Release here: https://www.multivu.com/infor/9413251-en-infor-unveils-expanded-industry-ai-new-research

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Infor Unveils Its Expanded Infor Industry AITM as New Global Research Finds Off-the-Shelf AI Falls Short for Two Out of Three Businesses
Infor Unveils Its Expanded Infor Industry AITM as New Global Research Finds Off-the-Shelf AI Falls Short for Two Out of Three Businesses
Infor Unveils Its Expanded Infor Industry AITM as New Global Research Finds Off-the-Shelf AI Falls Short for Two Out of Three Businesses
Infor Unveils Its Expanded Infor Industry AITM as New Global Research Finds Off-the-Shelf AI Falls Short for Two Out of Three Businesses
Infor Unveils Its Expanded Infor Industry AITM as New Global Research Finds Off-the-Shelf AI Falls Short for Two Out of Three Businesses
Infor Unveils Its Expanded Infor Industry AITM as New Global Research Finds Off-the-Shelf AI Falls Short for Two Out of Three Businesses

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:

  • PreciseOutcomes--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."

Learn More:

Frequently Asked Questions

What is Infor Velocity Suite?

Infor Velocity Suite is an all-inclusive AI package built for your industry. It includes industry-specific AI agents, GenAI, process mining, automation and a team of experts to implement it all for you. Infor Velocity Suite is designed to be the fastest path to real AI value and, ultimately, to becoming an agentic enterprise. Infor Velocity Suite is the single package through which customers access the capabilities announced today. It combines Infor Industry AI Agents, the Agentic Orchestrator, process mining, automation, prebuilt industry use cases, generative AI, and the implementation expertise to put them to work, all tied to a customer's Industry Process Catalog. Rather than stitching together separate tools and consumption models from multiple vendors, customers adopt a single offer with unlimited access within fair business use, so adopting faster doesn't mean a larger invoice.

What is the Infor Enterprise AI Adoption Impact Index?

It's Infor's proprietary research initiative tracking how enterprises are adopting, governing, and realizing value from AI over time. The October 2026 wave is the second in the series, surveying 2,111 business decision-makers across seven global markets, building on an initial April 2026 wave of 1,024 decision-makers across four markets.

What Was the Survey Methodology for the Infor Enterprise AI Adoption Impact Index Conducted?

The October 2026 wave was conducted in August 2026 and polled 2,111 business decision-makers across seven markets -- the UK (254), US (550), Singapore (260), Japan (266), France (260), Australia (263), and Germany (258). Research was conducted by YouGov on behalf of Infor.

What does "off-the-shelf AI doesn't fit our industry" actually mean?

In the survey, respondents were asked whether generic AI solutions adequately address their industry's specific regulatory, workflow, and data requirements. Six of seven markets surveyed disagree by a clear majority -- meaning most enterprises believe horizontal, one-size-fits-all AI tools fall short of what their industry actually requires, regardless of geography.

How does Infor's architecture address the gaps this research identifies?

Infor's architecture is built specifically around the two gaps the research surfaces most clearly. The industry-fit gap is addressed at the foundation: Infor's CloudSuites and underlying data models are purpose-built for a defined set of industries, rather than generalized across every vertical, and a semantic/ontology layer gives agents industry-specific context rather than generic data to reason from. The governance-ownership gap is addressed through built-in auditability and human-approval workflows across the orchestration layer, so accountability and traceability are part of the architecture rather than something a customer has to bolt on separately.

What does "industry-specific context" actually mean at a technical level?

Infor's agents draw on a shared semantic and knowledge layer -- internally referred to during development as Infor IQ that gives every application in a CloudSuite a consistent understanding of core business concepts (what counts as a "location," an "item," a "customer") along with the process- and industry-specific nuance underneath them. For example, an agent handling a raw-material order for a food manufacturer needs to understand an industry-specific spec like a sugar shipment's Brix factor, while an agent handling an automotive parts order needs to understand a VIN number -- two completely different kinds of precision that a generic, horizontal data model isn't built to carry. Because Infor works across a defined, finite set of industries rather than attempting to serve every industry on the planet, it can build and maintain that depth of context in a way a horizontal platform serving dozens of unrelated industries structurally cannot.

Why does industry-specific context matter for things like accuracy and cost?

Enterprise AI agents typically need to call many technical, granular APIs to complete one piece of business work, which increases both the chance of errors and the compute cost of getting a task done. Infor's architecture instead exposes business-level process APIs -- the equivalent of "create a purchase order" rather than dozens of underlying technical calls -- so agents can complete work in fewer steps. Combined with industry-specific context from the semantic layer, this is designed to reduce hallucination, improve reasoning accuracy, and lower the token cost of completing a given task relative to a generic AI approach working from generic data.

Does this replace a customer's existing AI or orchestration tools?

No. Infor's architecture is built to be open and composable -- it's designed to plug into a customer's existing ecosystem, including third-party orchestration tools, rather than requiring a customer to replace what they already use. A single orchestration layer inside Infor coordinates work across Infor's own applications regardless of which front-end experience or outside orchestrator a customer chooses to use.

About Infor
Infor is a global leader in business cloud software specialized by industry. We develop complete solutions for our focus industries. Infor's mission-critical enterprise applications and services are designed to deliver sustainable operational advantages with security and faster time to value. Over 60,000 organizations in more than 175 countries rely on Infor's 17,000 employees to help achieve their business goals. As a Koch company, our financial strength, ownership structure, and long-term view empower us to foster enduring, mutually beneficial relationships with our customers. Visit www.infor.com.

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