Infor expands Industry AI as survey finds generic AI falls short for two in three businesses

Infor unveiled an expanded Industry AI architecture and the next evolution of its Velocity Suite at Velocity Week in Orlando, paired with a 2,111-firm survey: 68% of business leaders say off-the-shelf AI doesn't adequately address their industry's needs.

Infor, the enterprise cloud software vendor, unveiled on Tuesday an expanded Infor Industry AI architecture and the next evolution of its Velocity Suite at Infor Velocity Week 2026, running in Orlando from October 6 to 8. The release pairs a new agentic architecture — industry-specific agents, an adaptive user experience, and enterprise-wide governance — with the second edition of Infor's Enterprise AI Adoption Impact Index, which finds that 68% of business decision-makers say off-the-shelf AI doesn't adequately address their industry's needs.

Four pillars: precise, open, easy to use, governed

Infor says its Industry AI architecture is organized around four pillars. Precise Outcomes is an expanded suite of Industry AI agents with what the company calls micro-vertical expertise — agents that draw on Infor's Industry CloudSuites, Industry Process Catalogs, and industry-specific domain language models rather than reasoning from a generic horizontal model. Infor claims customers using this layer are seeing shipments processed up to 60% faster. The Infor GenAI Knowledge Hub, which lets customers build custom agents on top of Infor's industry knowledge, is now generally available.

Open & Connected describes an interoperable architecture that extends across a customer's full ecosystem, not just Infor's stack: agents coordinate through Infor IQ, a semantic layer that gives every agent a consistent understanding of the business, with more than 350 value-driven use cases available out of the box. Easy to Use is an adaptive UX that assembles a personalized, role-aware view in the tools people already work in — Infor cites up to 90% time savings across procurement, supply chain, manufacturing, and sales workflows.

The fourth pillar, Governed, is where Infor is leaning hardest. The release adds enhanced governance, risk, and compliance capabilities — human-approval workflows, agentic permission structures, and audit trails running through the orchestration layer — with what the company describes as verifiable, immutable logging of every agent action. Infor cites up to a 90% reduction in auditing costs tied to access management. Given that the company's own survey found accountability for AI is scattered across nearly every firm, the timing is pointed.

The survey: two in three say generic AI doesn't speak their industry's language

The Enterprise AI Adoption Impact Index is Infor's proprietary research series, run by YouGov in August 2026. This wave surveyed 2,111 business decision-makers across seven markets — the UK (254), US (550), Singapore (260), Japan (266), France (260), Australia (263), and Germany (258) — building on an April 2026 wave of 1,024 respondents. The headline finding: two in three (68%) say off-the-shelf AI doesn't adequately address their industry's needs, a majority view in six of the seven markets.

The fit problem is sharpest where work is physical and variable: distribution (76%) and manufacturing (73%) lead, with retail at 69%. Yet investment keeps climbing — 59% of businesses globally expect AI investment to increase over the next 12 months, and 54% of leaders are now comfortable with autonomous agents fully executing critical business processes without human input at every step. Only 11% prefer humans to make high-stakes decisions without any AI input.

What leaders say is missing most is ownership. When asked who is responsible when AI gets something wrong, 23% point to the CEO, 22% to the CIO or CTO, 15% to an AI committee, 10% to individual department heads — and 15% say no one person has primary responsibility. Chief AI Officer roles remain rare at 10% globally. And 33% cite data security, sovereignty, or compliance as their single greatest barrier to advancing their AI strategy.

What they said

"Everyone has the same AI models now. What matters is what those models know about your business," said Infor CEO Kevin Samuelson. "General-purpose AI doesn't get the details of their world, like how a food manufacturer traces a bad lot back through its suppliers."

Alicia Thompson, CTO of Team Air Distributing, said pairing Infor's agents with engineers who understand the industry is "reducing manual work and turning operational challenges into practical improvements." Coverage of the announcement frames the move as Infor's answer to the generic-AI gap its own survey documents.

What to watch

The pitch is coherent: if the models are commoditized, the differentiator is context — and Infor's claim is that a semantic layer plus business-level process APIs (fewer, bigger API calls than the dozens of technical calls a generic agent needs) reduces errors, token cost, and deployment time. The open questions are the usual enterprise ones: whether the cited customer results (60% faster shipments, 90% auditing-cost reductions) hold beyond the reference accounts, and whether the governance layer actually answers the accountability vacuum the survey exposes. For now, Infor has put a number on the problem its product is built to solve: 68%.