Infor expands Industry AI as survey finds generic AI misses the mark for 68% of firms

Four pillars behind Infor Industry AI

Infor, an enterprise provider of cloud software, said the release includes personalized adaptive user experiences, new governance models across customers' entire enterprise, and more Industry AI agents. According to the company, the architecture is meant to close the "value void", the gap between what technology promises and what companies achieve, and to help businesses become agentic enterprises in which people and agents work as one coordinated team.

Infor says its agents are built with industry-specific context already in place, which is designed to reduce errors and guesswork, use tokens efficiently and shorten the path from deployment to value. The platform is organized around four pillars:

• Precise Outcomes: Agents draw on Industry CloudSuites, Industry Process Catalogs and industry-specific domain language models rather than a generic, horizontal model. Infor says customers using this layer are seeing shipments processed up to 60% faster. The Infor GenAI Knowledge Hub, which lets customers build custom AI agents, is now open to general availability.
• Open & Connected: The platform connects to non-Infor applications and existing orchestration and analytics tools, so customers need not standardize on one vendor's stack. Agents are coordinated through Infor IQ, a semantic layer, with a catalog of more than 350 value-driven use cases available out of the box.
• Easy to Use: Adaptive UX assembles a personalized, role-aware view, pulling items such as the bill of materials, quoted price and delivery date into one screen instead of ten. Customers can work through the Infor GenAI Assistant or AI assistants they already use. Infor reports up to 90% time savings across procurement, supply chain, manufacturing and sales workflows.
• Governed: Enhanced Governance, Risk, & Compliance capabilities include human-approval workflows, agentic permission structures and audit trails across the orchestration layer. Infor says every agent action runs through this layer, with explainable AI logic and verifiable, immutable logging. The company reports up to a 90% reduction in auditing costs tied to access management.

What the Enterprise AI Adoption Impact Index found

The second edition of the index found that investment in AI is outpacing efficiency gains, a mismatch Infor attributes to the post-adoption gap often created by traditional AI and ERP solutions. In six of the seven markets surveyed, the majority view was that off-the-shelf AI falls short for their industry, with 68% of businesses agreeing overall.

Industry differences were notable. Pooled across all seven markets, 73% of manufacturing respondents said off-the-shelf AI does not fit their needs. Distribution reached 76% and retail 69%. Infor reads this as a pattern: the more variable and hands-on the operating environment, the less generic AI delivers.

Regional trends diverged when compared with the first index in April 2026. Businesses in the US are accelerating deployment and seeing more efficiency gains. Germany held steady on both measures, favoring methodical pilot expansion. The UK was mixed, with efficiency gains falling back while deployment edged forward. Globally, 59% of businesses expect AI investment to increase over the next 12 months.

Trust in autonomy is growing: 54% of leaders are comfortable with autonomous agents fully executing critical business processes without human input at every step, while just 11% prefer humans to make high-stakes decisions without any AI input.

Accountability remains unclear

The survey also points to an ownership problem. Asked who is responsible when AI gets something wrong, 23% named the CEO or executive leadership, 22% the CIO or CTO, 15% an AI committee or governance group and 10% individual department heads. Another 15% said no one has primary responsibility or that it is unclear. Chief AI Officer roles sit at just 10% globally.

That gap shows up in stated barriers: 33% of leaders cite data security, sovereignty or compliance as their single greatest obstacle to advancing their AI strategy.

What it means for customers

Infor is positioning built-in industry context and governance as the answer to these concerns. "Everyone has the same AI models now. What matters is what those models know about your business," said Kevin Samuelson, CEO of Infor, in the announcement.

Alicia Thompson, CTO of Team Air Distributing, said Infor has kept pace with her company's ambitions by pairing Infor Industry AI Agents with Forward Deployed Engineers who understand its industry. The performance figures in the release, such as the 60% and 90% gains, are reported by Infor and refer to what it says customers are seeing.