Infor Study Reveals AI Adoption Barriers, New Platform Features

Infor has published the Infor Enterprise AI Adoption Impact Index, new proprietary research surveying 1,000 business decision-makers across the US, UK, Germany, and France on barriers to AI deployment and scaling.

The study reveals persistent structural barriers despite high confidence: 80% of business decision-makers believe their organization has internal capability to manage AI implementation, yet 36% cite data security, sovereignty, and compliance concerns; 25% report lack of internal AI talent; and 23% point to unclear ROI as major obstacles.

As a result, Infor has launched new features across Infor Velocity Suite and announced limited availability of an enhanced Infor Agentic Orchestrator, designed to deliver industry-specific precision and governed execution to close the gap between AI ambition and value.

Kevin Samuelson, CEO of Infor, emphasized the company's approach: "At Infor, agentic AI isn't a feature we bolted on. It's the culmination of two decades of deliberate foundation building. Our industry-specific platforms, multi-tenant architecture, and deep process intelligence give our agents a level of contextual precision that generic AI simply cannot replicate."

The research also found 49% of businesses remain in early AI deployment stages, running only pilots or limited rollouts. Infor states its new capabilities address the need for "leading technology, industry knowledge, effective execution, and transparent governance" to achieve AI value, with Samuelson noting: "A purchasing agent at a healthcare provider and one at a discrete manufacturer aren't the same agent. They shouldn't be."

This specificity, he argued, allows for clear ROI articulation: "That specificity is what allows us to clearly articulate the ROI and deliver on it. We're not selling automation for its own sake. We're selling measurable outcomes for industries by meeting our customers where they are with AI."

Artūras Malašauskas is an AI Systems Integrator with 20+ years of production-grade web engineering experience. He has designed, shipped, and scaled enterprise Python/PHP systems for logistics, SaaS, and public-sector clients. For the past year, he has focused exclusively on AI integrations: deploying open-source LLMs, building generative media pipelines (image, audio, video), and engineering multi-agent workflows for real production environments. His standard: reproducibility, security, cost-efficient inference—no vaporware. He documents and evaluates emerging AI tooling, separating verified capabilities from marketing noise. Technical editor at: muza-ai.eu, ai-verslas.lt, ai-naujinos.lt Connect on LinkedIn