Infor Launches AI Orchestration Tools Amid Scaling Research

The enterprise software landscape received another wave of AI announcements this month, but Infor took a different approach. Rather than hyping model capabilities, the company paired its April 2026 product release with sobering data about why most businesses can't actually scale artificial intelligence beyond experimental phases.

The announcement centers on two main updates: expanded capabilities within the Infor Velocity Suite and limited availability of an enhanced Infor Agentic Orchestrator. These tools are designed to coordinate multiple AI agents across enterprise workflows while maintaining industry-specific context. The release is backed by findings from the Infor Enterprise AI Adoption Impact Index, which surveyed 1,000 business decision-makers across the US, UK, Germany, and France.

Here's the uncomfortable reality the research uncovered: while 80 percent of organizations believe they have the internal capability to implement AI, nearly half—49 percent—remain in early deployment stages. Many are stuck running pilots or partial rollouts that never graduate to full-scale operations. (It's the enterprise version of buying a gym membership and only using the shower.)

According to the official press release from Infor's newsroom, the primary barriers are structural rather than technological. Data security and compliance concerns top the list at 36 percent, followed by lack of internal AI talent at 25 percent, and unclear return on investment at 23 percent. These aren't abstract problems—they're the friction points that kill projects in the real world.

Kevin Samuelson, CEO of Infor, framed the company's positioning around industry specificity rather than generic AI capabilities. "At Infor, agentic AI isn't a feature we bolted on. It's the culmination of two decades of deliberate foundation building," he stated. The argument is that a purchasing agent at a healthcare provider operates differently than one at a discrete manufacturer, and generic AI models can't replicate that contextual precision.

The Agentic Orchestrator, now in limited availability, is designed to coordinate multiple AI agents across enterprise workflows. The system supports orchestration of complex tasks, interoperability across applications using an open Model Context Protocol, and new observability tools to improve transparency and control. This matters because most enterprise AI failures happen not at the model layer, but at the integration layer where agents need to interact with legacy systems.

Independent reporting from Robotics & Automation News corroborates the timeline and scope of the changes, noting that the updated Velocity Suite expands access to Infor's Industry AI Agents and introduces curated AI use-case packs. The company also highlighted a warehouse-focused add-on where machine learning-driven pick path optimization has delivered up to a 25 percent reduction in travel distance in some cases.

Customer testimonials provide concrete examples of deployment. Vera Janssens, supply chain analyst at Coram International, reported achieving 15 percent faster picking and 25 percent less travel distance using Infor's AI-driven Pick Path Optimization. Zoaib Saifuddin, general manager of IT at AMADA America, noted that the Agentic Orchestrator represents an evolution where "instead of our service engineers searching for answers, the intelligence comes to them."

The physical reality of these tools matters. When a warehouse worker receives a pick path optimized by machine learning, they're not interacting with an abstract algorithm—they're walking a shorter distance, clicking fewer screens, and completing tasks faster. That's the difference between AI as a concept and AI as a measurable outcome.

Mickey North Rizza, group vice-president for enterprise software at IDC, added external validation: "It is very clear that Infor's clients are finding sustained economic value with their path to the agentic enterprise and they love the journey with Infor." Analyst commentary like this helps separate marketing claims from actual customer satisfaction.

The research also reveals regional variations in barriers. Data security concerns hit hardest in the UK at 45 percent, compared to 34 percent in the US and Germany, and 32 percent in France. Talent gaps range from 20 percent in the UK to 28 percent in Germany. These aren't uniform problems—they require different solutions depending on market conditions.

What's notable about Infor's approach is the emphasis on governance and observability. The new capabilities include tools that give operations and IT leaders transparency into agent reasoning before moving to production. This addresses a critical gap: enterprises need to understand why an AI agent made a decision before trusting it with autonomous execution.

The broader industry context matters here. Many competitors are still selling AI as a feature bolted onto existing platforms. Infor's strategy positions AI as embedded into industry-specific processes from the ground up. Whether this architectural difference translates to better customer outcomes remains to be seen.

Customer feedback from Kattsafe's Jamarl Scace illustrates the practical appeal: "With Infor Velocity Suite, we can grow rapidly without expanding resources at the same pace. Starting with customer order entry as our first automation, it provided a simple, practical path to AI." The emphasis on starting small and scaling gradually aligns with the research findings about why many organizations struggle.

The execution gap identified in the research—between 80 percent confidence in capability and 49 percent stuck in early deployment—suggests the market is ready for solutions that address integration, security, and talent constraints simultaneously. Infor's positioning attempts to hit all three.

Whether users actually pay for this remains the real question. The enterprise AI market is crowded with vendors making similar claims about industry specificity and governed execution. The difference will show up in customer retention rates and measurable ROI over the next 12 to 18 months, not in press releases.

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