Infor Research: Closing the Agentic AI Execution Gap

2026-04-22 · Source: AI Magazine · Field: Business & Management — Corporate Strategy & Leadership, Operations & Process Management, Consulting & Professional Services · Depth: Intermediate, quick

Summary

New research from Infor indicates that over half of businesses are struggling to scale AI, despite strong confidence in the technology. The study, conducted across the US, UK, France, and Germany, identifies structural barriers such as legacy systems, inconsistent governance, and fragmented data. Infor has responded by introducing new capabilities within its Velocity Suite and Infor Agentic Orchestrator, offering niche, industry-specific AI solutions designed to close this execution gap. Key challenges highlighted include data security concerns (cited by 32-45% of businesses), lack of internal AI talent (25%), unclear ROI (23%), and high costs (23%). Infor's approach emphasizes deep domain-specific knowledge and a "trusted, transparent infrastructure layer" for agentic AI, focusing on orchestration, interoperability, and observability to enable coordinated workflows and user control.

Key takeaway

For CTOs and AI Architects evaluating enterprise AI strategies, recognize that generic AI often fails to scale due to industry-specific operational realities. Your teams should prioritize solutions that offer deep domain knowledge and address critical barriers like fragmented data and governance. Consider platforms like Infor's Agentic Orchestrator that provide transparent infrastructure, multi-agent orchestration, and robust observability to ensure secure, compliant, and effective AI deployment.

Key insights

Industry-specific AI solutions are crucial for overcoming execution barriers and scaling AI effectively in enterprises.

Principles

• Generic AI struggles with industry-specific operational realities.
• Contextual precision drives measurable ROI for AI agents.

Method

Infor's Agentic Orchestrator provides a transparent infrastructure for multi-agent operations, using supervisor agents for anomaly flagging and a Model Context Protocol (MCP) for secure data access.

In practice

• Prioritize data maturity for reliable AI outcomes.
• Implement robust governance for secure AI deployment.

Topics

• Agentic AI
• AI Execution Gap
• Industry-Specific AI
• Data Security Concerns
• AI Orchestration

Best for: CTO, Executive, AI Architect, Director of AI/ML, VP of Engineering/Data, Consultant

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Editorial summary, takeaway, and curation by AIssential. Original article published by AI Magazine.