Healthcare Providers Struggle to Scale AI Beyond Pilot Projects
More than half of healthcare organizations cannot move AI beyond experimental programs, according to research from Infor. The gap between pilot deployments and operational scale stems from governance requirements, data fragmentation, and security concerns specific to clinical environments.
Infor released new capabilities across its Velocity Suite and Infor Agentic Orchestrator to address this execution gap. The tools target healthcare-specific obstacles that generic AI systems cannot handle.
Governance and Compliance Block Deployment
Healthcare organizations face regulatory constraints that manufacturing and distribution sectors do not. Patient data protection, clinical governance protocols, and accreditation standards create requirements that off-the-shelf AI tools fail to meet.
Infor surveyed businesses across the US, UK, France, and Germany. Around 70% of US businesses and 74% of UK businesses report they have the capability to manage AI implementation. This readiness does not translate to execution at scale.
Governance and compliance emerged as the primary barrier in healthcare settings. This differs from manufacturing, where legacy infrastructure creates the largest obstacle, or distribution, where fragmented supply chain data prevents progress.
Data Security and Fragmentation Slow Progress
About 45% of UK healthcare businesses cite data sovereignty, security, and privacy concerns as factors preventing AI advancement. This compares to 34% in the US and Germany, and 32% in France.
Healthcare data exists across fragmented systems: electronic health records, laboratory systems, imaging platforms, and billing infrastructure. AI models trained on incomplete or inconsistent datasets could produce outcomes that fail clinical validation.
Only 25% of businesses report data maturity sufficient to support AI deployment. Healthcare providers managing patient information across multiple systems face a more acute gap than organizations in other sectors.
Other barriers include lack of internal AI talent at 25%, unclear return on investment at 23%, and high implementation costs at 23%.
Domain-Specific Agents Replace Generic Automation
Infor's Agentic Orchestrator provides infrastructure for AI agents designed for healthcare operations. A purchasing agent in a healthcare setting operates differently than one in discrete manufacturing or retail distribution.
Healthcare purchasing agents built within this system account for formulary restrictions, group purchasing agreements, and regulatory approval requirements. The platform uses supervisor agents that flag anomalies for human review rather than executing actions autonomously in clinical contexts.
The system employs standardized Model Context Protocol for secure data access. This allows healthcare systems to deploy AI while maintaining compliance with patient data protection regulations.
Oversight Requirements Differ by Industry
About 32% of survey respondents ranked autonomous task performance as a factor in long-term AI success. Healthcare applications require different autonomy parameters than other industries due to clinical and regulatory implications.
The platform's observability features provide inline thoughts, evaluation frameworks, and focus mode. These tools offer oversight for AI actions in environments where errors could affect patient care or regulatory compliance.
Healthcare providers adopting AI agents for clinical purchasing, resource allocation, or administrative workflows could see different return on investment profiles than organizations in manufacturing or distribution. The specificity of healthcare governance requirements means generic AI deployment produces limited outcomes at scale.
Learn more about AI for Healthcare and AI Agents & Automation to understand how these systems apply to your organization.
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