Infor's Technology Tackles Healthcare's AI Execution Gap

Healthcare providers face an execution gap between AI ambition and operational reality. Data from Infor shows more than half of businesses cannot scale AI beyond pilot programmes.

The industry cloud provider has released capabilities across its Velocity Suite and Infor Agentic Orchestrator to address this gap. These tools target sector-specific challenges that prevent healthcare organisations from deploying AI at scale.

"At Infor, agentic AI is not a feature we bolted on. It's the culmination of two decades of deliberate foundation building," says Kevin Samuelson, CEO of Infor. "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."

"A purchasing agent at a healthcare provider and one at a discrete manufacturer are not the same agent, they should not be," Kevin notes.

Healthcare governance blocks AI deployment

According to Infor research conducted across the US, UK, France and Germany, governance and compliance present the primary barrier to AI implementation in healthcare settings. This differs from manufacturing, where legacy infrastructure creates the largest obstacle, or distribution, where fragmented supply chain data prevents progress.

Around 70% of US businesses and 74% of UK businesses report capability to manage AI implementation. This readiness does not translate to execution at scale.

Healthcare organisations operate under regulatory frameworks that generic AI systems cannot accommodate. Patient data protection requirements, clinical governance protocols and accreditation standards create constraints that off-the-shelf AI tools fail to address.

The gap between technical capability and operational deployment could mean healthcare providers require domain-specific AI rather than general-purpose automation.

Data security concerns dominate healthcare AI strategy

About 45% of UK businesses cite data sovereignty, security and privacy concerns as factors preventing AI advancement, according to Infor. This compares to 34% in the US and Germany and 32% in France.

Healthcare data exists across fragmented systems including 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. For healthcare providers managing patient information across multiple systems, this gap could prove more acute than in other sectors.

The study identifies lack of internal AI talent at 25%, unclear return on investment (ROI) at 23% and high implementation costs at 23% as additional barriers.

Industry-specific AI agents address healthcare workflows

Infor's Agentic Orchestrator provides infrastructure for AI agents designed for healthcare operations rather than generic business processes. The platform operates across three areas: orchestration, interoperability and observability.

Healthcare purchasing agents built within this system could account for formulary restrictions, group purchasing agreements and regulatory approval requirements. This differs from purchasing agents designed for discrete manufacturing or retail distribution.

The platform uses supervisor agents and task-specific agents. Supervisor agents flag anomalies for human review rather than executing actions autonomously in clinical or administrative contexts.

Infor Agentic Operator employs standardised Model Context Protocol for secure data access. This could allow healthcare systems to deploy AI while maintaining compliance with patient data protection regulations.

Autonomous AI requires transparency in healthcare settings

According to the Infor study, 32% of respondents rank autonomous task performance as a factor in long term AI success. Healthcare applications could require different autonomy parameters than other industries due to clinical and regulatory implications.

The platform's observability features offer three capabilities: inline thoughts, evaluation frameworks and focus mode. These tools provide oversight for AI actions in environments where errors could affect patient care or regulatory compliance.

"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," says Mickey North Rizza, Group Vice President of Enterprise Software for IDC.

Healthcare providers adopting AI agents designed for clinical purchasing, resource allocation or administrative workflows could see different ROI profiles than organisations in manufacturing or distribution sectors. The specificity of healthcare governance requirements means generic AI deployment produces limited outcomes at scale.