“A healthcare provider’s purchasing agent and an individual manufacturer’s purchasing agent are not the same agency, and they shouldn’t be,” Kevin points out.
Healthcare governance hinders AI adoption
According to Infor research conducted in the US, UK, France, and Germany, governance and compliance are the main barriers to AI adoption in healthcare settings. This is different from manufacturing, where legacy infrastructure creates the biggest hurdle, or distribution, where fragmented supply chain data hinders progress.
Approximately 70% of US companies and 74% of UK companies report having the ability to manage AI implementation. This preparation does not lead to large-scale implementation.
Healthcare organizations operate under regulatory frameworks that cannot be addressed by typical AI systems. Patient data protection requirements, clinical governance protocols, and accreditation standards create constraints that off-the-shelf AI tools cannot address.
Given the gap between technical capabilities and operational adoption, healthcare providers may need domain-specific AI rather than general-purpose automation.
Data security concerns dominate healthcare AI strategy
According to Infor, around 45% of UK companies cite concerns about data sovereignty, security and privacy as a barrier to AI progress. This compares to 34% in the US and Germany and 32% in France.
