Healthcare organizations are under pressure to reduce costs, improve care quality, and do more with constrained resources. This makes generic AI tools appealing, especially for clinicians looking to create efficient workflows and get faster insights. However, what appears to be a simple efficiency gain can create hidden costs due to security, compliance, operational, and clinical risks.
The real price tag of ungoverned clinical AI tools
Unauthorized AI use in clinical and administrative workflows can create financial risks. According to research by IBM, the average security breach in a healthcare organization cost $7.42 million in 2025, and of all industries, healthcare takes the longest to identify and contain a breach.
The same research reveals that among organizations that experienced an AI-related security incident, 97% lacked proper AI access controls and 63% had no formal governance policies.
A single incident can trigger:
- Regulatory penalties
- Legal expenses
- Operational disruptions
- Remediation costs
- Reputational damage
When AI is used outside approved channels, organizations may not know where an exposure exists until after an incident occurs.
Shadow AI is already in your organization
Unsanctioned AI use is an operational reality. One survey found that 58% of frontline health system staff used generic, free AI tools at least once in the previous month, with some users acknowledging that they entered identifiable patient information into these tools. Similarly, research by Wolters Kluwer found that 57% of respondents had encountered or used an unauthorized AI tool within their organization.
Shadow AI often proliferates because healthcare professionals are under pressure to work faster and may lack approved tools that meet their needs. A lack of clear guidance can further drive unauthorized AI use; 21% of providers felt that their organization’s AI policies were either unclear or neutral in clarity, making unapproved AI tools a common choice. This can create hidden costs such as duplicate technology spending and increased vendor management complexity, quickly becoming an expensive system-wide problem.
The hidden care quality cost
Beyond security and compliance, fragmented AI adoption can also contribute to care variation when teams rely on generic LLMs that are not transparently grounded in current, evidence-based standards or that base answers solely on individual articles without a broader clinical context. This can drive unnecessary tests, redundant procedures, preventable admissions, longer lengths of stay, and inconsistent treatment decisions.
Unwarranted care variation is estimated to cost the U.S. healthcare system approximately $100 billion annually, while medication errors cost the global healthcare system an estimated $42 billion each year.
AI tools that lack clinical governance can amplify these challenges and erode perceived savings, creating financial, compliance, and care quality risks that health system leaders can’t afford to ignore.
Leading health systems are investing in governed, enterprise AI
Despite all of this, the answer is not to eliminate AI use in healthcare, but to replace unmanaged AI adoption with a governed, enterprise-wide AI strategy.
Leading health systems are moving toward approved, purpose-built solutions that integrate with existing infrastructure, align with healthcare-specific security requirements, and support organization-wide governance.
A modular, connected architecture can help create benefits across the system:
- Reduced vendor sprawl and potential duplicate technology spend by consolidating disconnected solutions into a connected ecosystem.
- Strengthened security and compliance through enterprise-grade governance and access controls that generic tools often lack.
- Centralized visibility into tool usage across the organization, helping leaders manage risk, compliance, adoption, and generated value more effectively.
- Improved return on investment through platform consolidation and integrated insights that support clinical and operational performance.
In practice, that means standardizing with enterprise AI solutions that feature governance, analytics, and trusted clinical content. Solving the AI governance challenge requires a solution that clinicians can trust enough to use, rather than turning to ungoverned alternatives.
UpToDate® Enterprise Edition builds on a foundation of expert-authored, peer-reviewed clinical content that clinicians have trusted for decades and extends it through a governed AI approach designed to support clinical reasoning.
Unlike general-purpose AI tools that generate responses from broad internet-scale training data or individual studies, UpToDate Expert AI is grounded in expert-authored, peer-reviewed clinical content and developed within a framework of clinical oversight and ongoing evaluation. In comparative testing, general-purpose AI models produced one additional error for every seven clinical queries compared with UpToDate Expert AI—a 15% higher error rate, which has real consequences for patients and clinicians in practice.
Reducing shadow AI also requires selecting an enterprise solution that fits how clinicians already work. UpToDate is already a trusted resource across many health systems, making it easier to extend responsible AI adoption through a familiar platform rather than introducing a separate tool that clinicians may be less inclined to use.
By supporting access to evidence-based guidance within existing workflows, including capabilities such as formulary support, UpToDate Enterprise Edition can help health systems steer users toward an approved resource that aligns with organizational standards.
For leaders, enterprise-wide analytics and reporting can further support AI governance efforts by providing visibility into adoption patterns, helping identify whether clinicians are engaging with approved resources or turning to alternative tools.
See how UpToDate Enterprise Edition can help you bring shadow AI into the light, strengthen governance, and protect your financial and clinical outcomes.