U.S. hospitals lag on 2026 AI governance funding: Black Book Research
FLORIDA, UNITED STATES — United States hospitals are rapidly expanding their use of artificial intelligence (AI), but most are failing to match that pace with the funding needed for governance to keep systems compliant, transparent, and safe, according to new findings from Black Book Research.
As AI becomes more embedded in both clinical and administrative workflows—including outsourced functions—the lack of robust oversight could leave hospitals vulnerable to regulatory pressure and operational risks.
AI adoption rises faster than oversight budgets
The survey, which included 182 hospital leaders nationwide, found that the median budget allocation for AI governance and safety in 2026 is 4.2%. While larger systems dedicate more money to oversight, the gap remains evident.
“Underinvestment is the quiet risk in hospital AI programs,” said Doug Brown, founder of Black Book Research.
That underinvestment is most evident in audit readiness. Only 22% of hospitals reported being highly confident that they could produce a complete and auditable AI explanation within 30 days for regulators or payers.
This requirement is becoming increasingly important as AI expands into high-stakes functions, such as patient triage, documentation, and clinical decision support.
This challenge is multiplied for hospitals that outsource clinical documentation, revenue-cycle management, or data analysis to third-party vendors that use AI tools.
Even when AI is externally managed, hospitals remain responsible for audit trails and compliance — a rising expectation that many organizations are not fully prepared to meet.
Policy gaps widen governance and compliance risks
Governance challenges extend beyond budget constraints. According to the survey, only 29% of hospitals have implemented and enforced AI policies covering “model inventory, lineage, and sign-offs,” while 48% are still in the process of drafting them.
Weak internal structures make it more challenging to manage the AI models embedded in outsourced workflows, ranging from patient intake automation to clinical coding platforms.
Vendor transparency is another obstacle. About 41% of hospital leaders reported that “limited explainability artifacts from vendors (e.g., model cards, drift reports)” create the largest barrier to audits.
This is a critical issue for systems relying on outsourced AI tools, as vendors may not provide the necessary documentation that hospitals need for regulatory purposes.
Data provenance also remains a pain point, with 37% saying they cannot fully track data inputs and model versions — a foundational requirement for any health system that partners with external AI vendors.
At the organizational level, 33% of respondents cited unclear internal ownership between IT, quality/safety, and compliance teams.
This ambiguity becomes especially problematic when outsourced services are involved, as no single department is responsible for monitoring the AI that resides within vendor-delivered workflows.
“Even among large medical centers, governance budgets remain single-digit shares. Hospitals need audit trails, not just pilots, to prepare for 2026 scrutiny. Smaller facilities are one incident away from major disruption. Shifting even two to three percentage points of 2026 budgets toward AI governance could drastically improve readiness,” Brown warns.
Looking ahead, budget increases are modest. Only 26% of hospitals plan to boost governance and safety funding by two percentage points or more in 2026, while 18% do not plan to increase funding at all.
