AI interest is isolated, lacking formal executive sponsorship, a clear vision, or a dedicated budget. AI efforts are ad-hoc and uncoordinated with organizational or clinical strategic priorities.

Benchmark: No documented AI strategy or dedicated budget line item can be found.

Executive interest in AI is present, with some champions emerging. Draft AI strategy exists but lacks formal approval, dedicated funding, or clear alignment with overall organizational goals. Clinical leadership is consulted sporadically.

Benchmark: A draft AI vision statement or slide deck exists.

Formal AI strategy approved by executive leadership, clearly linked to organizational and clinical priorities, with a dedicated budget allocated. Key clinical and operational leaders are actively involved in AI steering.

Benchmark: A board-approved AI strategic plan document exists.

AI strategy is fully integrated into the organization’s operational and strategic planning. Leadership actively champions AI, ensures resource allocation, and monitors enterprise-wide AI performance and clinical impact.

Benchmark: AI program KPIs are reviewed in quarterly leadership meetings.

AI leadership is visionary and adaptive, fostering a culture of responsible innovation. Strategy dynamically adjusts to emerging AI advancements, clinical needs, and enterprise performance, with transparent public reporting on AI initiatives and outcomes.

Benchmark: Public reports or press releases detail the organization’s AI strategy and outcomes.

No formal ethical review processes or guidelines for AI. Ethical considerations are ad-hoc, if discussed at all, with minimal clinician input.

Benchmark: Project charters for AI initiatives lack a section on ethical review.

Initial discussions on AI ethics are occurring. Informal or developing review processes for AI initiatives, with limited scope and inconsistent application. Basic awareness of ethical principles related to AI in healthcare.

Benchmark: Email records show an existing committee (e.g., IRB) was informally consulted on a pilot.

A dedicated ethics committee or review board (IRB subcommittee) established with a defined charter for AI, including clinical representation. Formal ethical guidelines and principles for AI development and deployment are documented and communicated.

Benchmark: A documented charter for the AI ethics review committee is available.

Regular ethics audits and risk assessments of AI systems are conducted. Processes for addressing ethical concerns and incidents are well-defined and operational. Ethical considerations are integrated into the AI lifecycle.

Benchmark: Audit reports for high-risk AI systems are completed and stored annually.

Proactive and continuous ethical oversight is embedded in all AI initiatives. Public reporting on ethical frameworks and performance. Organization actively contributes to broader discussions on AI ethics in healthcare.

Benchmark: The organization has published or presented its AI ethics framework externally.

No specific AI-related policies or formal compliance focus. Awareness of regulatory implications (e.g., HIPAA, FDA) for AI is low.

Benchmark: No AI-specific policies are found in the organization’s policy library.

Draft AI policies are being developed. Minimal alignment with legal and regulatory requirements. Compliance checks are informal and inconsistent.

Benchmark: A draft “AI Data Handling” policy is in review with a single department.

Comprehensive AI policies are finalized, approved, and communicated, covering data privacy, security, and intended use, with explicit reference to healthcare regulations. A compliance framework for AI is established.

Benchmark: An approved, organization-wide AI policy is published and accessible to all staff.

AI policies are regularly reviewed, updated, and integrated with enterprise risk management and legal governance. Proactive compliance monitoring and reporting mechanisms are in place for all AI systems.

Benchmark: A compliance dashboard monitors AI systems against internal policies and external regulations.

AI policy and compliance are fully integrated into legal/risk governance. The organization is agile in adapting to new regulations and actively shapes best practices for AI compliance in healthcare.

Benchmark: Legal team actively participates in national task forces on AI policy in healthcare.

No dedicated AI analytics capabilities or performance tracking. Reporting is manual, ad-hoc, and limited to basic operational metrics.

Benchmark: No standardized metrics for AI model evaluation exist.

Basic reporting capabilities for AI projects exist, but tracking is largely manual and project-specific. Limited use of dashboards or standardized AI performance metrics.

Benchmark: Basic dashboards track usage counts for 1-2 AI pilot projects.

Routine dashboards and standardized reporting cycles for AI initiatives are in place, tracking key performance indicators (KPIs), usage, and basic outcomes. Analytics inform AI model refinement.

Benchmark: Standardized performance dashboards are implemented for all deployed AI solutions.

Automated analytics and feedback mechanisms monitor AI model performance, drift, and impact in real-time. Results are used for continuous improvement and to inform clinical/operational decision-making.

Benchmark: Automated alerts for AI model performance degradation or significant data drift are in place.

Predictive analytics are deeply embedded, and AI-driven insights proactively inform strategic decisions, care pathways, and operational efficiencies. Continuous learning systems optimize AI performance.

Benchmark: Continuous A/B testing frameworks are used to optimize AI model performance.

No dedicated cloud infrastructure or scalable solutions for AI. Basic security measures are in place for general IT, not specific to AI vulnerabilities.

Benchmark: AI models are run on local servers or individual workstations.

Early exploration of cloud services for specific AI projects. Basic security protocols applied to AI pilots, but AI-specific vulnerabilities are not systematically addressed.

Benchmark: A single AI project is being piloted on a cloud service (IaaS/PaaS).

A secure and scalable infrastructure (on-premise, cloud, or hybrid) is established to support current AI initiatives. AI-specific security policies and controls are implemented.

Benchmark: The organization has a documented, AI-specific security policy.

Robust, dynamically scalable infrastructure supports enterprise-wide AI deployments. Advanced security measures, including threat detection and data encryption for AI, are operational with regular audits.

Benchmark: Regular penetration testing and vulnerability assessments for AI systems are conducted.

Highly resilient, agile, and cost-optimized infrastructure supports rapid scaling and deployment of diverse AI models. Cutting-edge security practices are embedded, with proactive threat modeling for AI systems.

Benchmark: The organization uses advanced AI security measures like adversarial attack detection.

No formal change management plan for AI adoption. Resistance to new technologies may be high, with limited staff engagement in AI initiatives.

Benchmark: Staff surveys indicate high resistance or skepticism towards new technology.

AI champions are identified informally. Basic communication about upcoming AI projects occurs, but no structured approach to manage cultural or workflow impacts.

Benchmark: A basic communication plan (e.g., newsletter updates) exists for an AI pilot.

A formal change management program for AI is defined, including stakeholder analysis, communication plans, and strategies to address resistance. Leadership visibly supports the change.

Benchmark: A documented change management strategy is required for all major AI initiatives.

Change management strategies are consistently applied org-wide for AI initiatives, with active leadership engagement and support. Feedback mechanisms are used to adapt change strategies.

Benchmark: >75% of impacted staff report understanding the purpose of new AI tools.

An embedded culture of innovation and continuous improvement embraces AI. Change management is proactive and agile. Staff are empowered to co-create and adapt to AI-driven transformations, with rewards for innovation.

Benchmark: Documented examples exist of staff-led AI innovations being adopted by the organization.

No formal mechanisms for collecting clinician feedback on AI tools. Iteration is rare or non-existent.

Benchmark: No system exists for clinicians to report issues or suggestions for AI tools.

Informal feedback from clinicians involved in pilots is occasionally collected. Little structured process for evaluation or incorporating feedback into AI tool refinement.

Benchmark: Feedback is collected via ad-hoc emails or conversations.

Structured processes are in place for collecting, evaluating, and prioritizing clinician feedback on AI tools. Iterative improvements are made based on this feedback.

Benchmark: A dedicated feedback button or channel exists within the AI tool interface.

Clinician feedback is systematically collected and drives continuous improvement cycles for AI tools. There is a clear process for co-design and rapid iteration with clinical teams.

Benchmark: At least one major AI tool update based on user feedback has been deployed in the last year.

AI tools are co-designed with clinicians, and dynamic updates based on real-world performance and ongoing feedback are standard practice. A culture of collaborative improvement is established.

Benchmark: Clinicians are active partners in co-design sprints for new AI features and iterations.

No dedicated budget for AI. Funding, if any, is opportunistic and project-specific. No concept of reinvesting AI-generated gains.

Benchmark: AI projects are funded, if at all, from departmental savings.

One-time budgets for AI pilots or initial exploratory projects. Financial planning for AI is short-term and reactive.

Benchmark: AI pilots are funded via one-time innovation grants.

AI initiatives are included in annual operational and capital budgeting cycles. Mechanisms for identifying and tracking AI-driven savings or revenue are emerging.

Benchmark: Business cases for AI are a required part of the annual budgeting process.

Reinvestment of value (cost savings, efficiencies) generated by AI initiatives into further AI development or other strategic priorities is a standard practice, guided by ROI data.

Benchmark: A documented process for reinvesting AI-generated savings is in place and has been used.

Strategic, long-term financial planning fully incorporates AI as a driver of value and innovation. A virtuous cycle of investment, value generation, and reinvestment is established for AI.

Benchmark: A multi-year strategic AI investment plan is aligned with long-term financial forecasts.

No patient, caregiver, or community input into AI initiatives.

Benchmark: All AI communications are internal-only.

Basic outreach (e.g., surveys, informational sessions) to patients or community groups about AI.

Benchmark: General information about AI is available on the public website.

Patient and community representatives are included in advisory roles for AI pilots or planning.

Benchmark: A patient advisory council is consulted on specific AI initiatives.

Patients, caregivers, and community members are systematically involved in the co-design and testing of AI tools.

Benchmark: Patients are included in reviewing AI tools for fairness and usability.

Patients, caregivers, and community representatives are integral partners in AI governance and co-lead AI initiatives.

Benchmark: Patients and community members are active partners on AI governance bodies.