How AI-ready is your clinical development organization?
Score AI maturity across trial workflows, clinical data, oversight, governance, and operating model. End with a focused snapshot and a 30/60/90-day roadmap.
Focused on clinical operations more than AI? Try the Trial Capability Map instead.
The Clinical AI Maturity Model
A practical way to understand how AI moves from individual assistance to governed clinical development workflows.
- Level 1
Individual Experimentation
Ad hoc AI use for personal productivity
Clinical development teams are using AI mostly for individual drafting, summarization, research, or analysis. The value is real, but usage is personal, inconsistent, and not yet embedded in trial execution.
- Level 2
Assisted Clinical Workflows
AI supports defined clinical tasks
AI is helping with specific clinical development tasks such as document review, data checks, meeting preparation, or study team support, with manual review still carrying most of the workflow.
- Level 3
Governed Workflow Automation
Repeatable trial workflows with controls
AI is applied to repeatable study workflows with defined data sources, human checkpoints, risk controls, and measurable impact on cycle time, quality, oversight, or capacity.
- Level 4
AI-Enabled Clinical Development Operating Model
AI is embedded across governed trial execution
AI is part of the clinical development operating model, coordinating across systems, study teams, vendors, data, decisions, and governance while preserving inspection-ready accountability.
What the assessment covers.
Targeted questions about how AI is being used in your clinical organization today across workflows, data, oversight, and operating model.
Study execution
Startup, monitoring, enrollment, and close-out — where AI can reduce cycle time, improve predictability, and help teams intervene earlier.
Clinical data
Whether the data foundation across EDC, CTMS, eTMF, safety, RTSM, and analytics is connected, trusted, and usable for AI-supported workflows.
Governance
GxP, audit trails, validation, and inspection readiness — how ready AI-supported workflows are for regulated clinical development and accountable human review.
Operating model
Ownership, decision rights, metrics, and change management. The pieces that determine whether AI moves beyond pilots into clinical development operations.
What you walk away with.
- Current clinical AI maturity level (1–4) with a clear explanation of why
- Dimension-by-dimension scores across the readiness areas in scope
- Capability-specific interpretation tied to the function you care about
- The two readiness constraints most likely to block scaling AI further
- Recommended next moves and a 30/60/90-day roadmap
Ready to see where your organization stands?
The result lands in your inbox. We will follow up only if you ask us to.