Clinical AI Maturity Assessment
I built this as a working diagnostic for pharma and biotech leaders. It turns clinical AI readiness questions into a scored maturity level, capability-specific interpretation, and a practical 30 / 60 / 90-day plan.
What it shows
Domain fluency, structured product thinking, client-facing copy, scoring logic, and a production-style React workflow.
The maturity model
The assessment maps AI adoption from individual productivity to governed, operating-model-level execution across clinical development.
- 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 it covers
Study execution
Startup, monitoring, enrollment, and close-out workflows where AI can reduce cycle time, improve predictability, and help teams intervene earlier.
Clinical data
Whether data 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 for accountable AI use in regulated clinical development.
Operating model
Ownership, decision rights, metrics, and change management so AI moves beyond pilots into clinical development operations.
What the report returns
- Current maturity level from 1 to 4 with an explanation of why
- Dimension scores across workflow, context, data, oversight, governance, ownership, and measurement
- Capability-specific interpretation tied to the function selected
- The two readiness constraints most likely to block scale
- Recommended next moves and a 30 / 60 / 90-day roadmap
A few details first.
This asks 12 questions about how AI is used in clinical development today. It takes about 5 minutes and ends with a focused maturity report.