AI Adoption
AI adoption for clinical research teams that don't have an AI team.
Built for clinical research teams bringing AI into everyday work — sites and site networks, CROs and sponsors.
- Two-week assessment
- 2–3 workflows with payback
- Review and audit built in
- SOPs and training included
Assess → prioritize → build + govern → adopt
The problem
AI is already in the workflow. Nobody planned it.
- Uncontrolled toolsStaff paste protocol text into whatever AI tool is open.
- No ownerNobody owns AI, so pilots stall after the demo.
- No prioritiesLeadership can't tell which workflows are worth automating.
The journey
Four steps from scattered tools to governed workflows
Assess
Two weeks mapping where coordinator and ops hours actually go.
Prioritize
Pick 2–3 workflows with measurable payback.
Build and govern
Deploy inside your boundary with review and audit built in.
Adopt
Train the team, update SOPs, measure hours saved.
Workflows
Where it pays back first
Six workflows where site teams lose the most hours to manual work.
Every output stays a draft until a named person approves it.
Start with one workflow →- Protocol summaries
TodayEvery coordinator reads the full protocol and keeps their own notes.
With AIOne consistent synopsis per protocol, reviewed once.
- Schedule of events
TodayVisit tables retyped by hand into worksheets and calendars.
With AIExtracted from the protocol and checked by a person.
- Feasibility responses
TodayThe same sponsor questionnaires answered from scratch each time.
With AIDrafted from your site's past answers and capabilities.
- Source worksheets
TodayBuilt by hand per visit, rebuilt after every amendment.
With AIGenerated from the protocol, regenerated when it changes.
- Pre-screening support
TodayCharts read against long eligibility lists.
With AICriteria checklists drafted per candidate, decided by staff.
- Recruitment reporting
TodayWeekly numbers pulled together by hand from CTMS and spreadsheets.
With AIFunnel reports assembled automatically for sponsor calls.
Adoption without a record is a finding waiting to happen. Every workflow we build runs through Governed AI. See Governed AI →
In practice
Case studies
Vitalief — United States
Governed AI for clinical operations
Vitalief had expert AI workflows that only one person could run. We rebuilt them so the whole firm can — under signature, sanitized for PII and PHI, against an audit record that cannot be altered.
75%
Efficiency gain100%
Immutable audit records2
Signatures before release~1 month
Build to productionMulti-site network — United States
Recruitment and operations automation across a large US site network
Pre-screen failures were dead ends and coordinators were re-keying the same candidate data across systems. We rebuilt the pre-screen step as an automated matching engine and wired the surrounding operations into one traceable workflow.
1 in 3
Pre-screen failures matched to another active trial in the network.Network-wide
Every active study checked automatically, not just the referring one.