Solutions · Mid Pharma
12 FTEs. 15 Trials. No Contractors. DB Lock Accelerated 40–50%
A mid-sized pharma sponsor used Maxis AI to run 15 trials with 12 FTEs, speed DB lock by 40-50%, auto resolve 60-70% queries, and avoid hires.
15 Trials
Managed by 12 FTEs
60–70%
Queries auto resolved
40–50%
Faster DB lock
No
Contractors hired
WHO IT IS
The Context
A mid-sized pharma sponsor managing 15 late-phase trials with an internal biometrics team faced a core execution constraint: capacity did not scale with workload.
As query volumes increased, database lock timelines slipped across multiple programs. With 80% of effort spent on manual queries and SAP cycles extending to 4–8 weeks, execution bottlenecks intensified.
Scaling through contractors added cost and complexity without resolving the constraint. The requirement was not more headcount—it was the ability to scale execution output within the existing team.
Challenges
Key barriers to Trial Execution
Capacity Overload
- 12 FTEs managing 15 trials
- Workload exceeding capacity
- DB lock delays across 4 programs
Manual Query Burden
- 80% time spent on queries
- Limited bandwidth for oversight
- Reduced focus on clinical review
Programming Bottleneck
- 4–8 week SAP cycles
- Bottlenecks across parallel trials
- Competing submission timelines
Contractor Risk
- High onboarding effort
- Knowledge gaps and inconsistency
- Quality concerns for in-house standards
Operational solution
Maxis AI agentic workflows — under human oversight throughout
AI for Data Management
- 60–70% query reduction
- Reduced manual review workload
- Shift to clinical oversight
Outcome
60–70% queries auto-resolved
AI for Programming
- 70% mapping and TFL automation
- SAP timelines cut from weeks to days
- Capacity freed for advanced analysis
Outcome
40–50% faster DB lock
In-House Scaling
- 12 FTEs managed all 15 trials
- No additional headcount needed
- Full quality control maintained
Outcome
No contractors needed
Maxis AI operates as a governed and supervised execution layer within existing systems throughout.
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Measured impact
Quantified outcomes after deploying Maxis AI's agentic workflows
| Metric | Before Maxis AI | After Maxis AI |
|---|---|---|
| Biometrics Workload | In-house team of 12; 15 concurrent trials; query volume outpacing capacity | 60–70% of routine queries auto-resolved; team capacity freed for clinical oversight |
| Database Lock | 4 programs with slipping milestones; contractor expansion rejected as unworkable | 4 programs returned to schedule; database lock accelerated 40–50% per study |
| Data Issue Detection | Weeks — reactive identification | Days — proactive AI-flagged signals |
| Execution Visibility | Limited — periodic internal reporting | Continuous — real-time tracking across all 15 trials |
| Timeline Predictability | Variable — milestone dates uncertain across portfolio | Improved — predictable delivery with AI-assisted oversight |
Outcome
Quantified Benefits
The in-house biometrics team of 12 FTEs successfully managed all 15 concurrent trials without contractor expansion — delayed programs returned to schedule.
60–70% of queries auto-resolved — data managers refocused on clinical oversight
Database lock accelerated 40–50% — delayed programs back on track
SAP timelines cut from weeks to days
No contractors hired — 12 FTEs managed 15 trials with full quality control
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