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12 FTEs. 15 Trials. No Contractors. DB Lock Accelerated 40–50%.
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Outcomes
What we did
15 Trials
Managed by 12 FTEs
60–70%
Queries auto resolved
40–50%
Faster DB lock
0
Contractors hired
About Company
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.
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
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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
Core platform
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
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
40–50% faster DB lock
In-House Scaling
12 FTEs managed all 15 trials
No additional headcount needed
Full quality control maintained
No contractors needed
Maxis AI operated as a governed and supervised execution layer within existing systems throughout.
Core platform
Measured Impact
Quantified Outcomes After Deploying Maxis AI’s Agentic Workflows
| Metric | Before Agentic AI | After Agentic 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 |
Outcomes
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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