.svg){kind=icon}
100+ Programs. Margins Protected. No Headcount Growth Required.
.png)
Outcomes
What we did
$200–400K
Savings per trial
8–12 Wks
RBQM risk advance warning
70%
Programming automation
0 FTE
Additional headcount needed
About Company
Headquartered in London, with delivery operations across North America, Europe, and Asia-Pacific, this global CRO was managing a growing portfolio of sponsor programs across oncology, rare diseases, and cardiovascular trials.
As new programs scaled, delivery remained dependent on proportional increases in biometrics headcount — limiting profitability despite continued business growth.
At the same time, sponsors were increasingly expecting portfolio-level RBQM. Without a unified data infrastructure, the CRO was unable to deliver a credible, cross-study risk view.
As new programs scaled, delivery remained dependent on proportional increases in biometrics headcount — limiting profitability despite continued business growth.
At the same time, sponsors were increasingly expecting portfolio-level RBQM. Without a unified data infrastructure, the CRO was unable to deliver a credible, cross-study risk view.
Challenges
Key Barriers
to Trial Execution
Reactive Quality
%201.svg){kind=icon}
Missed or late risk detection
30–40% DB lock delays
Late escalations
No Portfolio RBQM
Fragmented data across programs
No unified risk visibility
Shrinking margins
Weak sponsor confidence
Headcount Model
Headcount-driven delivery model
Rising costs with scale
Programming Bottleneck
$180–220/hr FSP costs
25–30% attrition
Quality variability and margin drag
Core platform
Operational Solution
Maxis AI Agentic Workflows — Under Human Oversight Throughout
AI for RBQM
Unified portfolio risk view
Risks predicted 8–12 weeks early
Portfolio-level RBQM
AI for Programming
70% automation
$200K–$400K savings per trial
Scalable delivery model
$200–400K per trial savings
AI for Data Management
Query backlog reduced
Capacity expanded without hiring
No FTE expansion required
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 |
|---|---|---|
| Delivery Margins | Revenue growing; margins eroding — every new study required proportional FTE increases | Delivery capacity scaled without proportional headcount; margins protected at higher volume |
| RBQM Capability | No credible portfolio RBQM view to show sponsors; data fragmented across systems | Unified RBQM portfolio view; predictive signals 8–12 weeks ahead; RBQM as sponsor differentiator |
| Programming Costs | $180–220/hr FSP rates; dataset delivery the margin bottleneck | $200K–$400K per-trial savings; programming delivery decoupled from headcount |
| Risk Detection | Reactive; KRI thresholds missing risks until weeks too late | Proactive; risks resolved before milestone impact; sponsor confidence strengthened |
| Scalability | Headcount-constrained — could not bid new programs without margin sacrifice | Scalable delivery; new programs taken on without FTE expansion; RBQM as competitive differentiator |
Outcomes
The CRO achieved scalable delivery, restored margins, and established credible RBQM as a competitive differentiator across 100+ programs.
Scaled delivery without added FTEs — higher biometrics throughput
$200K–$400K savings per trial vs. FSP rates
Credible RBQM capability — unified risk view with 8–12-week foresight
Stronger sponsor confidence — more wins without headcount growth
Looking for Agentic AI for clinical trials?
Explore our Agentic AI Platform to see how AI agents are transforming study startup, data management, oversight, and regulatory submissions.
