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Identified Recruitment Risk 4 Months Earlier. Preventing ~$120M Revenue Loss
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Outcomes
What we did
4 Months
Earlier delay risk detection
$120–150M
Potential revenue loss avoided
20–25%
Faster mitigation decisions
$500K/day
Cost of delay in clinical development
About Company
A Clinical Trial CFO based in the United States is managing a portfolio with $50M–$200M+ development exposure, where financial risk depends on operational trial performance.
Enrollment delays and site performance issues typically surface 3–4 months after early indicators emerge, limiting proactive intervention.
With trial delays costing ~$500K per day, delayed visibility can translate into $100M+ revenue impact per program.
Enrollment delays and site performance issues typically surface 3–4 months after early indicators emerge, limiting proactive intervention.
With trial delays costing ~$500K per day, delayed visibility can translate into $100M+ revenue impact per program.
Challenges
Key Barriers
to Trial Execution
Invisible Operational Risk
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Limited visibility into operational data
Enrollment and site risks not tracked in real time
Finance teams lack early signals
Late Reporting Cycles
Risks identified after mitigation window
Reactive budget revisions
Delayed financial decisions
Investor Reporting Pressure
Incomplete, outdated data
Difficulty building credible forecasts
Board-level pressure
Portfolio Blind Spots
Fragmented visibility across programs and CROs
No unified cost and risk view
Weak portfolio-level insight
Core platform
Operational Solution
Maxis AI Agentic Workflows — Under Human Oversight Throughout
Real-Time Enrollment Monitoring
Continuous tracking of enrollment and milestones
Early detection of delays and risks
4 months earlier detection
4 months earlier detection
Centralized
Orchestration
Predictive signals on velocity and milestones
Budget vs actuals tracking
Improved forecast credibility
20–25% faster mitigation
Portfolio Financial
Visibility
Unified cost and operational signals
Audit-traceable governance layer
$120–150M potential revenue protected
Real-time portfolio intelligence
2–3× deal size growth
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 |
|---|---|---|
| Delay detection | Recruitment risks identified late during operational reviews — after the mitigation window had passed | Enrollment risk detected 4 months earlier through real-time supervised signal monitoring |
| Revenue at risk | ~$120M+ projected revenue exposed to launch delay risk with no early warning system | ~$120M–$150M potential revenue loss avoided through proactive site expansion and recruitment adjustment |
| Mitigation decision speed | Reactive strategies with limited recovery options by the time risk was confirmed | 20–25% faster mitigation decisions across trial operations — intervention window preserved |
| Financial visibility | Limited forecasting of delay impact; board presentations built on incomplete data | Real-time financial risk intelligence integrated across all operational programs |
| Portfolio oversight | Fragmented — no integrated view of cost and operational risk across programs and CROs | Unified portfolio financial intelligence; reduced risk of late-stage cost surprises |
Outcomes
Emerging recruitment risk detected 4 months earlier — leadership activated additional sites, adjusted strategies, and protected an estimated $120M–$150M in projected launch revenue.
4 months earlier detection of enrollment-related delay risk — intervention window preserved
~$120M–$150M potential revenue loss avoided through proactive site expansion and recruitment adjustment
20–25% faster mitigation decisions — board and investor forecasts strengthened
Real-time portfolio financial intelligence across all development programs
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