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4–6 Months Earlier Risk Detection. $15–20M Saved. 30–40% Faster Decisions. 8–12 Weeks Predictive Signals.
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Outcomes
What we did
4–6 Months
Portfolio visibility
$15–20M
Manual coordination cycles
30–40%
Governance across all teams
8–12 Wks
Milestone predictability
About Company
A CMO of a leading global R&D operation in Basel, Switzerland is overseeing 10–25 active Phase II–III programs across multiple CROs and regions.
Operational, safety, and enrollment signals remain fragmented across systems, with risks surfacing only during monthly or quarterly reviews — often 4–6 months after early indicators appear.
With $10M–$20M exposure per program, delayed visibility directly impacts portfolio decisions and increases the risk of late-stage failure.
Operational, safety, and enrollment signals remain fragmented across systems, with risks surfacing only during monthly or quarterly reviews — often 4–6 months after early indicators appear.
With $10M–$20M exposure per program, delayed visibility directly impacts portfolio decisions and increases the risk of late-stage failure.
Challenges
Key Barriers
to Trial Execution
Portfolio Blind Spots
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Fragmented updates across CROs
Risks identified in late-stage reviews
6+ months delay in detection
Slow Decision Cycles
Manual analytics and reporting
Delayed critical decisions
Limited ability to course-correct
Late Signal Detection
CRO performance gaps missed
Slow enrollment trends overlooked
Safety signals detected too late
No Unified Visibility
Fragmented reports across teams
No single source of truth
Reactive risk management
Core platform
Operational Solution
Maxis AI Agentic Workflows — Under Human Oversight Throughout
Continuous Portfolio Monitoring
Unified view of safety and enrollment
AI agents monitor across studies
Risks detected months earlier
4–6 months earlier detection
Automated Progress Reporting
Automated reporting and compliance checks
Human-in-the-loop validation
Replaces manual analytics cycles
30–40% faster decision cycles
Predictive Risk Intelligence
Early signals on CRO performance and enrollment
Governance-driven risk detection
8–12 weeks early intervention
$15–20M cost avoided per program
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 |
|---|---|---|
| Time to detect risk | Issues surfaced during late-stage reviews, often 6+ months after early signals were present | Risk signals detected 4–6 months earlier through continuous supervised monitoring |
| Decision cycles | Manual reporting delays slowing critical go/no-go decisions across portfolio | 30–40% faster portfolio decision cycles; earlier course corrections protecting pipeline value |
| Program cost exposure | Costly late-stage program termination with limited recovery options | $15M–$20M potential cost avoided per program through early adjustment |
| Predictive foresight | Static KRI reviews — risks identified weeks too late to resolve cost-effectively | Predictive risk signals 8–12 weeks in advance across all trials and CRO partners |
| Stakeholder visibility | Fragmented reports from multiple teams, no single source of truth | Unified portfolio view with real-time trial signals under expert oversight |
Outcomes
Early identification of failure probability in a Phase III program enabled leadership to re-scope, reallocate resources, and prevent significant late-stage development losses.
Late-phase trial risk detected 4–6 months earlier than traditional monitoring
$15M–$20M potential cost avoided through early portfolio adjustment and resource reallocation
30–40% faster portfolio decision cycles through centralized trial intelligence
8–12 weeks predictive risk foresight — governance and audit traceability maintained
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