Governed AI Execution for Pharma R&D Leaders
Embed governed AI agents across R&D to accelerate decisions and de-risk pipelines.
A Day in the Life of a Head of Pharma R&D
The portfolio review is tomorrow. Three programs are advancing across two therapeutic areas. A regulatory question landed overnight. A vendor escalation is sitting in the inbox. AI pilots have been running for over a year — none have reached validated production use.
The science is intact. The science isn't the constraint. Execution capacity, governance and consistency across the portfolio are.
Maxis AI moves AI from isolated pilots into governed, measurable execution — across the R&D lifecycle.
Human-in-the-loop validation · Audit traceability · Governed execution
The Pressures R&D Leaders Carry Every Day
Pilots Without Production
AI pilots demonstrate insight in isolation but rarely reach validated, governed execution across the portfolio.
Inconsistent Execution Across TAs
Execution varies across therapeutic areas and CROs — leadership absorbs the gap through escalation and rework.
Governance Without Throughput
Governance demands continue to rise — without a model that converts oversight into operational throughput.
R&D needs an operating model that scales without diluting governance.
Industry Reality
- Protocol complexity rises across modalities and therapeutic areas.
- Global site networks expand and multiply coordination load.
- Data volume across clinical systems exceeds review capacity.
- Regulatory oversight tightens across regions.
- Workforce constraints persist; growth tied to headcount is unsustainable.
R&D leadership is being asked to deliver more programs, faster, with the same governance bar.
How Maxis AI Is Built for Pharma R&D
Maxis AI operationalizes AI inside live, regulated R&D workflows — moving from isolated pilots to governed, measurable execution. Supervised agents execute across study design, conduct, data and reporting.
Outcomes are tied to R&D KPIs — cycle time, capacity, predictability — and remain inspection-ready under GxP, 21 CFR Part 11 and ICH-GCP.
From Pain to Outcome: How Maxis AI Works for You
Pain Point
AI Capability
Outcome
AI pilots stuck below production threshold.
Production-grade supervised execution layer.
AI deployed across programs under real audit conditions.
Inconsistent execution across therapeutic areas.
Standardized execution patterns across programs.
Predictable execution across the portfolio.
Manual coordination across functions and vendors.
Cross-functional agent workflows with audit trails.
Reduced coordination overhead and escalation load.
Reactive risk detection after slippage.
Continuous monitoring with structured intervention.
Earlier detection with governed, repeatable response.
Limited portfolio-level execution visibility.
Unified oversight across studies and therapeutic areas.
Portfolio-level visibility tied to R&D KPIs.
Detected Late-Phase Trial Risk 4–6 Months Earlier, Avoiding ~$20M Portfolio Loss
Explore how a global pharma R&D leader improved portfolio visibility and reduced late-stage risk across Phase II–III programs.
3
TAs Standardized
2.5x
Execution Throughput
100%
Audit Traceability
0
System Replacements
All You Need to Know
It operates as a supervised execution layer aligned to GxP, 21 CFR Part 11 and ICH-GCP, with audit traceability across every action.
No. Maxis AI integrates with existing EDC, CTMS, eTMF, safety and imaging platforms.
Reviewers retain approval authority through defined supervision checkpoints across every governed workflow.
Startup predictability, query throughput and portfolio-level oversight.
Yes. Agents are configurable across therapeutic areas and trial phases.
Explore the Agentic AI Platform.
See how AI agents are transforming study startup, data management, oversight, and regulatory submissions.