Why do most AI initiatives fail in pharma?
They stay isolated. A pilot without integration into enterprise workflows, data ecosystems, and governance frameworks will always stall. The technology works. The architecture around it usually doesn't.
Agentic AI for Pharma - Scaling to Enterprise Value
Most pharma agentic AI pilots never leave the lab. Decision Foundry helps pharmaceutical organizations transform isolated experiments into embedded, enterprise-grade AI- governed, compliant, and built to last.
Still stuck in pilot mode?
If any of these sound familiar, it's time to move beyond experimentation:
GenAI pilots deliver demos but don't integrate into core workflows.
Clinical, regulatory, and commercial data sits in fragmented silos.
Compliance and governance concerns are blocking adoption.
Data isn't unified or structured enough to support reliable AI outcomes.
Use cases are chosen for novelty, not business impact.
There's no clear path from experiment to enterprise-scale deployment.
Agentic AI for Pharma : Six Agents We Deploy
Regulatory Submission Agent
Automates the preparation and review of regulatory documents, faster, audit-ready, and compliant.
- Drafts and structures CTD/eCTD submission components aligned to FDA, EMA, and ICH guidelines
- Flags missing data, inconsistencies, and compliance gaps before submission
- Full audit trail and explainability controls on every document action
- Human oversight and escalation pathways built in from day one
Clinical Trial Intelligence Agent
Keeps trial execution on track by monitoring data, timelines, and patient recruitment in real time.
- Monitors patient recruitment against enrolment targets and flags site performance issues
- Detects protocol deviations early and synthesizes trial data into structured summaries
- Integrates across clinical, regulatory, and commercial data systems
- Human-in-the-loop escalation for critical trial decisions
Drug Discovery Copilot
Accelerates target identification and compound design with governed, evidence-grounded intelligence.
- Searches and synthesizes literature, assay, and compound data in seconds
- Surfaces target identification insights across internal and external datasets
- Contextual intelligence layer built on unified, AI-ready data foundations
- No analyst bottleneck, governed answers grounded in your own research data
Medical Affairs Knowledge Agent
Turns complex scientific content into accurate, compliant communication, at scale.
- Generates MSL briefing documents, scientific summaries, and HCP engagement content
- Synthesizes clinical evidence with governance controls ensuring on-label, compliant outputs
- Intelligent document processing embedded into existing medical review workflows
- AI-powered decision support built on unified scientific and regulatory data
Pharmacovigilance Monitoring Agent
Always-on safety signal detection across adverse event data, with full regulatory traceability.
- Monitors incoming adverse event reports in real time across all data sources
- Flags safety signals and clusters for medical review with automated case narratives
- Governance frameworks aligned to GxP, FDA, and EMA requirements
- Ongoing compliance monitoring and model validation built into the production architecture
Commercial Intelligence Agent
Real-time insights on market performance, HCP engagement, and patient access, without the analyst wait.
- Tracks brand performance, competitive signals, and HCP engagement trends daily
- Generates territory and account-level summaries automatically from CRM and claims data
- Personalised patient engagement insights delivered through existing commercial platforms
- Connects to unified data layer spanning clinical, regulatory, and commercial systems
Our Process
Agentic AI projects fail when they skip the foundations. Our approach prevents this, moving fast without cutting corners.
1
Step 1: Identify High-Impact Opportunities
We align Agentic AI use cases with your business priorities across the pharma value chain. We avoid technology-first thinking. Every use case is evaluated against a measurable business outcome.
2
Step 2: Build Data Foundations
We unify and prepare your enterprise data before any model deploys. Clinical, regulatory, and commercial data moves into a governed, AI-ready layer.
3
Step 3: Design Scalable Architectures
We create interoperable systems. These integrate Agentic AI into existing workflows. We avoid parallel systems that become unused after the pilot.
4
Step 4: Implement & Integrate AI
We deploy Agentic AI solutions embedded within your regulatory, medical, and commercial processes. These include full audit trails, explainability controls, and governance guardrails.
5
Step 5: Govern & Optimise
We monitor performance, ensure ongoing compliance, and continuously improve outcomes. AI in production requires continuous management.
Why Decision Foundry
We make AI work at scale, not just in demos. Data foundations, governance, and workflow integration are the starting point, not something we add later.
Deep expertise in regulated industries.
GxP, FDA, and EMA requirements shape our architecture decisions from day one, not after the first audit finding.
Data foundations first. Every time.
An AI agent is only as reliable as the data it reasons over. Every engagement begins with unifying and governing your enterprise data before a single agent is deployed.
Governance built in, not bolted on.
Audit trails, explainability controls, human-in-the-loop design, and compliance monitoring from the start.
A proven path from pilot to enterprise adoption.
We build the architecture, integration layer, and change management plan that gets Agentic AI embedded into how your organisation actually works.
Experience across the full pharma value chain.
R&D, clinical development, regulatory affairs, scientific engagement, and commercial, we've deployed AI solutions across all of them.
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Ready to move from AI experiments to intelligent pharma?
Book a 30-minute call. We'll look at your current data environment and AI ambitions, identify your highest-impact use case, and give you a clear picture of what moving from pilot to production looks like timeline, architecture, and what you get on the other side.
Book a Free Agentic AI AssessmentFrequently Asked Questions
What does it actually take to scale Agentic AI?
Four things: data readiness, interoperable architecture, use cases aligned to business outcomes, and governance that satisfies regulatory requirements. Most organisations focus on one or two. All four are needed.
Is Agentic AI compliant with pharma regulations?
Yes , when implemented with proper governance, auditability, and human oversight. The platform isn't a compliance risk. How it's implemented is.
Where should pharma companies start?
High-impact, knowledge-intensive workflows: regulatory documentation, clinical trial reporting, scientific synthesis. Build a strong data foundation first, then scale to other use cases.