The pharmaceutical industry has spent a decade investing in AI. Genomic sequencing, clinical trial matching, intelligent supply chain and real-world evidence engines. The use cases are well-defined and the business case is clear. But across boardrooms from Basel to Boston, the same question keeps surfacing: why isn't it working at scale?The answer isn't the AI. It’s the data underneath it.

Ask a life sciences CDO what keeps them up at night and you'll hear a common story:

• Their organization runs dozens of AI pilots 
• A few deliver results in controlled settings 
• Almost none make it to full deployment 

But when they trace the failures back, the root cause is almost always fragmented, ungoverned, untrustworthy data.

Clinical trial data lives in LIMS systems that haven't been integrated with commercial CRM in years. HCP records are duplicated across six systems with six different address formats. Product safety signals are refreshed on different schedules in different markets. Genomic and RWE data sit in cloud environments that are technically accessible but practically siloed.

AI agents are only as good as the context they're given. For example, an agent making a trial recruitment decision based on a stale safety signal, a second agent generating a Medical Information Request response from conflicting product records and another agent recommending a supply reorder based on duplicated inventory data are not AI failures. They're data failures, which, in a regulated industry, carry consequences spanning patient safety, regulatory violations, and billion-dollar liabilities.

This is the fragmentation crisis and it's the central challenge to solve before all others facing any serious agentic AI strategy in life sciences.

A trusted context is the governed data infrastructure enabling AI agents to act with confidence, providing not just access to data, but verified, unified, real-time data that reflects a single source of truth. It has three components:

1. A Golden Record. Every entity an agent needs to reason about — a patient, an HCP, a trial participant, a chemical compound, a product — has a single authoritative golden record that resolves duplicates, reconciles conflicting attributes and survives M&A integrations and patent cliffs. This is master data management. Without it, agents are reasoning over noise.

2. Automated governance. In a regulated industry, data doesn't just need to be accurate, it needs to be traceable, protected and compliant. PII and PHI must be masked before they reach an LLM. GxP audit trails must be maintained. IDMP and SPOR submissions must be generated from the same data that powers clinical workflows. Governance has to be enforced at the data layer rather than attached after the fact.

3. Real-time connectivity. A golden record that was accurate last quarter is dangerous today. Trusted context requires live signals — supply chain shifts, updated prescribing patterns, new safety data — feeding continuously into the unified foundation. Static data warehouses don't power agentic workflows. Living data pipelines do.

This is what Informatica IDMC provides: the Trusted Context layer that transforms fragmented enterprise data into the governed foundation that AI actually needs to operate in high-stakes environments.

The modern agentic life sciences enterprise runs on three interconnected layers.

Informatica IDMC is the execution layer: the enterprise's shared vocabulary and verified map of every clinical, commercial and operational asset. It ingests data from the 70% of R&D information still trapped in legacy systems: Veeva, SAP, mainframes, LIMS, IQVIA. CLAIRE® AI profiles and governs pharmaceutical data assets automatically. Agentic Multidomain MDM creates and maintains golden records for HCPs, patients, trial participants and products across global organizational boundaries.

Salesforce Data 360 is the harmonization layer: it synthesizes behavioral and engagement data with the golden record to make unified context available to agents without requiring data movement or duplication.

Agentforce 360 is the orchestration layer: It’s the brain that executes clinical and commercial workflows,but it only performs at the level of the context it's given. Ground it in trusted data and it becomes a reliable enterprise capability. Ground it in fragmented, ungoverned data and it becomes a liability.

The differentiator is the data layer. Competitors in this space rely on vector search,  a retrieval approach that surfaces plausible-looking answers from multiple siloed sources without resolving conflicts or establishing ground truth. This strategy fails in regulated environments because it cannot distinguish between a current safety signal and an outdated one, or between an active HCP record and a duplicate. Trusted Context, by contrast, resolves all of that before the agent ever acts.

1. Autonomous Trial Recruitment

Trial recruitment is one of the most expensive and time-consuming bottlenecks in drug development and one of the highest-value targets for agentic AI. Agents that continuously scan unified real-world evidence, EHR data and trial eligibility criteria can then match participants to trials in real-time, eliminating manual screening and compressing timelines from months to weeks

But this only works if the underlying patient and clinical data is clean, unified and current. An agent reasoning over fragmented records — different identifiers, missing social determinants, stale safety data — will surface the wrong candidates, miss eligible participants or trigger a regulatory audit. Informatica IDMC's Patient 360 golden record, built from harmonized clinical and RWE data, is what makes autonomous trial recruitment viable at scale.

2. Medical Information Requests (MIR)

HCPs submit thousands of MIRs (complex, time-sensitive questions about drug interactions, dosing, contraindications and, and off-label use) every year. Each response must be accurate, GxP-compliant and traceable. This is mostly handled manually today, with turnaround times eroding HCP trust and commercial effectiveness.

Agentforce agents can respond to MIRs instantly and compliantly, if grounded in a unified product data foundation that reflects the latest research, safety signals and label updates. Informatica IDMC's product golden record, connected to the regulatory submission pipeline, is the infrastructure that makes GxP-compliant, automated MIR response possible.

3. Global Order and Supply Management

Supply chain disruptions in life sciences, resulting in shortages of critical therapies, delayed manufacturing runs, regulatory hold risks, have direct patient consequences. Agents that monitor inventory can predict shortages and autonomously trigger reorder workflows that dramatically reduce these risks. But they require a live, unified view of global supply chain data that current environments rarely provide.

IDMC's connectivity layer, consists of 10,000+ connectors to legacy ERPs, SAP and supply chain systems and feeds real-time supply signals into the golden record for products and logistics. Agents operating on this foundation can act on current data, not yesterday's snapshot.

4. Regulatory Submissions

IDMP, SPOR, FDA submissions and EMA filings create a regulatory submission process that is among the most data-intensive, error-prone workflows in the enterprise. Manual preparation, inconsistent product data across markets and fragmented documentation trails cause submission delays and cost organizations months and millions.

Informatica IDMC automates SPOR/IDMP compliance from the above, reducing manual regulatory submission burden by up to 40%. By maintaining a single, auditable product record that feeds directly into submission workflows, organizations move from "compliance as overhead" to compliance as a competitive accelerator.

5. HCP Field Engagement

Whether it’s field reps making next-best-action recommendations, medical science liaisons delivering targeted clinical content or digital engagement platforms personalizing outreach, they all depend on an accurate, current 360-degree view of the HCP that's reconciled across every system the organization touches.

The "HCP Identity Problem" refers to duplicate records, mismatched affiliations, stale specialty and licensing data and is one of the most persistent data quality failures in commercial pharma. Informatica IDMC's HCP golden record solves this issue, giving Agentforce the clean identity foundation it needs to power personalized field engagement at scale.

Gilead Sciences deployed Informatica for product MDM and IDMP compliance, transforming how product data is managed across global markets and creating the foundation for regulatory submission automation. The project established a single authoritative product record that now serves as the trusted anchor for commercial and regulatory workflows.

LabCorp implemented Informatica Data Quality and Governance capabilities to create the reliable data infrastructure required for clinical workflows at enterprise scale — addressing the data observability and compliance requirements that AI-driven operations demand.

Takeda Pharmaceutical deployed multi-domain MDM to unify patient, HCP and product records across a complex global organization. This project then became the foundation for post-M&A data integration and the agentic use cases now being built on top of it.

These aren't pilot projects. They're production-grade, enterprise-scale deployments of the exact data foundation the agentic enterprise requires.

One of the persistent friction points in enterprise data infrastructure is the integration overhead. Custom connectors, proprietary pipelines and governance tools that live outside the systems they're supposed to govern each adds cost, latency and risk.

Informatica IDMC's headless architecture eliminates three categories of that overhead:

1. Zero Copy. Data doesn't have to move to be governed. IDMC applies MDM, quality and governance capabilities directly where data lives — in Snowflake, Databricks or Data Cloud — without duplication or migration risk.
2. Zero Custom Connectors. 10,000+ pre-built connectors cover the full range of life sciences environments, from legacy mainframes and Veeva to modern cloud platforms. No custom integration work required.
3. Zero Governance Gaps. Governance is enforced at the data layer, not the application layer. That means PHI masking, audit trails and compliance controls are in place before data ever reaches an agent or an LLM — regardless of which application accesses it.

For organizations running complex, multi-cloud, post-M&A environments — which describes most large pharma and biotech companies — this architecture dramatically reduces the time and cost of getting to a production-ready agentic foundation.

The data fragmentation crisis feels intractable because organizations try to solve it everywhere at once. The pragmatic path is different: pick the highest-value use case, build the trusted data foundation for that use case and extend from there.

Just 90 days is all it takes to:

• Assess the current state of MDM and data quality across the target domain (HCP, Patient, or Product)
• Identify the specific data gaps blocking the highest-priority agentic use case
• Deploy Informatica IDMC to unify and govern the relevant data domain
• Connect to Agentforce 360 and demonstrate the use case in production

The organizations that win in the agentic era won't be the ones with the most sophisticated AI models. They'll be the ones with the most trusted data foundation. That's the durable competitive advantage and it's available now.

Ready to move from fragmented data to agentic enterprise?

Download the Agentic Life Sciences Enterprise Solution Brief to see the full architecture, use cases and customer proof.

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