2026-07-14

The pharmaceutical industry’s relationship with data has evolved significantly over the past 20 years. Clinical trial records, Electronic Health Records (EHRs) and regulatory submissions now exist at an unprecedented scale. By some estimates, a typical pharma organisation creates ~1 billion data points and processes roughly ~400,000 documents annually. Yet, the core metrics of drug development remains largely unchanged. Phase 1 success rates have not improved. Regulatory submissions continue to face significant delays. Patient recruitment remains the leading cause of trial timeline failures.

The industry’s default response has been to demand better data, expanded sources, improved collection, and stronger interoperability. However, this overlooks a critical reality: data availability is rarely the constraint. Despite this immense volume, 80 percent of collected pharma data is uncurated and remains entirely unused, representing the lowest ROI of any data initiative. Furthermore, less than 10 percent of external data is ever reused. The more consequential challenge lies in what happens after data is collected, whether it reaches the right people, in usable form, at the moment decisions need to be made.

The Gap Between Data and Action
Pharmaceutical organisations operate on vast volumes of structured and unstructured data, including clinical reports, regulatory submissions, Adverse Event (AE) records, and real-world evidence. The issue is not data availability, but fragmentation, inconsistent formatting, and limited accessibility at speed.

The root of this ‘decision problem’ stems from structural bottlenecks, ‘dirty data,’ and a complex ‘technology jigsaw’. Knowledge workers are often burdened by manual, multi-step workflows scattered across siloed tools, making it exceptionally difficult to coordinate research or find actionable information. As a result, insights are delayed, and decision-making remains largely reactive.

Fewer than 20 percent of organisations believe they effectively use data products for key business decisions. Instead, critical decisions in clinical workflows and patient care continue to rely on heuristics rather than data-driven insights.

This gap becomes evident in regulatory submissions. While the required information exists, most organisations lack systems to quickly retrieve, synthesise, and present it in a usable format, leading to significant delays.

A similar challenge exists in drug labeling, where thousands of documents require precise extraction of product attributes. Manual processing is slow, inconsistent, and operationally intensive, creating risks in compliance and resource allocationat scale.

Where the Technology Gap Actually Sits
Natural Language Processing (NLP) and Named Entity Recognition (NER) have matured to accurately extract, classify, and contextualise information from regulatory and clinical documents. Their value in pharmaceutical workflows is practical and measurable.

In drug labeling, domain-trained NER models can automate the classification of product attributes and populate document management systems, significantly reducing processing time while producing consistent, structured, and auditable outputs.

In regulatory intelligence, domain-trained Large Language Models (LLMs) can analyse prior submissions, identify relevant content for health authority queries, and generate structured draft responses for expert review. These systems augment regulatory professionals by eliminating the time-intensive while producing consistent, structured, and auditable outputs burden of data retrieval and synthesis, allowing them to focus on judgment and validation.

Pharmacovigilance presents a similar opportunity. AE data from clinical trials, literature and post-market surveillance can be processed at scale using NLP systems trained on domain-specific terminologies such as Medical Dictionary for Regulatory Activities (MedDRA). This enables consistent entity recognition, efficient signal detection, and seamless integration with safety databases, improving both speed and compliance in reporting workflows.

The Real-World Evidence Problem
Real-World Evidence (RWE) is now a substantive input for regulatory decisions, not just supplementary. The challenge for pharma is not accessing Real-World Data (RWD), but curating and structuring heterogeneous sources (Electronic Health Records (EHRs), claims, lab, wearable) to a regulatory-grade standard. Integrating these disparate datasets reliably requires systematic data harmonisation, and rigorous normalisation of terminology and formats to enable tractable downstream analysis.

This is where the decision problem becomes most visible in the context of RWE. Organisations that have invested in RWE programs often find that their data pipelines produce outputs that are technically available, but practically difficult to act on quickly. The analysis exists. The decision infrastructure to route it into trial design, label updates, or regulatory submissions in a timely manner often does not.

Why AI Implementations Frequently Fall Short
Artificial Intelligence’s (AI) potential in pharmaceutical work flows is clear, yet realised value remains limited. Most initiatives fail to scale beyond pilots due to fragmented data, weak integration with operational workflows, and limited domain validation in regulated environments.

To move from simply accumulating data to driving actionable, data-led decisions, pharmaceutical organisations are increasingly adopting end-to-end AI operationalisation frameworks. This begins with robust data curation and engineering, where fragmented and latent data must be transformed into structured, analysis-ready pipelines supported by strong data architecture for efficient processing and accessibility. Building on this foundation, insights and analytics play a critical role in translating complex data signals into clear, decision-ready intelligence through Business Intelligence (BI) tools, reporting systems, and advanced analytical models.

Equally important is AI design and deployment, which requires seamless integration into enterprise systems, supported by rigorous testing, validation, and continuous monitoring to ensure reliability and compliance. To maintain accuracy and regulatory alignment, expert-in-the-loop operations are embedded within workflows, enabling domain specialists to guide, validate, and refine AI-driven outputs. Finally, product engineering and broader technology transformation are essential to redesign enterprise systems, workflows, and user interfaces so that AI-driven insights are seamlessly embedded into everyday decision-making processes. Together, this integrated approach enables AI to move beyond isolated pilot initiatives and deliver scalable, reliable, and decision-centric impact across pharmaceutical operations.

The Decision Infrastructure that is Missing
What pharmaceutical organisations need is not more data, but infrastructure that makes existing data usable at the point of decision. This includes domain-trained models aligned to pharmaceutical workflows, seamless integration with operational systems, and expert-in-loop processes that ensure accuracy without reintroducing manual bottlenecks.

To bridge the gap between hoarding data and making decisions, there is a critical need to unlock the potential of ‘dark data’ to improve discoverability and drive actionable insights. By implementing unified data platforms and AI-driven workflows, organisations can democratise access, reduce time to insight by up to 75 percent, and enable faster, evidence-based decisions.

The impact is measurable. Faster regulatory submissions reduce query cycles, automated labeling improves consistency and efficiency, and scaled adverse event processing strengthens signal detection and reporting timelines.