Building a data foundation for GenAI in life sciences

The adoption of generative artificial intelligence (GenAI) in life sciences is accelerating rapidly. Yet despite the enthusiasm, impact remains uneven. A recent McKinsey survey found that while nearly all major life-science organizations had piloted GenAI, fewer than 5% had achieved consistent value at scale (McKinsey & Company, 2025).

Why is value so difficult to capture and grow? One of the clearest reasons is data. GenAI does not eliminate the need for high-quality, interoperable, well-governed data. Rather, it makes these foundations more critical. A model trained on fragmented, inconsistent, or poorly contextualized information will generate insights that are equally fragmented, inconsistent, or misleading. Many of the long-standing challenges that once slowed data integration, analytics, and discovery resurface more urgently the moment GenAI enters the workflow.

The sections below explore several essential themes that life-science organizations should consider when preparing their data and technology environments for scalable, reliable, and scientifically sound GenAI.

GenAI data quality in pharma R&D

Can GenAI make sense of unstructured data?

Not effectively without a strong data foundation.

Large language models can process vast amounts of text, but they cannot infer consistent meaning from data that is ambiguous, fragmented, or mislabeled. In life sciences, where data comes from diverse sources, such as clinical trials, omics, real-world evidence, and literature, lack of structure leads to noise rather than insight.

Key implications:

• Poor structure produces poor reasoning. LLMs generate fluent but potentially misleading answers if input data is inconsistent.
• Hidden silos persist. Without integration and harmonization, AI models reproduce the same blind spots that limit human analysis.
• Trust becomes fragile. Models may sound confident even when their reasoning rests on weak or conflicting evidence.

Creating FAIR data, that is data that is Findable, Accessible, Interoperable, and Reusable, remains one of the most reliable safeguard. Clear semantics, metadata, and standardized identifiers allow GenAI systems to connect meaning across domains, transforming fragmented information into validated knowledge.

Pharma data architecture requirements for GenAI

Does GenAI make data architecture less important for R&D organizations?

It actually makes it indispensable.

Traditional research platforms operate within fixed filters and structured workflows. GenAI, however, introduces open-ended exploration: users can ask complex, cross-domain questions that the system has never encountered before. This flexibility changes how data must be stored, queried, and connected.

Modern architectures need to handle dynamic, context-rich queries that combine multiple datasets in real time. They must support semantic indexing and graph-based relationships to ensure more meaningful retrieval. And they must remain modular enough to integrate emerging AI tools without breaking existing processes.

The result is a fundamental shift away from static data warehouses toward agile ecosystems that prioritize ontology alignment, metadata layers, and robust APIs. Investing in this foundation is not a detour from AI progress, rather it’s what makes AI reliable, explainable, and scalable.

GenAI user experience design for scientific and clinical workflows

Are natural-language interfaces the best way for scientists to use GenAI?

They improve accessibility but cannot replace structured exploration.

Conversational interfaces lower the barrier to entry for interacting with data, yet they do not automatically improve scientific productivity. In research and clinical settings, users rely on precision, reproducibility, and transparent control. A chat interface may simplify early-stage exploration but complicate validation or deep analysis.

Many scientists prefer structured interfaces such as filters, tables, and dashboards that allow them to inspect and manipulate data directly. Natural-language queries can obscure how results are generated or make refinement slower.

The most effective GenAI experiences combine both modes. Natural language supports discovery and ideation, while structured tools enable precise, traceable workflows. Productive friction, which can be seen as carefully designed steps that help users review sources or validate outputs, is often essential to maintain scientific rigor.

Trusted and explainable GenAI for drug discovery and development

How can life-science teams build trust and transparency into GenAI results?

By combining language models with structured knowledge.

Trust in GenAI depends on understanding why an answer is correct, not just what the answer is. In drug discovery, safety evaluation, and clinical development, explainability is essential because decisions are high-stakes and often regulatory-facing. A fluent answer is not enough, users need to see the logic behind it.

Knowledge graphs offer one of the most reliable paths to trustworthy AI. They encode relationships between entities using curated semantics. When GenAI systems draw on this structured context, they become significantly more accurate and more transparent in their reasoning.

A recent benchmark study demonstrated this clearly: combining knowledge graphs with large language models improved accuracy on domain-specific question-answering tasks from 16% to 54%, and accuracy increased to 72% when ontology-based validation checks were added. This shows that structured knowledge reduce hallucinations while fundamentally strengthening the reasoning layer that makes GenAI suitable for scientific use.

GenAI-driven knowledge discovery in pharma and biomedical research

Is GenAI simply an advanced search tool for biomedical data?

More than that, it redefines how users interact with knowledge.

Traditional search retrieves documents. GenAI synthesizes them. It can connect concepts across datasets, generate context-specific explanations, and produce new representations such as summaries, hypotheses, or visualizations.

This shift introduces new expectations and pressures. Queries now span data types and domains that older systems were never designed to connect, leading to performance bottlenecks. Real-time synthesis requires more flexible infrastructure. And users increasingly expect multimodal responses that adapt to the context of their question.

GenAI is not a smarter search bar. It is a new interface for scientific reasoning. One that requires data ecosystems built for semantic understanding, dynamic computation, and continuous adaptation.

How life sciences teams can accelerate real-world GenAI impact

The organizations that benefit most from GenAI will be those that invest first in data quality, semantic consistency, and modern architecture. Generative models amplify existing strengths and expose existing weaknesses in the data landscape.

FAIR data, ontology-driven integration, knowledge graphs, and modular architectures form the foundation of reliable, explainable GenAI in pharma. Companies that invest in these capabilities will unlock better decision support, faster insights, and more trustworthy automation.

Ultimately, the future of GenAI in life sciences will be determined not by the size of foundation models but by the intelligence of the data they rely on.