The life sciences and pharmaceutical sectors are evolving with the influence of AI, especially generative AI, which is opening up new approaches to research, clinical trials, and patient engagement. Generative AI promises substantial value in the life sciences and pharmaceutical industries, with McKinsey estimating an annual economic impact of over $60 billion. This largely arises from three business-critical functions: R&D, manufacturing and supply chain, and commercial operations.

Let’s take a closer look at how AI can support these three core areas:

• Accelerating R&D, Drug Discovery, and Clinical Development: One of the most promising applications of AI in life sciences and pharmaceuticals is accelerating drug discovery. AI enables faster identification of drug candidates by analyzing vast datasets and leveraging biological insights. This process can streamline clinical trials, optimize patient selection, and predict outcomes, ultimately bringing treatments to market faster by decreasing real-world iterations and at reduced costs. Similarly, automation can also streamline regulatory filings, expediting approval processes for new therapies.
• Manufacturing and Supply Chain Optimization: Within manufacturing and supply chain management, AI’s capabilities in predictive analytics allow companies to optimize lead times, minimize costs, and improve product traceability. By integrating AI with supply chain operations, pharmaceutical companies can reduce inefficiencies, streamline operations, and enhance product quality control.
• Enhanced Commercial Operations: Generative AI enhances commercial operations by enabling insight generation, targeted content creation, and interactive tools like chatbots for patient and provider engagement. It supports precision medicine by tailoring treatments to individual needs, improving patient experiences in areas like oncology and genetic disorders. By understanding and anticipating patient and healthcare provider needs, AI drives targeted marketing, improves engagement, and delivers personalized care, significantly improving outcomes in critical therapeutic areas.

Data-Centric Challenges in Life Sciences & Pharma 

While AI-driven innovation holds great promise for the life sciences and pharmaceuticals industry, it faces significant challenges, particularly around ensuring data quality, governance, and compliance with tightening regulations. For AI to truly deliver its potential benefits, organizations must rely on high-quality, reliable data as the essential foundation for developing effective AI driven solutions. Without robust data management practices and adherence to regulatory standards, the full impact of AI in life sciences and pharmaceuticals cannot be realized.

Here are some of the primary data challenges facing the life sciences and pharmaceutical industries today:

• Data Silos and Fragmentation: Life sciences and pharmaceutical companies often manage complex ecosystems comprising multiple business units, each with its own data management systems and processes. This fragmentation leads to data silos, where information is isolated within specific departments or systems, making it difficult to achieve a unified view. Siloed data can slow down innovation, as research and analytics teams lack the consolidated datasets needed for comprehensive analysis.
• Data Quality and Consistency: For AI models to produce reliable outcomes, data quality is essential. Poor data quality—caused by incomplete, inconsistent, or outdated information—can lead to biased or inaccurate AI models. This is particularly problematic in life sciences and pharmaceuticals, where erroneous data could impact patient outcomes, regulatory compliance, and drug efficacy.
• Data Privacy and Compliance: New regulations, such as the EU AI Act, introduce stringent requirements around AI model transparency and data governance. Failure to comply with these regulations can result in significant fines and reputational damage, making compliance a critical challenge.
• Data Accessibility and Usability: Inconsistent data definitions, redundant data, and accessibility issues can limit the utility of data in AI applications. Life sciences and pharmaceutical organizations may need to integrate data from varied sources—including patient health records, clinical trial data, and external databases—but differing formats and standards can complicate this integration. Without unified definitions and easy access, data’s full potential cannot be realized, limiting its utility in AI and analytics.

5 Key Strategies to Tackle Data Challenges in Life Sciences and Pharma

To address the challenges outlined above, organizations need a modern strategy for data quality, observability, and governance, ensuring that data is accurate, reliable, and compliant with regulations. Here are 5 key steps life sciences and pharmaceutical organizations can take to overcome these challenges:

1. Implementing a Centralized Data Hub

A centralized data repository consolidates both internal and external data sources, addressing diverse user needs and breaking down data silos. By cataloging all datasets in one accessible location, it creates a single source of truth, enhancing data accessibility and supporting analytics and AI applications. This approach streamlines collaboration, improves decision-making, and fosters an integrated data ecosystem across departments and business units.

Centralization also strengthens compliance by ensuring uniform application of data access and privacy policies. Proper documentation and traceability of data provenance are maintained, reducing risks associated with regulatory violations. Frameworks like medallion architecture allow organizations to progressively clean, classify, and organize data, laying the foundation for a scalable and trustworthy data environment.

2. Establishing Rigorous Data Quality with Real-Time Observability

High-quality data is crucial for the success of AI and business initiatives. Organizations should implement real-time data monitoring and automated quality checks to ensure accuracy, completeness, and timeliness. These assessments identify and address issues proactively, reducing risks of degraded AI model performance. Automated evaluations verify data fitness while access controls protect sensitive information, ensuring compliance with regulatory standards.

Observability tools further enhance data quality by detecting anomalies and monitoring trends over time. Embedding fitness-for-purpose scoring within data pipelines ensures datasets align with specific business and regulatory needs. These practices collectively support ethical AI applications, maintain trust, and improve outcomes in critical areas like patient care and drug development.

3. Enhancing Data Governance for Compliance

As privacy and AI regulations evolve, data governance must be integrated into every facet of data management. Data governance frameworks enforce data usage policies, track data lineage, and protect sensitive information like personally identifiable information (PII) and protected health information (PHI). Automating data discovery and classification helps companies meet regulatory requirements while policy-based access controls restrict data usage to authorized roles, reducing compliance risks.

Integrating AI governance into data management frameworks is critical for managing model risk and ensuring accountability. With increasing regulatory scrutiny, AI governance should be embedded to track model lineage, document data transformations, and assign accountability for data and model accuracy. This supports compliance and enhances decision-making by providing clear traceability from data inputs to AI outputs.

4. Designing Data Systems with Scalability in Mind

As the regulatory environment and data volumes grow, life sciences and pharmaceutical companies need data systems that are adaptable and scalable. The presenters recommended a modular, scalable architecture where data quality and governance tools can be easily expanded as business needs evolve.

Future-proofing data systems ensures they remain compliant and efficient even as datasets become more extensive and intricate. Scalable systems are vital for maintaining data integrity and meeting regulatory standards, allowing life sciences and pharmaceutical organizations to innovate sustainably while addressing industry growth and technological advancements.

5. Promoting Data Accessibility and Usability

Standardizing data definitions, integrating semantic layers, and creating searchable metadata repositories make data more accessible and meaningful. This approach enables non-technical users to discover, understand, and leverage data independently, fostering a data-driven culture and encouraging cross-departmental collaboration.

Semantic layers add business context to data, enhancing its usability and promoting collaboration across teams. By using data catalogs and user-friendly platforms, organizations empower all team members, not just technical experts, to access trusted data. This reduces reliance on data engineering teams and supports AI initiatives, driving innovation and informed decision-making.

A Unified Data Management Solution: How Wipro, Alation, and DQLabs Work Together 

By integrating Wipro’s frameworks, Alation’s data intelligence platform, and DQLabs’ data quality tools, life sciences and pharmaceutical organizations can establish a robust, AI-ready data environment that meets stringent industry standards. This unified approach addresses the entire data lifecycle, from ingestion to AI model deployment. 

Wipro acts as a systems integrator, bringing together disparate technologies to form a cohesive solution. Wipro provides a structured framework that lays out foundational elements necessary for life sciences and pharmaceutical companies to leverage AI effectively and responsibly. Alation contributes robust metadata management and AI governance tools, enabling centralized cataloging, data discovery, and privacy features. This helps life sciences and pharmaceutical  companies manage sensitive data while meeting regulatory standards like GDPR and HIPAA. DQLabs complements this with its four-step data quality and observability framework, ensuring high-quality, bias-free data through proactive monitoring and remediation of data issues. Integration between Alation’s cataloging and DQLabs’ quality metrics enriches data with quality scores, enabling stakeholders to trust and reliably access data across AI applications. 

Together, these three partners create a seamless data management and governance solution as seen in Fig.1, enabling life sciences and pharmaceutical organizations to leverage AI responsibly and effectively while maintaining stringent compliance and data privacy standards.

Fig.1: How Wipro, Alation and DQLabs work together 

The recent Alation-hosted webinar, featuring experts from Wipro, DQLabs, and Alation, explored these topics, focusing on best practices for establishing data trust in AI. Watch this on-demand webinar now.

Next Steps in Preparing for the Future of AI in Life Sciences & Pharmaceuticals

The life sciences and pharmaceutical industries stand on the brink of an AI-driven transformation. To fully capitalize on AI’s potential, organizations must establish a strong foundation of data trust and governance. By prioritizing data intelligence, quality, and compliance, life sciences and pharmaceutical companies can prepare to use AI safely and responsibly—enhancing research, patient care, and overall operational efficiency. Discover how DQLabs’ Data Quality and Data Observability Platform, along with Alation’s Data Intelligence Platform, can help you excel as you navigate the AI landscape.