Fragmented Pharmaceuticals: Why Indian Biopharma Does Not Have AI-Ready Data
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Fragmented Pharmaceuticals: Why Indian Biopharma Does Not Have AI-Ready Data
In life sciences, data silos are more than an IT problem - they’re a strategic liability. Most healthcare and life sciences data today is fragmented, poorly standardized and designed for billing or compliance rather than machine learning, which directly undermines AI accuracy, explainability and clinical trust.
As AI increasingly supports clinical decisions, drug discovery, trial optimization and manufacturing quality, regulators now expect clear data lineage, context and auditability to establish model credibility and compliance.
Organizations that invest in AI-ready data - clean, connected, well-governed and semantically consistent -are able to scale AI safely and repeatedly across R&D, care delivery and operations, while those that do not remain stuck in pilots, exposed to regulatory risk and unable to translate AI ambition into real-world impact.
Organizations need to work with data that is findable, accessible, interoperable and reusable, underpinned by solid metadata, lineage and quality controls. “Without these foundations, AI models risk producing incomplete insights or compounding the inconsistencies already present in the data,” Greg Killian, SVP, Head of Business, Lifesciences and Healthcare at IT services company EPAM Systems, notes.
According to Grand View Research, the global clinical trials market size was estimated at $84.54 billion in 2024 and is projected to reach $158.41 billion by 2033, growing at a CAGR of 7.5% from 2025 to 2033.
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