"The pharmaceutical industry stands at an inflection point where data architecture decisions made today will determine whether organizations can deploy AI at scale or remain constrained by technical debt for another decade," says Sujit Murumkar, a visionary leader who designed and implemented enterprise scale modern data ecosystems for one of the largest pharmaceutical organization, describing the digital transformation facing life sciences companies worldwide.
For nearly two decades, enterprise data systems in life sciences have operated on infrastructure built for an earlier era of computing. Legacy platforms, designed primarily for transactional processing and static reporting, now strain under demands for real time analytics, machine learning model deployment, and integration of vast datasets spanning clinical research, genomics, commercial operations, and patient programs. The gap between what these systems deliver and what modern drug development requires has widened into a structural constraint that affects how quickly therapies reach patients.
The Acceleration of AI in Pharma
The AI in pharmaceutical market stood at $1.94 billion in 2025 and analysts project growth to $16.49 billion by 2034, implying a compound annual growth rate of about 27 percent as companies modernize research, development, and commercial functions. North America accounts for roughly a third of this market today, yet Asia Pacific is expected to expand at the fastest pace as regional firms invest heavily in cloud infrastructure and advanced analytics. These trends signal that competitive advantage will increasingly depend on technical execution rather than geographic presence alone.
Digital transformation in life sciences more broadly has become a priority budget item, with cloud platforms and data integration tools receiving the bulk of new investment. Executives view these initiatives not as optional modernization but as prerequisite infrastructure for AI at scale. The shift is visible in organizational charts as well, where chief data officers and heads of enterprise architecture now participate directly in commercial and medical strategy discussions.
From Data Warehouse to Data Products
Murumkar's career traces the evolution of this architectural shift. Between 2020 and 2024 he led the migration of legacy analytics platforms to a cloud native environment, enabling large scale deployment of machine learning models that supported global sales and marketing for one of the largest Pharma organisations. The work contributed to a 15 percent improvement in targeting and segmentation efficiency, demonstrating how infrastructure upgrades translate into measurable commercial outcomes.
"We were not just lifting and shifting systems," he explains. "We had to redesign how data flows end to end, from ingestion through curation to consumption, and embed governance so that every analytical output could be trusted by commercial and medical teams." A central element of the effort involved defining global master data models for customer, patient, and brand domains, creating consistent reference structures that allowed analytics to operate reliably across dozens of markets with varying regulatory constraints.
These initiatives informed what he later termed a modular commercial data product framework. Instead of a single monolithic warehouse, the framework organizes data into domain specific products that package curated datasets, quality rules, lineage information, and access controls together. Each product serves a focused business purpose, such as omnichannel engagement analytics or field force performance, while conforming to common governance standards.
Governance as Engineering
Data governance has emerged as one of the most difficult aspects of AI enablement in life sciences. Regulatory expectations for data quality and integrity continue to rise, reflecting concerns about unauthorized access, incomplete audit trails, and inconsistent handling of source systems. An industry analysis of data governance in life sciences highlighted how complex multi source data flows can introduce errors and compliance risk when organizations lack robust lineage tracking and standardized controls.
Murumkar argues that modern architecture can address these issues more effectively than legacy systems if designed correctly. "Older environments made it nearly impossible to trace how data moved across systems or who touched it at each stage," he says. "With well designed cloud platforms, you can encode policies directly into pipelines, audit every transformation, and automate checks that used to depend on manual review." His work integrates AI driven data quality checks and metadata management directly into data products, turning governance into an operational capability rather than a separate oversight process.
The AI enabled insights orchestration model he developed uses shared master data and reusable feature stores to support analytics across sales, marketing, market access, and patient services. By standardizing inputs and definitions, the model reduces repetitive engineering work and shortens model deployment cycles. Teams can reuse certified features, such as physician level propensity scores or channel engagement indicators, instead of rebuilding them for each new use case.
A Critical Perspective on Risk
Some observers question whether the rush to modernize has outpaced institutions' ability to manage risk. Dr. Elena Martinez, a former regulatory affairs director who now advises on AI ethics in healthcare, offers a skeptical view. "There is a real danger that organizations will deploy sophisticated models on top of poorly understood data," she says. "If the training sets underrepresent certain patient groups or combine sources with unresolved quality issues, the resulting recommendations can reinforce bias or obscure safety concerns."
Martinez points to recent work on AI in clinical trials that documents both progress and pitfalls in the use of machine learning for trial design and patient selection, including unresolved questions about transparency and validation across diverse populations. She argues that commercial analytics can encounter similar challenges when models drive decisions about resource allocation and engagement strategies. "The more you operationalize AI, the more important it becomes to demonstrate why a model outputs what it does, particularly in regulated environments," she notes.
Her critique underscores tension between speed and rigor in digital transformation. Commercial teams seek faster insights and real time decision support, while regulators and ethicists emphasize the need for validation, explainability, and clear lines of accountability. Bridging this divide requires both technical controls and organizational discipline.
Lessons from Large Scale Implementation
Murumkar's experience spans industries that demand precision and reliability. Earlier in his career he helped architect migration from a traditional enterprise data warehouse to a distributed data lake for a global life sciences organization, using technologies like Spark and Hive to handle large volumes of heterogeneous data. He also led integration of analytics platforms following a major merger, unifying new product portfolios into existing reporting environments without interrupting executive dashboards.
Outside life sciences he designed high performance reporting applications and credit risk dashboards for a multi billion dollar fixed income portfolio managed by one of the world's largest investment funds. The systems delivered near instant views of exposure and risk, providing decision makers with timely information during periods of market volatility. The common thread across these projects is an emphasis on building infrastructure that supports high frequency, high stakes decisions.
Today, as he prepares to present his methodologies at technical conferences and contribute to industry discussion through papers and panels, Murumkar returns repeatedly to a central theme. "The question is no longer whether life sciences organizations should adopt AI," he says. "The real question is whether they can build the data and governance foundations that make AI reliable enough for decisions that affect patient access and public health."
He views the move from legacy systems to AI ready ecosystems not as a discrete project but as an ongoing engineering discipline. For pharmaceutical leaders weighing investment decisions, the message is straightforward. The systems that once sufficed for periodic reporting are no longer adequate for a world where data flows continuously and decisions must follow at a similar pace. Those who rearchitect their foundations with that reality in mind are likely to set the standards others will follow.
