The promise of artificial intelligence (AI) in healthcare and life sciences (HLS) is clear: accelerating drug discovery, transforming diagnostics, personalizing patient care, adding commercial excellence, and optimizing operations. Yet beneath the buzz around AI lies a fundamental truth – its impact is only as strong as the quality and structure of the data behind it, and true transformation demands moving beyond outdated, fragmented data architectures.
Legacy Systems Are Holding AI Back
Many organizations are realizing that their outdated data systems, such as isolated electronic health records (EHRs) and clinical trial databases, are not suitable for the needs of modern AI technologies. These disconnected infrastructures were never built to handle the volume, speed, and variety of data required for advanced analytics.
For instance, a hospital that is starting an AI initiative to predict patient readmissions may discover that its fragmented EHR data lacks the necessary standardization and completeness needed for effective model training. This situation could lead to a lengthy and costly data remediation process, which is required to achieve a viable foundation for AI implementation.
It’s not surprising that only 30% of AI pilot projects make it to production. This low success rate is often due to challenges such as data readiness, integration costs, security concerns, and a lack of in-house expertise.
Harmonized and Integrated Data
It’s a common misconception that simply acquiring new AI tools is enough. However, successful AI initiatives are built on a foundation of data that is clean, standardized, and connected across systems.
Creating an integrated, harmonized data ecosystem often means structuring data into logical “data domains,” such as patient records, clinical trials, genomics, or supply chain, that are each aligned with specific business contexts. This approach not only improves data governance but also makes complex data landscapes more manageable, ensuring that data products are both accessible and fit for purpose in driving AI-driven insights.
For example, drug-discovery efforts integrating genomics, clinical trials, and real-world evidence can be more effective when organized under well-defined domains. This allows teams to accelerate model training, improve accuracy, and ultimately speed up the time to market for new therapies.
Building for Long-Term Impact
In the drive to adopt AI, some organizations turn to point solutions to overcome budget constraints, limited resources, or lack of stakeholder alignment. These tools address isolated problems and can be implemented quickly. While they may deliver short-term gains, layering them on to legacy systems can compound technical debt and make long-term, scalable transformation more difficult.
Forward-thinking organizations are instead embracing cloud-native architectures — flexible, scalable environments built on modern cloud services such as data lakes, managed data warehouses, containerization, and serverless functions. These technologies enable the creation of resilient, adaptable, and repeatable data ecosystems that support real-time processing, reusable services, and AI model deployment at scale.
With these foundations in place, integrating new tools or scaling existing capabilities becomes significantly more cost-effective and sustainable.
Data Management Through Distributed Ownership
For multinational HLS companies, data challenges are compounded by regional differences in regulations, systems, and standards. Achieving unified views across global operations, whether for clinical trials or supply chains, requires more than centralized control.
A more effective approach is to organize data into domains and assign ownership to the teams closest to the source, such as regional or functional groups. These domain experts are responsible for managing data quality and governance within a shared enterprise-wide framework. This structure promotes accountability and ensures the data reflects the realities of each region or function.
When supported by cloud-native infrastructure, this distributed model allows organizations to comply with local requirements, like data residency laws, while still maintaining global visibility. That visibility is essential for coordinated decision-making and efficient cross-border operations.
Meeting Evolving Market Demands
To fully realize the potential of AI and Gen AI in particular, HLS leaders must prioritize the strategic areas where these technologies can deliver the greatest impact. That begins with clearly defining business problems best suited to Gen AI, aligning stakeholders across technical and operational teams, and ensuring that the foundational infrastructure — data, governance, and processing capabilities — is in place to support deployment at scale.
These considerations are already top-of-the-mind for many organizations. Nearly 60% of industry executives are closely tracking Gen AI and digital transformation, recognizing them as critical trends shaping the future of HLS.
The Strategic Imperative Ahead
To lead in the next era of healthcare and life sciences, organizations must treat AI readiness as a core pillar of business strategy. Success depends on modern, scalable infrastructure designed for continuous innovation.
That requires moving beyond fragmented systems and stopgap fixes. Cloud-native, harmonized data environments are no longer optional; they are foundational to delivering the speed, intelligence, and adaptability today’s market demands.
The organizations that act decisively will be the ones best positioned to accelerate discovery, improve outcomes, and unlock the full potential of AI across the value chain.
