Deliver Repeatable, Measurable, and Enterprise-Ready AI for Life Sciences
Pharmaceutical and life science companies use AI to enhance drug discovery, clinical development, and patient experiences. In these types of regulated environments, the key to unlocking AI-assisted breakthroughs and return on investment (ROI) is a back-to-basics approach—focusing on data unification, interoperability, and security and governance.
On the latest episode of the Healthcare IT News podcast, HIMSSCast, Rameez Chatni, Global Director of AI Solutions at Cloudera, explains that the industry is transitioning from a nascent focus on AI strategy back to the bedrock of a robust data foundation.
Ensure Interoperability Across The Value Chain
The typical global pharma organization comprises 12 to 15 distinct, enterprise-like verticals—R&D, manufacturing, commercial, and so on—and building an AI-ready data set requires managing sophisticated, distributed architectures.
Data unification is difficult, and the solution isn't to force all data into one homogenous system. Instead, organizations are embracing a hybrid architecture that accommodates on-premises systems, multiple clouds, and software-as-as-service (SaaS) solutions.
Using open-source, interoperable technologies that support open data formats ensures that multiple query engines can access data for a variety of engineering, analytic, and AI workloads, and reduces the risk of vendor lock-in.
The ultimate goal for data unification is to give AI models the context they need to connect the dots across the organization and provide better outputs. One contextual model many pharma companies are leveraging is a knowledge graph. This structure captures the relationships within the business—linking drugs to genes, diseases, clinical trials, and commercial data— that humans often miss, creating a truly comprehensive and usable data set.
However, these advanced architectures hinge on one critical, often-overlooked first step: data inventory and data lineage. These are the unsung heroes and foundational pillars that prevent different functions (like R&D and manufacturing) from duplicating licenses for the same data sets and wasting resources.
Treat Governance as a Feature, Not a Bug
In a sector that is trying to innovate quickly with data, governance is frequently an afterthought, and projects can stall for as long as nine months as a result. Rameez argues that governance must be treated as a feature, not a bug. This means transforming it into “governance as a service,” a proactive, continuous capability within the enterprise.
The only way to achieve governance as a service is through a multidisciplinary center of excellence (CoE) that connects business leaders, data strategists, technology architects, and privacy/legal lawyers. This ensures technical teams, who understand how data moves, can communicate effectively with legal teams, who understand privacy and consent restrictions.
Crucially, governance should be applied early. Failure to consider compliance, like restrictions on using clinical trial data for secondary purposes, can halt an entire project late in the game. In fact, AI should be applied to governance itself to accelerate contract reviews and ensure compliance checks are automated and auditable.
Prove ROI To Achieve Scale
The industry is littered with reports of AI pilot failures. Organizations that are just starting their AI journeys should find the operational AI use cases first. Automating "boring" tasks like clinical trial protocol writing (saving a week on each of a thousand documents) or processing adverse events faster are clear, quick wins.
Rameez advises that success starts with defining a clear, measurable ROI that aligns with the business. In pharma, enabling a “fail fast” culture is a ROI. Computational failure is significantly cheaper than a late-stage clinical trial crash.
Rameez frames this ROI simply, advising that organizations take steps to identify and solve issues quickly, before they snowball: "The earlier you find problems... you can get to a (solution) much faster before it becomes a much bigger problem."
Finally, standardize your systems: define the agentic frameworks, the tools, the support models, and, most importantly, have clear rules for promotion from development to a validated, auditable production environment.
The Next Frontier: Personalized AI
Looking ahead, the next three to five years promise even greater transformation. We’ll see a rise in personalized agents that tailor interactions and insights to the individual user.
AI models will evolve to optimize for multi-parameters simultaneously. Instead of optimizing just for efficacy, models will suggest molecules that are effective, non-toxic, manufacturable, and have a good shelf life—all at once. We may even see the first commercially available drug marketed as “generated by AI.”
Want to learn how to prepare your organization for this future? Listen to the full conversation with Rameez Chatni for all the details on AI implementation and best practices.
