Harnessing AI to Future-Proof Biopharma

Raj Indupuri, CEO and co-founder, eClinical Solutions

The biopharmaceutical industry has crossed the threshold into the AI era. As scientific and technological advancements accelerate, trials and data management complexity is growing exponentially. To keep pace with these trends, it is imperative to think of “AI native” processes or processes with intrinsic and trustworthy AI capabilities for achieving scalable outcomes. And those who consider AI as a niche or leverage AI for a discrete set of use cases risk falling behind in a rapidly evolving landscape.

AI technology is advancing at a groundbreaking pace. With the widespread commoditization and applications of AI and the integration of chatbots like ChatGPT and Google’s Gemini into everyday life – from summarizing search results to assisting users with several tasks by responding to queries or voice – AI is no longer viewed as an advanced technology, but is now an integral part of user experience and expectations and a necessity to pervasively embed for intelligence and automation at scale. In life sciences and clinical development, this shift was gradual and then sudden. Teams no longer have the luxury of experimenting with AI, but must fully integrate it into core business processes and decisions.

Soon, AI-driven innovation will be assumed and expected to address complex problems much like the internet, mobile revolution, or cloud computing.

Addressing data challenges in clinical development with AI

The pressure on clinical development to reduce cycle times while doing more with less is intensifying. Advances in science and innovative trial designs add layers of complexity that traditional and manual methods simply cannot scale. These methods are increasingly holding companies back from maximising the value of their data to achieve quality outcomes in clinical trials and drive broader innovation.

The shift towards decentralised clinical trials, wearables, and remote-monitoring devices has transformed data collection, resulting in a massive increase and extreme differences in the volume, velocity, and variety of data generated. This complexity requires intelligent automation to ensure real-time data ingestion, rapid standardisation, and harmonisation across diverse sources for quick actionable insights and decisions. This will ultimately drive more efficient trials, reducing the cost and time it takes to bring medicines to patients.

AI should not be leveraged to solve discrete use cases in clinical development, but should be considered an integral component across the value chain with a holistic data strategy elevating oversight, quality, and efficiency at multiple stages of clinical development.

Taking clinical data management as an example, we can achieve end-to-end automation by leveraging AI-driven apps for: the detection and categorisation of protocol deviations, enabling early identification and resolution of compliance issues, and which prevents delays and maintains the integrity of trial outcomes. Similarly, AI-driven apps can automatically flag data inconsistencies and anomalies, allowing teams to focus on critical areas and ensure higher data quality.

AI also has the potential to enhance patient recruitment and retention by analysing diverse datasets to pinpoint suitable candidates more accurately and personalising communication strategies, reducing enrolment time and improving retention rates. Real-time intelligent data integration allows for adaptive trial designs, where adjustments can be made based on interim results to improve overall success rates.

While it is essential to think holistically about embedding AI across all stages of clinical development, incremental adoption is also a viable approach. By focusing on areas that offer immediate value, organizations can gradually build a solid foundation for more widespread AI implementation, setting the stage for the next steps in creating a comprehensive AI strategy. 

Building the foundation for AI

A robust foundation for AI starts with a strong data infrastructure that supports interoperability, accessibility, and high-quality data. By ensuring governed real-time access to all trial data, organisations can enable faster AI application builds and smarter decision-making throughout the trial lifecycle.

Next, enabling technology for building and training models to run AI workloads for inference and rendering output that can be validated with a closed human-in-the-loop mechanism is essential for more precise data interpretation and decision-making, where machine intelligence augments human expertise. This also requires a focus on governance for a set of policies, frameworks, and practices that guide the development, use, and deployment of AI. The goal is to ensure that AI is used in a responsible and ethical manner, while also maximising its benefits and minimising potential risks.

As organizations build this foundation, they should consider a phased approach that starts with key areas where AI can deliver immediate value, such as automating data review processes or enhancing patient recruitment strategies. This enables companies to realize early wins while preparing for more comprehensive adoption in the future.

People and processes transformation for AI adoption

Embracing AI in biopharma goes beyond technology – it requires reskilling people, reengineering processes, diligent change management practices, and cross-functional collaboration. Building strong communication channels and shared goals between data managers, statisticians, and clinical programmers is essential for driving adoption and effectively leveraging AI insights throughout clinical development.

Biometrics teams are evolving from traditional roles to become key drivers of data strategy and innovation. Today, data stewards, or clinical data scientists, ensure the quality and integrity of data flows and enhance data-driven decision-making in clinical development.

Future readiness

To prepare for an AI-driven future, biopharma companies must rethink their approach to AI – not as a nice-to-have technology leveraged to solve a discrete set of problems, but as a strategic and powerful technology that can be used to enhance simple features, solve complex problems, drive intelligent automation, and deliver rich user experiences. This requires a shift in mindset: seeing AI as the intelligent layer that unleashes the value of data with unprecedented efficiency. Proactive adoption of AI will determine which companies thrive in a rapidly evolving landscape.

By building a comprehensive AI strategy, companies can foster innovation, streamline clinical trials, and improve data management processes. In conclusion, those who implement AI successfully will not only shape the future of healthcare, but also lead in delivering innovative, life-saving treatments. However, for those hesitant or skeptical, the window of opportunity is closing fast.

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