How pharma companies can use artificial intelligence (AI) and data to their advantage

Pharmaceutical companies and life sciences companies recognize the transformative potential of artificial intelligence (AI), particularly generative AI. Using generative AI in marketing efforts, clinical trial design, or accuracy and efficiency in their drug discovery is important. However, many pharma companies still struggle with the complexities of their data. To gain a competitive edge in the market, the challenge lies in harnessing the data. According to recent research, many pharma executives plan to rethink their data strategies, but only half of them believe their current approach drives meaningful differentiation.

To unlock AI’s full potential, pharma companies must shift the focus from only managing data to strategically leveraging it for growth. To make data useful in the AI era, companies need to ask a fundamental question—where does data create unique value? One opportunity is combining untapped unstructured data with traditional structured data, using generative AI to create new insights and improve data findability. However, scaling AI use beyond basic productivity tools requires a purpose-built data strategy, one that aligns with business outcomes and fosters continuous learning. Let’s look at how pharmaceutical companies can effectively use AI in pharma to drive innovation, improve efficiency, and gain a competitive edge in the healthcare landscape.

How to build a strong data strategy?

To make sure a company’s data strategy is built for success, pharma companies should follow these basic steps:

·         Look at data from a business perspective: Recognize how certain features can increase data’s potential to be of value in various parts of the business.

·         Be more agile: Move from reviewing data strategies every three to five years and use a more flexible system that continuously collects, connects, and improves data. This approach focuses on adapting to changing needs, concrete business outcomes, and managing costs more effectively.

·         Strengthen your data strategy: Combine data from various sources (internal company data and third-party data) and use Gen AI to improve data management.

How to leverage AI and data for competitive advantage?

Harnessing clinical data beyond regulatory submissions

One of the most significant ways pharma companies can use AI and data to their advantage is by harnessing clinical trial data for more than just regulatory submission. Clinical data has been tightly linked with compliance and approval processes, which has limited its broader potential. However, with AI-driven strategies, companies can unlock new opportunities, like optimizing future trials, discovering new therapeutic applications, and improving data design.

A focus on making clinical data FAIR (findable, accessible, interoperable, and reusable) holds a significant potential value. By following these data principles, companies can break down data silos and make data more accessible across teams. Generative AI can enhance data organization by automatically tagging and adding descriptions, making it easier to find and reuse the data for different research and development needs. They also help ensure that only the right people can access sensitive clinical data and manage ongoing data integrity. Additionally, AI can integrate multimodal data, like tumor biopsy results, CT scans, and electronic health records (EHRs), to provide deeper insights into disease progression and treatment effectiveness.

The key advantages of harnessing clinical data include:

·         Optimizing future trials by analyzing past studies to improve efficiency and outcomes.

·         Discovering new therapeutic applications for existing drugs by identifying new patterns and insights from clinical data.

·         Facilitating knowledge transfer between research and clinical development to accelerate innovation.

·         Enhancing understanding of disease pathways and progression.

·         Optimizing study design and execution using predictive biomarkers to improve trial success rates.

·         Advancing precision medicine initiatives by tailoring the treatment based on clinical data analysis.

Triangulating commercial data for healthcare ecosystem engagement

In the evolving healthcare ecosystem, pharma companies can harness the power of AI and commercial data to improve their strategies and customer engagement. Typically, commercial data efforts are focused on collecting structured data like demographics or basic customer metrics. While this information is valuable, it often does not fully capture the underlying needs and motivations of customers. However generative AI can help in gaining deeper insights by analyzing unstructured data sources and enabling pharma companies to gain a complete understanding of their customers.

AI can play a pivotal role in triangulating data for pharmaceutical companies.

·         Collecting data directly from patients: Gather information straight from patients, like their health details and treatment history.

·         Combine data from multiple sources: Link and integrate data from various first, second and third-party sources to uncover new insights.

·         Add extra context to the data: Enrich the data with useful details, like identifying patient risks or understanding where healthcare providers are in a patient’s treatment journey.

This deeper understanding of customers enables companies to create context-driven, individualized strategies that consider not just transactional needs but also the broader challenges and preferences of the customer. A real-life example is a U.S. subsidiary of a global pharmaceutical company that combined patient data from various sources, such as patient data from pharmacies, hubs, and claims, to identify patients with rare diseases facing activation delays. By analyzing the data, the company was able to pinpoint healthcare providers who have patients at elevated risk of delayed treatment. This allowed commercial teams to proactively reach out to providers, understand the cause of delays, and expedite patient care.

The benefit of leveraging AI and commercial data for pharma companies include:

·         They can create plans that are tailored to each customer, considering all the products and teams involved with the customer.

·         They can track how well these strategies are working in real-time, across distinct roles and communication channels.

Optimizing manufacturing process

Pharmaceutical companies use AI and data to transform their manufacturing processes from reactive to predictive. Currently, companies identify most manufacturing issues only after they occur, resulting in costly breakdowns and delays. By using AI and data, companies can proactively detect and address problems before they occur, reducing downtime and optimizing production.

One of the ways to achieve this is by using digital twins—virtual replicas of manufacturing processes. These models simulate the real-world scenarios, which allow companies to monitor, analyze, control, and optimize their operations. Gen AI can enhance the accuracy of these replicas by analyzing data from connected devices, RFID tags and sensors in real-time in manufacturing facilities and warehouses. AI can monitor how raw materials are used, how equipment is functioning, and whether the operational procedures are being followed to maintain consistent quality, efficiency, and compliance.

By integrating and analyzing data from across different systems, like manufacturing execution systems (MES) or laboratory information management systems (LIMS), pharma companies can build predictive models. These models can optimize every stage of manufacturing—from raw materials to finished products.

Generative AI has the potential to transform the pharmaceutical industry by unlocking the full potential of data. Pharma companies must stop treating data just as a regulatory requirement or support tool. Instead, they should build a strong data strategy, integrating structured and unstructured data. By effectively using AI in clinical trials, commercial strategies, and manufacturing, pharma companies can improve efficiency, accelerate drug development, and optimize operation processes.