When it comes to generative artificial intelligence (gen AI), it is natural for healthcare and pharma leaders to feel excited about the ability to make major productivity gains. But the possibilities go beyond that. Productivity is important, yet real success will come when generative AI begins providing deep insights that support making smart decisions. In addition, very soon, most organizations will use gen AI to promote efficiency, but the real game-changer will be that it will apply to generating insights and direct, important options. Beyond the automatic tasks, there are many areas where AI is ready to create a meaningful effect, including:
Identifying drug molecules
In pharmaceutical research and development (R&D), a lot of effort is required to identify the correct molecules. Generative AI has the ability to reduce this burden by streamlining the process and making it more efficient.
Drug discovery
Another amazing possibility that gen AI can help realize is the ability to extract and link between T-cell phenotypes and their respective genetic markers, which can open new avenues for research.
Clinical trials
Developing a clinical testing protocol is often time-consuming. AI integration can help streamline the planning and operations of clinical trials and revolutionize clinical research, making it more efficient, cost-effective and patient-centric.
Regulatory submissions
Generative AI and large language AI models are already gaining popularity for their ability to generate text, and their capabilities are improving steadily. Over the coming years, it will be worth observing how these tools can support healthcare consulting companies in producing the extensive documentation required by regulatory authorities, like the U.S. Food and Drug Administration.
Data moats
Another valuable application of gen AI lies in building data moats. Generative AI enables organizations to work with large, diverse datasets in ways that produce sharper insights and help establish a competitive edge.
Predicting patient drop-off
There is also scope for generative AI in pharma for creating synthetic data that can be used to anticipate when patients might discontinue participation in a clinical trial, allowing for better planning and interventions.
How to mitigate the risk of AI in pharma
It is impossible to talk about the potential of generative AI without acknowledging the risks that come with it. While every industry faces certain challenges, the risks are especially significant for pharmaceutical companies. This sector operates under strict regulatory frameworks and must also navigate sensitive issues, such as intellectual property protection and data privacy. Given that the outcomes often relate directly to human health, it is critical for life sciences organizations to evaluate these risks carefully and establish strong policies and controls to minimize them. Generic, off-the-shelf AI solutions are unlikely to meet these stringent needs, making it essential to develop customized systems equipped with proper safeguards and active human supervision.
Pharmaceutical firms also need to recognize that not all risks are the same and that each use case or domain carries its own level of exposure. For example, in medical affairs, where AI-generated insights could influence patient outcomes, the margin for error is extremely small. In contrast, within research environments, a model’s inaccuracy or “hallucination” might have lower consequences and, in some cases, could even inspire new ideas, such as suggesting an unexplored chemical compound with therapeutic potential. For pharma, there are certain key risk areas that must be kept in mind:
- Model inaccuracies: If gen AI is trained on incomplete data or low-quality data, it may produce inaccurate or misleading information. To mitigate this, organizations should implement robust review mechanisms to ensure that humans validate AI-generated outputs before they reach healthcare professionals or patients. This means that AI should serve as a decision-support tool rather than the final authority.
- Intellectual property and data privacy: Since most fundamental models are trained on vast amounts of publicly available data, issues such as copyright, plagiarism or IP infringement may occur. For pharmaceutical companies, this concern is heightened by the strict rules governing the storage and use of patient information jurisdictions, for example, require sensitive data to reside on in-house servers. To protect against these risks, organizations should rely on internally obtained data for model training and include explicit IP protection clauses in agreements with third-party vendors.
The key to developing generative AI for the long run
Looking at the bigger picture, the question becomes: what does it take to build a lasting generative AI capability? Cloud infrastructure is certainly a key element, but just as important is the development of orchestration–and agent-based architectures that can scale effectively. Laying down the right foundation models will be critical for enabling generative AI at scale. In addition, working with a technology services partner can help organizations set up a robust tech stack of generative AI services and machine learning tools designed to unlock the full value of their data.
Getting started with generative AI begins with choosing the type of model that best suits your needs. One approach to this is working with publicly available models that are already well-known and then optimizing them to suit specific organizational requirements. Another option is to create a custom AI model from scratch and optimizing it to meet unique needs. Whichever approach is selected, there are several important factors to keep in mind when planning to adopt generative AI:
- Ensuring that the right cloud infrastructure is in place.
- Developing AI capabilities and models that can be scaled effectively.
- Establishing clear processes for writing prompts and deciding who should oversee them.
- Having the right mix of people with the skills needed to manage and grow AI capabilities.
- Maintaining strict privacy and security for sensitive data.
- Tracking impact through well-defined performance indicators
- Integrating generative AI alongside existing traditional AI systems
Some organizations are beginning to experiment with insight agents, which are essentially question and answer (Q&A) interfaces that enable insight leads and marketers to ask questions about the data directly. Instead of letting valuable information sit unused in old market research files, dashboards or secondary data sets, these tools help teams pull out insights more quickly, driving efficiency. In some cases, generative AI is also being used to analyze call center transcripts from patients and providers, uncovering insights that can guide real-time strategic adjustments and approaches that are already familiar in several other industries.