Generative artificial intelligence (gen AI) is no longer just an emerging technology; it is now central to how life sciences is planning its future. A technology that many organizations are prioritizing ahead of solving challenges like rising costs, drug pricing pressures, and reimbursement constraints.
So far, most have started small, using generative AI to solve practical, low-risk problems like automating repetitive tasks, summarizing reports, locating data or performing analytical jobs. Though isolated, these allow teams to get things done much faster while building confidence in the technology. However, this approach has its limits.
Across the industry, many organizations are stuck in a phase where excitement around generative AI pharma is high, but measurable impact is still limited. Investments are often spread across isolated use cases, leading to small wins but no meaningful business transformation. At the same time, employees often have poor experiences and feel disengaged since the broader vision isn’t clearly communicated by their leaders.
To move forward, companies need to shift their mindset: from focusing only on efficiency to creating real business value.
Beyond productivity: A value-driven approach
For long, organizations have used classical AI to drive productivity. However, that alone is not enough. Classical AI can make predictions based on patient behavior, however, to drive real impact, these models require access to a large volume of structured data, which can be quite difficult in the healthcare scenario, as most data is in silos and unstructured. Generative AI can mine and synthesize this unstructured data and allow users to derive value.
Generative AI can be understood across three levels of impact:
Productivity enhancer
These help teams do existing work faster. From automating tasks to summarizing data, and improving operational speed. While these use cases are widely adopted, they offer limited long-term differentiation.
Insight generator
This is where generative AI starts unlocking deeper value: through automation. The technology can analyze large volumes of unstructured data to uncover insights and detect patterns that traditional tools often miss.
Decision enabler
This is where the highest value lies. Generative AI can support smarter decision-making by creating personalized marketing content, simulating reactions to different messaging and guide next-best actions.
Most companies today are still concentrated on the first category, but the real opportunity lies in combining all three with existing tools.
The power of combining AI approaches
It’s important to remember that generative AI does not replace classical AI; it complements it.
Classical AI is strong in predicting outcomes based on structured data, such as forecasting patient behavior or optimizing supply chains. Generative AI, on the other hand, excels at working with unstructured data and generating new insights or content.
AI and generative AI in pharma and healthcare create a powerful system that helps:
In commercial pharma
Understand what motivates healthcare providers (HCPs), predict intent, identify unmet needs and what motivates HCPs. The collaboration can also enable effective content generation, personalize communication with HCPs to improve engagement and suggest refinements based on rep queries and physicians’ reactions.
In clinical development
Develop a trail concept plan, design key statistical measures based on past protocols and cohort groups and optimize protocols for faster drug development. Collaborations between new-age clinical research consulting firms and generative AI can helps teams prepare trails plans that have higher data quality, enhancing patient engagement and improving decision making.
In supply chain and manufacturing
Building more resilient and responsive systems using real-time data. While generative AI can analyze regulatory and external data, and feed downstream AI, classical AI can help predict future demands, optimize procurement plans based on the supplier profiles generated by the gen AI. Together, both can optimize production and inventory plans.
This combination leads to better decisions and stronger outcomes. The organization goes from isolated use cases to connected systems that drive tangible business value.
