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20 Apr 2023
GenAI has emerged as a transformative force in life sciences, unlocking opportunities across research, commercial, medical, and regulatory functions. In my earlier blog, I highlighted a few considerations that are critical for the adoption of GenAI technologies like ChatGPT in pharma.
Pfizer’s "Charlie" assistant and Moderna’s "mChat" offer real-world examples of ChatGPT applications in life sciences. Many pharma organizations have started using ChatGPT for smarter HCP engagement and medical content personalization. According to McKinsey research, GenAI is expected to produce $60 billion to $110 billion in annual value across the pharmaceutical value chain.
In this blog, I’ll focus on the practical side: the use cases where GenAI delivers the most value today, and where it’s gaining momentum. These examples are best viewed through the lens of broader business processes and not just isolated technologies.
Visualizing prioritization: A framework for high-impact GenAI use cases
To guide where to start with GenAI, frameworks like the one below help life sciences leaders assess and prioritize use cases across business impact, feasibility, and strategic alignment.
| Value Lever | Value Driver | Value Description | Impact Value | Impact Level | ||
|---|---|---|---|---|---|---|
| 1 Minor | 3 Moderate | 5 Significant | Enter Score | |||
| Business Value | Cost Savings | Target average savings per year; only realized cost savings count, FTE savings to be put in reduction of process resources | ≤$50k | ≤$500k | >$500k | 1 |
| Strategic Relevance | Alignment with company goals/vision | Indirect impact on company goals/vision | Direct impact on company goals/vision | Direct impact on multiple company goals/vision | 5 | |
| Time to clinical trial impact | Target reduction of time to clinical trial initiation or completion compared to status quo | ≤10% | 10-30% | >30% | 1 | |
| Competitive advantage | Improvement in benchmark metrics for this project in comparison to industry standards | Negligible improvement of performance | Consistent improvement of performance | Substantial improvement of performance | 1 | |
| Risk reduction | Compliance | Improvement in adherence to existing regulations and process | Negligible or no impact on compliance | Measurable increase in compliance | Increase in compliance across areas | 3 |
| Quality | Improvement in quality of deliverables | Negligible or no impact | Measurable quality improvement | Significant improvement in quality | 1 | |
| Variability | Improvement in Consistency of deliverables | Negligible or no impact | Measurable reduction | Significant reduction in variability | 5 | |
| Efficiency improvement | Increase in throughput | Increase in throughput per process execution | ≤10% | 10-30% | >30% | 3 |
| Reduction of process resources | Reduction in FTEs, materials, time etc., per process execution | ≤10% | 10-30% | >30% | 5 | |
| Increase in robustness (intelligence) | Improvement in overall product process, e.g. workflow/ elimination of administrative tasks | Negligible improvement in overall process effectiveness | Measurable improvement in overall process effectiveness | Substantial improvement in overall process effectiveness | 5 | |
| Scalability | Transferability of the solution across other use cases in the organization | Isolated use case | 2-5 use cases | >5 use cases | 5 | |
Personalizing Customer Journey Maps with ChatGPT
In the experience economy, pharmaceutical companies face the challenge of building personalized customer journey maps in an omnichannel world. Traditionally, this was approached through Next Best Action models, but these often-lacked flexibility and scalability.
ChatGPT provides a more dynamic, cost-effective alternative. It enables the creation of personalized journey maps and associated omnichannel plans for a defined customer segment. This saves a significant amount of time and effort while offering a cost-effective option vis-Ã -vis traditional methods that require high investment.
By carefully setting up the context and guardrails, you can ensure that the technology understands the intent of the question and provides consistent responses. With this foundation in place, ChatGPT can then be used to develop detailed journey maps and plans that cater to each customer segment and persona.
Intelligent Chatbot for Real-time Patient Interaction
Conversational AI / Chatbots have been around for a long time. Now, GenAI offers you the ability to build your own chatbot and scale it to the next level. One example I was personally involved in was building a robust interactive chat functionality in a matter of days instead of months.
Today, ChatGPT can be deployed to power intelligent virtual assistants that support patients across various touchpoints. One of the most promising applications is in pharmacovigilance. By guiding conversations and extracting key information from natural language inputs, ChatGPT can capture and structure adverse event (AE) data efficiently reducing manual follow-ups and accelerating downstream processing in systems like Oracle Argus Safety.
Recent Indegene pilots have demonstrated 30–40% reduction in follow-up workload by integrating ChatGPT for AE intake and medical narrative creation.
ChatGPT can double up as a call centre/chat agent in other domain areas, with a carefully defined context preventing the AI from providing incorrect information. By guiding the conversation and asking relevant questions, ChatGPT can collect valuable information from users (patients) and AI-driven insights in medical research in real-time.
The true power of ChatGPT lies in its ability to parse sentences and extract the required information from users' responses, even if they are not formatted as data inputs. Once the AI engine has collected all the necessary information, it can be converted into a structured format that can be easily fed into a database or system.
Other promising use cases of ChatGPT in Life Sciences:
Final Thoughts
The practical applicability of ChatGPT in pharma is expanding rapidly. With ChatGPT and GenAI use cases in pharma moving from POCs to scaled deployments, the next frontier is responsible adoption.
It’s not just about deploying tools but also building institutional knowledge and governance to scale safely. As seen with leaders like Moderna, Pfizer, and J&J, the ones who are investing in talent, frameworks, and trust are unlocking real business value.
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