What is Artificial Intelligence (AI) in a pharma commercial context?
In a pharma commercial context, AI primarily refers to generative AI, which differs from traditional “classical AI” or machine learning.
- Classical AI / Machine Learning models perform specific tasks, like analyzing data for predictions or identifying patient groups, using very specific datasets.
- Generative AI works with a foundational, large language model (LLM) trained on vast amounts of data, enabling it to be applied to many different problems. Its key characteristics include:
- Basic competences applicable to various problems.
- Ability to learn from structured and unstructured data.
- Generation of multiple outputs such as texts, images, speech, video, and designs.
- An intuitive, conversational user interface.
In pharma, generative AI is used for activities like writing marketing materials, generating webinars, building image and video libraries, spotting segmentation opportunities, and speeding up marketing processes. It is projected to grow faster in healthcare than any other industry, potentially generating between $60 billion and $110 billion a year in economic value for the pharma and MedTech industries, with most value going to commercial operations.