Case Study: How AbbVie is Pioneering AI Integration in Pharma


AbbVie is a research-driven biopharmaceutical company with a presence in 175 countries, catering to over 30 million patients. In the competitive pharmaceutical landscape, AbbVie stands out due to its commitment to technological advancements. The company has invested heavily in AI tools and strategies to transform and enhance its clinical processes. Collaborating with tech giants like Intel, AbbVie aims to refine its operations, harnessing the capabilities of modern machine learning models for data analysis and research optimization.

Key Takeaways

  • AbbVie has developed AI tools like Cortellis Search and SCRAP to improve their clinical study reports (CSRs) generation and management.
  • The company has employed transformative AI like FOCAL for various tasks, from content identification to translation and generation of content.
  • Collaboration with Intel led to the optimization of NLP models, significantly accelerating language translation and document search operations.
  • AI adoption hasn’t negated human involvement; rather, it has augmented human capabilities and efficiencies.

Deep Dive: How AbbVie is Pioneering AI Integration in Pharma


AbbVie has adopted a multi-faceted AI strategy. The company uses tools like Cortellis Search for quick phrase identification in documents, SCRAP for content retrieval and authoring, and FOCAL for tasks such as translation. AbbVie’s focus has been on creating user-centric tools that not only bring efficiency but also are easy to integrate into the current workflow of employees.


Through collaborations, notably with Intel, AbbVie has managed to optimize its AI tools. For instance, Abbelfish Machine Translation, AbbVie’s proprietary language translation service, was enhanced through Intel’s Optimization for TensorFlow. Likewise, AbbVie Search, an NLP model for scanning research documents, was accelerated using the Intel Distribution of OpenVINO toolkit.


The advancements have led to significant improvements in efficiency. Abbelfish language translation saw a 1.9x improvement in throughput. Similarly, AbbVie Search witnessed a 5.3x acceleration in comparison to unoptimized TensorFlow. These improvements not only hasten the processes but also reduce the reliance on additional hardware for AI tasks.

Challenges and Barriers

One of the challenges is the need to ensure that AI-generated content remains accurate. While models like FOCAL have vast capabilities, there is still a reliance on human validation to ensure the output’s accuracy. Another challenge lies in ensuring the seamless integration of these tools into existing platforms, making them user-centric.

Future Outlook

AbbVie plans to release the technology behind many of these models into the public domain in the coming months. They believe that while the CSR remains their strategic asset, the models and the technological advancements can be beneficial to other pharmaceutical companies and industries.


AbbVie’s commitment to integrating AI into their operations showcases how a blend of technology and human expertise can drive unparalleled efficiencies in the pharmaceutical domain. The collaborations, advancements, and future plans underline AbbVie’s position as a forward-thinking global leader in the biopharmaceutical industry.

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