This PPT slide, part of the 33-slide Generative AI (GenAI) in the Pharmaceutical Industry PowerPoint presentation, outlines a structured approach for pharmaceutical companies to effectively implement Generative AI (GenAI) while managing associated risks. Central to this framework is the emphasis on a holistic risk management strategy that encompasses several critical areas.
The first point stresses the importance of investing in data governance and integration. This involves establishing automated data pipelines and ensuring that both internal and external data sources are well-curated. Accurate datasets are crucial for AI models to function effectively, thereby enhancing the reliability of insights generated.
Next, the slide highlights the need to fine-tune AI models using proprietary datasets. It suggests that AI should be integrated into existing workflows rather than being confined to isolated pilot projects. This integration is essential for maximizing the utility of AI and ensuring that it contributes meaningfully to operational processes.
The third point addresses the implementation of AI governance frameworks. These frameworks should include compliance controls and mechanisms for human validation of AI-generated insights. This approach not only mitigates risks related to regulatory compliance, but also fosters trust in the AI outputs.
Lastly, the slide advocates for the use of McKinsey’s product-platform model to scale AI adoption across various business functions. This model promotes cross-functional collaboration and aims for long-term sustainability in AI initiatives.
Overall, the slide serves as a guide for pharmaceutical executives looking to navigate the complexities of GenAI implementation while ensuring that risks are effectively managed. It emphasizes the necessity of a comprehensive strategy that aligns data governance, model integration, compliance, and organizational collaboration.
This slide is part of the Generative AI (GenAI) in the Pharmaceutical Industry PowerPoint presentation.
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Healthcare Artificial Intelligence Risk Management Data Governance Sustainability Governance Compliance
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