Data's renaissance: Gen AI and the new value of data (AIM247)

Key Takeaways from the Video Transcript

The Rise of AI and its Dependence on Data

  • AI is making a significant impact across industries, with 93% of pharmaceutical companies planning to invest more in AI and data by 2025.
  • GenAI has become a top priority for C-suite executives, with CEOs of companies like Sanofi and Genentech highlighting their AI-driven initiatives.
  • Investments in AI are expected to grow from $33 billion last year to $1.3 trillion in 2024, reflecting the immense potential and real-world applications of this technology.

AI Delivering Value in the Pharmaceutical Industry

  • AI is being leveraged to optimize clinical trials, automate protocol authoring, enhance digital marketing personalization, and improve field training for sales representatives.
  • Unlocking the potential of unstructured data, such as clinical literature and medical documents, is crucial for generating valuable insights.

Challenges and Opportunities in Data Management

  • The exponential growth of data, with medical knowledge doubling in just 73 days, poses a challenge for both organizations and healthcare professionals to make sense of this deluge of information.
  • Untapped potential of unstructured data, with 80% of healthcare data lying in this format, remains a significant opportunity.
  • The lack of context in data collection strategies has led to millions of dollars being spent on data acquisition, without effectively leveraging the insights.
  • 70% of AI projects are failing or not scaling, primarily due to inadequate data readiness for AI use cases.

Strategies for Data-Driven AI Adoption

  1. Focus on Business Value: Adopt a use case-driven strategy that identifies the problems to be solved and collects unique, proprietary data points.
  2. Collect and Connect Data: Bring together structured and unstructured data from various sources, both within the organization and externally.
  3. Leverage AI to Enrich Data: Utilize AI capabilities, such as natural language processing and large language models, to contextualize and enhance the data.
  4. Implement a Data-as-a-Product Approach: Curate and design data assets as reusable, high-quality products accessible through a data marketplace.
  5. Establish the Right Data Architecture: Ensure a federated data management strategy that provides seamless access and utilization of data across the organization.
  6. Employ AI for Data Management: Leverage AI to automate data governance, coding, and reporting, improving the efficiency and quality of data management.

Conclusion

  • Data is a strategic asset that can unlock innovation, especially when combined with AI to solve complex problems in the pharmaceutical industry.
  • The key steps for a successful data-driven AI strategy include curating proprietary data sets, treating data as a product, contextualizing data, and ensuring data is findable, accessible, interoperable, and reusable (FAIR).
  • By adopting a use case-driven, iterative, and collaborative approach to data management, organizations can effectively scale their AI initiatives and gain a competitive advantage.

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