Innovate with analytics and governance: Customer Panel (ANT203)
Key Takeaways
Healthcare and Life Sciences
Nota, a clinical testing lab, is leveraging generative AI and large language models (LLMs) to extract insights from unstructured clinical data like patient records, test results, and treatment histories.
This allows them to create comprehensive digital health records for patients, track cancer treatment journeys, and compare patient cases to inform better treatment decisions.
The ability to combine structured test data with unstructured clinical data has been a game-changer, enabling them to scale these capabilities across all their patients.
Financial Services
At NatWest, generative AI has flipped the paradigm, with business units now proactively engaging the data and analytics teams with hundreds of ideas on how to apply the technology.
Use cases include automating call summarization for relationship managers and dramatically improving fraud investigations by building graph embeddings models to uncover complex fraud networks.
The speed and depth of insights these models provide have enabled the bank to take down a human trafficking operation that was exploiting their systems.
Logistics and Transportation
At Amazon, the scale of data and systems involved in their logistics operations is immense, with petabytes of data, 50,000 active users, and 100 different systems.
They tackled the challenge of root cause analysis at delivery stations by building a composite AI solution - using machine learning for the analysis and generative AI to represent the insights.
This led to 91% acceptance by the delivery station teams, who found the insights more comprehensive than what they would have identified manually.
The next steps involve incorporating LLM-based reasoning and building an agentic AI system to automatically take actions based on the insights.
Common Themes
All the organizations are dealing with challenges like data and tool sprawl, the need to empower distributed teams to own their data, and driving a cultural shift around data-driven decision making.
Embedding governance and controls directly into the data and analytics workflows, rather than as an afterthought, has been crucial.
The pace of innovation with generative AI is causing a mindset shift - from focusing on feasible projects to embracing the "magical" and being bold in what's possible, knowing the capabilities will continue to evolve rapidly.
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