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AI has profoundly impacted the life sciences industry for the past couple of decades. In the 2000s, researchers were able to use AI to analyze the human genome, identifying genetic markers and variations that could predict an individual’s susceptibility to certain diseases. This opened the door to personalized medicine and more effective therapies for genetic disorders.

The year is 1424. You’re shipping goods across the world, and the ship in question gives you a bill of lading. It’s a piece of paper containing details about what your goods are, where you’re shipping them from, and where they’re headed. Fast forward to 2024. You’re shipping your goods across the world, and the shipping company gives you a bill of lading. It’s still (most likely) a piece of paper.

MIT research shows data governance is the precursor to effective AI implementation.

The last decade has seen a giant shift by organizations into the cloud for software, storage, and compute, resulting in business benefits ranging from flexibility and lower up-front costs to easier maintenance. But lately we have seen more and more companies re-evaluating their cloud strategies and opting to move their data back to on-premises infrastructure due to several key factors.

For insurance companies, balancing customer expectations with the rigorous requirements necessary to mitigate risks poses a challenge. Especially when you’re using manual underwriting processes. By turning to artificial intelligence (AI) in insurance underwriting, you avoid costly delays, streamline your employees’ processes, improve accuracy, and create an optimal customer experience. In this blog post, you’ll learn how AI facilitates greater efficiency in underwriting.

BigQuery vector search, now GA, empowers data and AI use cases such as semantic search, similarity detection, and RAG with an LLM.

At Starwest 2024, Mr. Anand Gopalakrishnan delivered an insightful presentation titled "AI-Assisted Exploratory Testing for Healthcare Software Based on the STEEEP Domain of Healthcare Quality." He highlighted how artificial intelligence is revolutionizing exploratory testing by enabling real-time risk assessment and providing deeper insights into software performance.

How are AI governance and data governance related? Better still, what’s more important for an organization to focus on, AI-powered data governance or AI data governance? These are important questions, but before we answer these, let’s understand how AI and data governance are related to each other.