As AI continues to evolve, data management has become increasingly complex for enterprises. CIOs from diverse sectors, including financial services and healthcare, convened in roundtables to discuss their experiences and challenges in harnessing data for AI initiatives. They highlighted the importance of adapting existing data governance policies to account for AI’s unique requirements, yet some argued that traditional frameworks still apply to an extent.
Key challenges include setting realistic business expectations for AI model readiness, as the lifecycle of AI development differs significantly from traditional applications. A disconnect between data scientists and business users complicates timelines for deploying AI solutions. Furthermore, the workforce is facing a skills gap, particularly as AI’s risks are not fully understood, prompting many organizations to seek tailored strategies based on their specific contexts. The discussions revealed a wide range of practices, from major overhauls of data governance to selectively utilizing existing data, emphasizing that context is crucial when implementing AI strategies.
👉 Pročitaj original: CIO Magazine
