This article uncovers parallels between Pilot & Demonstration (P&D) programs and agile research and development (R&D) in data science teams. Drawing from previous research, it presents five key strategies to enhance R&D agility: the embrace of iterative development, promotion of knowledge spillovers, the importance of showcasing successes, cultivating a culture that views failure as a learning opportunity, and nurturing critical thinking to avoid unreflective technology adoption. This holistic approach can effectively guide data science teams towards improved adaptability and innovation in an ever-evolving technological landscape.
Read moreRevolutionizing Competitiveness with Generative AI: Linking Internal Data Assets through Retrieval Augmented In-Context Learning
AI is a General Purpose Technology with the potential to transform multiple industries and impact society as a whole. This introduces the need for companies to innovate rapidly to stay competitive, and one promising approach is to link generative AI models with company-specific data assets through retrieval-augmented in-context learning. This allows businesses to create custom AI models that can generate more accurate and relevant output, improving their data security and privacy, and staying ahead of the curve in the rapidly-evolving landscape of generative AI.
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