The piece highlights the difference between merely training AI models and their application in real-world scenarios that impact business operations. Craig Partridge from HPE underscores that trusted AI inference is critical for achieving value, especially for industries reliant on accuracy. Despite progress in operationalizing AI, many organizations still struggle with transitioning from pilot projects to full-scale implementations, often hindered by a lack of data quality and trust in AI outcomes. The article also explains how organizations are moving from a model-centric view to a data-centric approach, focused on unlocking value from data rather than solely on developing sophisticated AI models. This shift requires thoughtful data governance and an effective IT strategy to scale AI services across enterprises, addressing challenges reminiscent of early cloud adoption failures. Partridge warns against the dangers of shadow AI, wherein unapproved AI tools proliferate, emphasizing the necessity for structured oversight and a cohesive data platform strategy.
👉 Pročitaj original: MIT Technology Review – AI
