Data immaturity leads to billions in wasted AI spend

Group 1 - The core issue in AI implementations is the data and infrastructure, which are creating bottlenecks for services and hindering model training for specific use cases [3][4] - Hyperscalers are significantly increasing capital investments, with plans to raise spending by nearly 40% this year to meet the soaring demand for AI services, as reported by S&P Global [4] - Data quality is identified as the most critical factor for successful AI implementations, with data sprawl in enterprises potentially hampering broader AI adoption [5][9] Group 2 - A Hitachi Vantara analysis indicates that data infrastructure issues are leading to an annual waste of $108 billion in AI spending, based on a survey of 1,200 IT decision-makers [9] - Companies with mature data estates report a higher ROI on AI investments, with over 80% achieving positive returns, compared to less than 50% of "data laggards" [9] - Despite existing data limitations, IT leaders anticipate a 76% increase in AI spending over the next two years as businesses develop in-house platforms and expand deployment efforts [9]