Organizations around the world are pouring significant resources into artificial intelligence (AI), with more than 90% of Chief Information Officers (CIOs) increasing their budgets for AI-related projects, according to research firm Gartner. This has made AI the fastest-growing segment of enterprise technology spending. As companies aim to integrate AI into their operations—ranging from real-time data analysis to personalized customer experiences—they are also ramping up their investments in data infrastructure. On average, enterprises now spend $29.3 million annually on data programs, covering everything from data movement and preparation to cloud computing and internal engineering support. Despite this growth, many businesses are still struggling to realize the full potential of AI. Underlying weaknesses in their data architecture are slowing down progress and limiting returns. Nearly two-thirds of data initiatives are underperforming, and 73% of organizations report that their data projects are not meeting expectations. About 62% of companies have low data maturity, indicating a gap between what they hope their data and AI initiatives can achieve and what their current infrastructure can support. Poor data infrastructure can have tangible effects on business performance. In large organizations, data pipeline failures cause over 60 hours of downtime each month, costing an estimated £50,000 per hour. Data teams are also heavily burdened, spending more than half of their engineering capacity on maintaining pipelines rather than developing new AI applications. This highlights the need for more robust and flexible data systems that can support the growing demands of AI. One promising solution is the adoption of Open Data Infrastructure (ODI), an architectural approach that allows organizations to have greater control over how data is accessed, processed, and used. ODI relies on open standards and modular components, enabling different tools and platforms to work together seamlessly. Unlike traditional systems that are tightly integrated and proprietary, ODI separates storage from computation, allowing each part to be upgraded independently. This flexibility supports efficient scaling of analytics and AI workloads while reducing reliance on specific vendors. As AI becomes more central to business operations, the need for a unified data environment becomes more urgent. ODI also challenges the trend of vendor lock-in, where companies become dependent on a single provider’s ecosystem. With AI systems increasingly acting as primary data users—often outnumbering humans by a ratio of 82:1—having a shared, consistent data source is critical. Inconsistent data definitions across systems can lead to conflicting decisions and unreliable AI outputs. ODI ensures that all systems, whether human or automated, have access to a unified view of the business, enabling more efficient and accurate decision-making. Organizations that adopt open data foundations are better positioned to harness AI's full potential, reduce costs, and drive innovation.