{
  "id": 7302399,
  "title": "Closing the gap between AI investment and impact: the rise of Open Data Infrastructure",
  "url": "https://urgent.news/2026/09/14/closing-the-gap-between-ai-investment-and-impact-the-rise-of-open",
  "topic": "ai",
  "section": "AI",
  "published": "2026-09-14T10:49:49.000Z",
  "source": {
    "name": "TechRadar",
    "slug": "techradar",
    "url": "https://www.techradar.com/pro/closing-the-gap-between-ai-investment-and-impact-the-rise-of-open-data-infrastructure"
  },
  "original_language": "en",
  "account": "The demand for artificial intelligence (AI) within businesses has reached unprecedented heights, with a staggering 90% of Chief Information Officers (CIOs) worldwide planning to increase their AI spending, as reported by Gartner. This surge in investment correlates directly with the expansion of AI applications, ranging from real-time analytics to personalized customer experiences. However, this escalating expenditure on AI is not translating into equal improvements in data-driven performance. Despite significant annual investments of around $29.3 million per enterprise—encompassing data movement, ingestion, preparation tooling, recurring cloud costs, and the internal engineering required for pipeline maintenance—many companies are still failing to unlock the full potential of AI.\n\nData initiatives are underperforming in more than two thirds of businesses, with 73% of organizations admitting their data-centric projects fall short of expectations. Alarmingly, 62% of these enterprises exhibit low data maturity, indicating a substantial disconnect between the desired capabilities of AI and the infrastructure supporting them. This gap results in measurable setbacks for enterprises, with downtime due to data pipeline failures reaching over 60 hours per month in large organizations, translating to a cost of approximately £50,000 per hour in lost productivity. Furthermore, data teams spend over half of their engineering capacity maintaining pipelines instead of innovating new use cases.\n\nTo bridge this divide and foster AI innovation, Open Data Infrastructure (ODI) has emerged as a transformative solution. ODI is an architectural framework that empowers organizations to manage data access, movement, and usage more effectively by adhering to open standards. By decoupling storage from compute components, ODI allows for independent evolution of these layers, enabling a unified data environment conducive to scalable analytics and AI operations. This approach also counters vendor lock-in, which has become increasingly restrictive as data becomes more proprietary. Traditional data ecosystems often impose hidden costs and dependencies that restrict companies to specific walled-garden solutions. AI's dominance in data consumption is evident, with non-human entities outnumbering humans in these environments by 82 to 1, necessitating a shared source of truth for cohesive decision-making. Open architectures facilitate this alignment by providing consistent data definitions across both human-operated and automated systems, thus preventing misaligned outcomes and redundant engineering efforts.\n\nMoreover, ODI directly addresses the escalating costs associated with data management, particularly in large-scale operations. Modern data management systems designed with flexibility, portability, and reliability in mind can significantly reduce expenses related to data pipelines, especially as enterprises scale to hundreds of these processes. Organizations leveraging legacy systems face disproportionate costs, with estimates suggesting that modern data management can cut these expenses by up to 50%, thus freeing up resources for more strategic endeavors. Research underscores the critical link between data maturity and the success of AI initiatives, revealing that entities with robust, managed, and open data foundations are nearly twice as likely to surpass their return on investment (ROI) targets. This evidence underscores the direct correlation between investing in open data infrastructure and achieving sustained success in AI-driven endeavors.",
  "summary": "Organizations are investing heavily in AI – but are their data foundations holding them back?",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}