Closing the gap between AI investment and impact: the rise of Open Data Infrastructure

Date:
Mon, 14 Sep 2026 10:49:49 +0000

Description:
Organizations are investing heavily in AI but are their data foundations holding them back?

FULL STORY ======================================================================Copy link Facebook X Whatsapp Reddit Pinterest Flipboard Threads Email Share this article 0 Join the conversation Follow us Add us as a preferred source on Google Newsletter Subscribe to our newsletter The appetite for AI in the market has never been greater. According to Gartner, over 90 percent of CIOs globally are increasing funding in AI, making it the fastestgrowing area of enterprise technology spend. As organizations look to integrate AI-powered workflows, from real-time analytics to personalized customer experiences , this ambition is accelerating investment in data initiatives.

Additional research shows that enterprises now spend an average of $29.3 million per year on data programs which encompasses data movement, ingestion and preparation tooling, recurring cloud ingest and compute costs, and the internal engineering capacity required to keep pipelines running. Anjan Kundavaram Social Links Navigation

Chief Product Officer at Fivetran. While this shift in spend mirrors the demands of scaling AI (organizations with successful AI initiatives invest up to four times more in data and analytics foundations), higher budgets do not automatically result in highquality data. Many businesses continue to miss
out on the transformative impact of AI, held back by underlying weaknesses in their data architecture that slow delivery and limit returns. Latest Videos From TechRadar Watch full video here: Almost two thirds of data initiatives are underperforming Despite unprecedented levels of investment, the majority of enterprise data initiatives continue to underperform with 73 percent of organizations reporting their data initiatives are falling short of expectations. At the same time, nearly 62 percent report low levels of data maturity, pointing to a persistent gap between what organizations want their data and AI initiatives to deliver, and what their infrastructure is equipped to support.

Weak data foundations constrain innovation and carry measurable consequences for enterprise performance. In large organizations, downtime caused by data pipeline failures now exceeds 60 hours a month, disrupting productivity and costing an estimated 50,000 per hour in business impact. Data teams are also affected, as they spend over half of their engineering capacity on pipeline maintenance, rather than advancing new use cases. You may like The biggest barrier to AI success isn't AI Could infrastructure demands be what's really holding AI back? Why AI infrastructure planning must happen now Open Data Infrastructure as the foundation for AI Beyond the daytoday costs of downtime and maintenance, the deeper impact of unreliable data foundations is consistent disruption of AI initiatives. For AI systems to thrive, organizations need democratized, interoperable data programs, where access to data is fast, governed and reliable. In response, Open Data Infrastructure (ODI) has emerged as the foundation for AI.

ODI is an architectural approach that gives organizations greater control
over how data is accessed, moved and used, by allowing tools and platforms to work together through shared, open standards. Instead of relying on tightly coupled, proprietary systems, ODI is built on a modular, standardsbased foundation that separates storage from compute, enabling each layer to evolve independently. Are you a pro? Subscribe to our newsletter Sign up to the TechRadar Pro newsletter to get all the top news, opinion, features and guidance your business needs to succeed! Contact me with news and offers from other Future brands Receive email from us on behalf of our trusted partners
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As data and AI workloads continue to grow, this creates a unified data environment where analytics and AI can scale more efficiently.

ODI is also emerging as a direct challenge to vendor lockin. The industry is seeing a shift towards data becoming more restricted, both technically and commercially. Often, these constraints show up as hidden costs or
dependencies that push companies toward specific walled-garden ecosystems.

This problem is amplified when AI entities become an organization's primary data users. Indeed, studies suggest that non-human entities are present in modern enterprises at a ratio of 82:1 compared to humans. What to read next The AI data problem nobody talks about: Why more information isn't making better decisions Why enterprises need to rethink data in the AI era AI is messy: here's how to clean up your data before it derails your strategy

For AI agents to work effectively alongside human users, a shared source of truth is essential. Dashboards, operational workflows, machine learning
models and AI agents may all draw from the same underlying data, but often operate in separate environments with different definitions and models.

When those definitions drift, the result can be misaligned decisions, unreliable AI outputs and additional engineering overhead. ODI helps address this by giving every system, human or automated, a consistent view of the business.

Furthermore, AI agents generate exponentially more queries than humans, but closed ecosystems often route them through the same expensive compute infrastructure. Agents can only optimize for cost opting for cheaper compute engines when appropriate when open architectures afford them the opportunity to choose. And the cost considerations dont stop there.

Organizations using legacy systems pay significantly more per data pipeline, which, when multiplied by the hundreds of pipelines at enterprise scale, adds up to a significant, ongoing expense. Modern data management: flexible, portable, trusted As investment in AI tools continues to ramp up, organizations must think ahead to alleviate the strain on both budgets and engineering resources. They should ensure AI systems have consistent access
to fresh, trustworthy and context-rich data while maintainingcontrol of their data andarchitectureto avoid lock-in.

Those that prioritize open foundations will create the right conditions for innovation and enable their data teams to focus on delivering real business value, from predictive modelling and real-time analytics to faster agent production.

The impact is ultimately reflected in performance outcomes. Research shows that organizations with modern, managed and open data foundations are nearly twice as likely to exceed their ROI targets than those relying on legacy systems evidencing the direct correlation between data maturity and measurable success of AI initiatives. We've featured the best AI website builder. This article was produced as part of TechRadar Pro Perspectives ,
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