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

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 fastest‑growing 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.

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 high‑quality 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.

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.

Open Data Infrastructure as the foundation for AI Beyond the day‑to‑day 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.