Enterprises have poured billions into data centralization, yet despite bold AI roadmaps, many organizations are hitting roadblocks — delays, underperformance, and outright failures in AI initiatives. In fact, of enterprises that have centralized less than 50% of their data, 68% report lost revenue opportunities due to AI project delays or failures.
The issue isn’t a lack of vision — it’s the gap between strategy and execution. AI can’t deliver results if data isn’t fully centralized, governed, and ready for action.
Our new report highlights poor data readiness as a leading factor in undermining enterprise AI implementation and readiness on a wide scale. The report, which surveyed over 400 global data leaders and professionals, emphasizes the long-term need for AI-ready data in order to achieve AI implementation and success.
[CTA_MODULE]
The business cost of poor AI execution
AI is no longer an experimental technology — it’s a critical driver of efficiency, revenue, and customer engagement. When AI initiatives fail due to execution challenges, the consequences extend beyond IT and negatively impact business growth, operational costs, and customer satisfaction. Businesses struggle to identify market trends, optimize pricing strategies, improve efficiency, and deliver AI-powered recommendations that drive sales and retention.
AI inefficiencies don’t just slow innovation — they increase costs:
- 38% of enterprises cite increased operational costs due to AI project failures.
- 67% of centralized enterprises allocate over 80% of their engineering resources to maintaining pipelines, increasing operational expenses.
If enterprises do not get to the root cause of AI implementation failure, they will not be able to compete in ever-advancing marketplaces.
The hidden AI roadblock: Why data readiness defines success
AI initiatives stall when teams can’t efficiently prepare, integrate, and operationalize their data, and 42% of enterprises currently report that over half of their AI projects have been delayed, underperformed, or failed due to data readiness issues.
Organizations often struggle to ensure their data is fully centralized, governed, compliant, and AI-ready. Major roadblocks to getting to that ideal data state include:
- Integration bottlenecks – 74% of enterprises manage or plan to manage over 500 data sources, significantly increasing complexity.
- Pipeline maintenance overload – 67% of highly centralized organizations spend over 80% of their data engineering resources maintaining pipelines, leaving little room for AI innovation.
- Data readiness gaps – 41% of companies report that real-time data access issues prevent AI models from delivering timely insights. (MIT)
- Data silos – 29% of enterprises report that data silos block AI success.
To unlock AI’s full potential, organizations must address the fundamental challenges of data readiness in order to open the floodgates of data accessibility.
How enterprises can achieve greater AI success
AI initiatives aren’t failing because organizations lack data — they’re failing because organizations can’t efficiently prepare, integrate, and operationalize that data.
To fully capitalize on AI’s potential, enterprises must eliminate data silos and embrace automation. With nearly 70% of organizations with highly centralized data allocating over 80% of their data engineering resources to building and maintaining pipelines, there is little bandwidth for AI innovation. This imbalance not only hinders scalability but also drives up operational costs. To shift from maintenance to value creation, organizations need modern data integration tools that automate pipeline management. By investing in automation-first infrastructure, enterprises can reduce long-term costs, enhance agility, and ensure their AI initiatives are fueled by reliable, high-quality data.
Additionally, AI initiatives depend on seamless data integration, yet many enterprises struggle to connect their rapidly expanding data ecosystems. Without a modern, automated integration strategy, AI models risk operating on incomplete or inconsistent data, producing unreliable outputs that erode trust and limit impact. By investing in low-code or automated integration solutions and scalable infrastructure, enterprises can streamline workflows to reduce the engineering burden and accelerate AI adoption — unlocking more accurate insights, faster innovation, and greater competitive advantage.
Finally, AI outcomes depend heavily on having a reliable, performant data system that delivers high-quality, up-to-date information. Without seamless integration, AI models may produce misleading results that undermine trust and decision-making. A modern, automation-first data architecture ensures that data pipelines are not only scalable but also resilient, secure, and accurate — powering better, faster decisions across the enterprise and improving the experience for everyone who depends on that data.
[CTA_MODULE]
