Most AI budgets are being wasted on the data underneath the model, not the model itself. 

Forrester’s new report, AI Data Fabric Supercharges Enterprise Data For AI At Scale, puts a fine point on something CIOs already feel in their gut: “In an era when AI shapes every competitive edge, enterprises must ensure the data fueling it is trusted, consistent, and real time, or risk falling behind.” Every AI pilot that stalls, every model that produces answers nobody trusts, every compliance review that turns up more questions than answers traces back to the same root cause: fragmented data. 

For businesses, this is a business risk that belongs on the leadership team’s radar because the costs are already adding up. 

The Real Price Tag on Fragmented Data 

Forrester’s research points to data quality as one of the top challenges organizations report today. That challenge shows up in three specific ways. 

• Wasted AI investment. Every pilot that never makes it past a proof of concept represents real spend. Data science hours, cloud computing, vendor licensing, all with nothing to show for it. Forrester’s framing is direct: AI only delivers at scale when it sits on top of data that is trusted, accessible, and governed. Skip that foundation, and pilots stay pilots indefinitely. 

• Compliance and governance exposure. When data lives in silos across systems, business units, and clouds, no one can answer “where did this number come from?” with any real confidence. That is a governance gap today. It becomes a regulatory finding tomorrow, particularly as AI-driven decisions face more scrutiny. 

• Competitive lag. While your team reconciles spreadsheets and chases down source-of-truth content, competitors with a cleaner data foundation are already running real-time AI use cases. Forrester notes that generative AI and LLMs can now automate integration, entity resolution, and data mapping across silos in real time. The gap between organizations that are data-ready and those that are not is widening faster than it used to. 

What Forrester’s Reference Architecture Recommends 

Forrester does not treat this as a data hygiene project. The fix is architectural: a data fabric that acts, in Forrester’s words, as “a multiplier for data teams by dramatically increasing their ability to deliver trusted, high-quality data at scale for AI.” In practice, that means a unified, governed layer spanning cloud and on-premise environments, with AI automation taking over much of the manual work of managing, integrating, and governing data. 

That shows up as five capabilities Forrester calls out directly: 

• Natural language access to data, so business users can query enterprise data conversationally instead of waiting on a report request 

• Automated data integration, using generative AI to handle code generation, entity resolution, and mapping across systems that were never designed to talk to each other 

• Similarity search through vector databases, surfacing relevant data by context rather than exact match 

• Real-time data quality, with anomaly detection and cleansing happening continuously instead of in a quarterly audit 

• Real-time security and governance, automating classification and access policy enforcement as data moves 

Why This Belongs on the Leadership Agenda 

The instinct is to treat data fabric as an infrastructure investment that IT would implement. That would be a mistake. Every cost listed above shows up in business terms the rest of the leadership team already tracks: wasted AI spend, compliance exposure, competitive lag. Framed that way, funding the data foundation stops being a hard sell. It becomes the obvious next line item in the AI roadmap conversation. 

The conversation shifts once you frame it this way. It is no longer “we need to fix our data pipelines.” It becomes “here’s what fragmented data is costing us, and here’s the architecture that fixes it.” 

Where to Start 

You don’t need to rebuild your entire data estate to make progress. Most organizations get the most value by starting with the systems feeding their highest-priority AI use cases. These are often the same integration points, ERP, EDI, and legacy systems, that have been patched together over the years rather than designed as a coherent whole. 

That is exactly the layer where DataXstream spends most of its time, untangling the integration foundation underneath enterprise systems so the data flowing through it is trustworthy enough to build on.