Legacy systems may limit business AI potential.

A Digital Journal Q&A reports that messy legacy data and outdated architectures are keeping many AI pilots from scaling, and the interviewee says modernizing the data foundation and governance is central to moving from experimentation to measurable outcomes.

Enterprises are moving quickly to adopt AI, yet many early projects do not scale into lasting business value, the article reports. Legacy systems, fragmented data, and older architectures are cited as common barriers. Digital Journal spoke with Arun "Rak" Ramchandran, CEO of QBurst, who discusses why a modern data foundation and governance matter and how organizations can progress without disrupting core operations. The interview contrasts short-term AI additions with longer-term design changes that align data, architecture and controls. Key points: - Legacy systems often contain messy data and workflows that are not ready for AI's speed and scale; without context, AI can amplify errors. - The article highlights that modernization does not always require full replacement; integrations, wrappers and hybrid DataLakes can connect legacy apps to AI scaffolding. - Prioritizing the Data Foundation—data estate modernization, advanced data engineering and governance—is presented as essential for scalable AI outcomes. - Practical, reported approaches include internal augmentation in department-level use cases and using cloud or hybrid platforms to centralize shadow copies for analytics. - The interview frames a shift from "AI fascination" to "AI accountability," noting outcome-based commercial models and managed agents as emerging patterns. Summary: The piece reports that legacy technology and fragmented data are common reasons AI initiatives stall at the pilot stage, and that a strengthened data foundation and governance framework are central to moving toward measurable ROI. It describes transitional options such as departmental augmentation, hybrid DataLakes, and cloud-enabled infrastructures as ways organizations are reported to progress while retaining core systems. The article frames these steps as part of a broader shift in how enterprises design for AI outcomes rather than layering AI over legacy apps.