Why Agentic AI Projects Fail When They Leave the Pilot Stage

Why It Matters

Without solid data governance and cross‑functional leadership, AI agents amplify errors at scale, turning pilot successes into costly production failures.

Key Takeaways

• Data quality, not model, drives agentic AI success.
• Robust governance across ingestion, processing, and output is essential.
• Pilot projects often fail when scaling due to hidden data debt.
• Cross‑functional leadership (CDAO, CIO, CAIO) must align on roadmaps.
• Prioritize master‑data cleansing across systems before deploying multi‑agent orchestrations.

Summary

The discussion centers on why agentic AI initiatives stumble after the pilot stage, emphasizing that the root cause lies not in the underlying models but in data quality and governance frameworks. Sanjay Cura argues that while AI models are increasingly mature, organizations often overlook the critical infrastructure needed to ingest, validate, and control data throughout the AI lifecycle. Key insights include the prevalence of "data debt" and "process debt" that surface when pilots transition to production, the necessity of robust governance around data pipelines, and the complexity introduced by multi‑agent orchestrations that span disparate systems. The conversation cites Gartner’s finding that roughly 70% of AI projects fail, and highlights how limited‑scope pilots on static data mask the challenges of real‑time, high‑volume environments. Illustrative examples range from the early RPA wave—where automation was driven by broken IT systems—to a supply‑chain client whose returns‑automation effort faltered due to poor master‑data quality. The dialogue also underscores the ripple effect of a single faulty agent within a network of agents, stressing the need for clear handoff protocols and credential controls. For businesses, the implication is clear: success demands a coordinated strategy that aligns the chief data officer, chief AI officer, CIO/CTO, and business units. Prioritizing master‑data cleansing, establishing federated yet governed data sources, and embedding governance into the AI roadmap are essential steps to move beyond proof‑of‑concepts and achieve scalable, reliable agentic AI deployments.

Original Description

In this Techstrong TV interview, Mike Vizard speaks with eClerx Head of Global Technology Sanjay Kukreja about why many agentic AI challenges are not really model problems at all. Instead, they often stem from weak data governance, poor process design and a lack of controls that only become obvious when organizations try to move from promising pilots into full production.

Kukreja explains why AI pilots can look successful on small, controlled data sets but break down when they depend on real-time data, enterprise integrations and downstream systems. The conversation also explores why CIOs, CDOs, CTOs and chief AI officers need shared governance, stronger risk assessment and clear guardrails if they want to scale AI agents responsibly across the enterprise.

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