Episode 10
The Hidden Infrastructure of Trust
with Amit Shivpuja
About This Episode
Amit Shivpuja, Director of Data and AI Enablement at Walmart, exposes the systems and practices that operate beneath the surface of every trustworthy AI deployment. The conversation covers data governance architecture, data lineage, and the often-invisible infrastructure that determines whether stakeholders can actually trust what AI systems produce.
Key Insights
Trust in AI systems is not built through marketing or executive proclamations; it is built through governance infrastructure that most stakeholders never see. When organizations invest in visible AI capabilities yet skip the unglamorous work of governance, they build systems that appear sophisticated while remaining fundamentally unreliable.
Data lineage is the foundation of accountability in any AI system, and organizations that skip it cannot credibly claim their AI is reliable. When stakeholders cannot trace where data came from, how it was transformed, and what assumptions were baked into it, they cannot assess whether AI recommendations are trustworthy.
The invisible systems that support data quality, access control, and provenance determine the ceiling of what AI can achieve. Building this infrastructure is unglamorous work, yet without it, every AI initiative sits on unstable ground.
Topics Explored
The episode covers data governance architecture design, data lineage and provenance tracking, trust infrastructure for AI systems, data quality management at scale, enterprise AI enablement, and the operational foundations that make reliable intelligence possible. Discussion includes how to build governance systems that don't impede innovation yet ensure accountability.
About the Guest
Amit Shivpuja is the Director of Data and AI Enablement at Walmart, where he leads efforts to build the foundational infrastructure that supports AI at massive scale. His experience spans enterprise data architecture and the governance systems that make AI trustworthy.
Questions This Episode Answers
What is the hidden infrastructure behind trustworthy AI?
Trustworthy AI depends on invisible systems including data lineage tracking, governance frameworks, quality monitoring, and provenance documentation that most users never see. These infrastructure elements determine whether AI outputs can be trusted, audited, and explained. Without this hidden infrastructure, even technically sophisticated AI systems rest on an unreliable foundation.
Why does data lineage matter for AI?
Data lineage provides the audit trail that connects AI outputs back to their source data, transformations, and processing steps. In regulated industries like healthcare, this traceability is essential for compliance, error investigation, and maintaining stakeholder confidence. When AI produces unexpected results, lineage is what makes it possible to understand why.
How do you build trust in enterprise AI systems?
Trust in enterprise AI is built through transparency, reproducibility, and accountability. Making data pipelines visible, documenting model decisions, and establishing clear ownership of AI outputs creates the conditions for institutional trust. Trust cannot be declared; it must be earned through consistent, verifiable practices.
"The infrastructure that makes AI trustworthy is invisible to most people. Data lineage, governance, quality monitoring. These are the systems that determine whether AI deserves the trust we place in it."
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Guest
Amit Shivpuja
Director of Data & AI Enablement at Walmart
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