The Emergence of Decision Trust Infrastructure | Chiru Bhavansikar

Chiru Bhavansikar, Founder and CEO of Arhasi, presents a 1ArtificialIntelligence session on the rise of decision trust infrastructure, the operational layer that helps enterprises make AI decisions traceable, explainable, and defensible. The session focuses on why enterprise AI adoption depends not only on model capability, but on the ability to know, show, and defend how a critical AI decision was made.

Bhavansikar frames the challenge around a growing enterprise AI trust deficit. Seventy-three percent of enterprise AI projects never move beyond proof of concept, the average AI-driven compliance event in regulated sectors is approximately $4.2 million, and fewer than 30 percent of AI outputs are explainable. His argument is direct: as AI moves from co-pilots and chatbots into mission-critical workflows and agentic systems, organizations need infrastructure that can trace decisions back to data, model versions, policies, and the systems that shaped the result.

The session introduces Integrity-First AI as an operating discipline and explains the difference between guardrails and real policy management. Guardrails can help control what AI systems do, but enterprise-scale AI requires a broader trust infrastructure stack built on data integrity, explainability, traceability, and policy enforcement. For business leaders, the session provides a practical framework for moving from reactive AI monitoring to managed, proactive, and ultimately defensible AI that can support board confidence, regulator readiness, customer trust, and enterprise-scale deployment.