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Rohit Khanna, Chief Customer Officer at Smarsh, breaks down how AI agents are reshaping customer service operations in regulated industries, with a focus on financial services compliance. He shares how Smarsh implemented Salesforce Agentforce to reduce level-one support demand and improve service outcomes without adding headcount.
The conversation covers the strategy behind deploying agents safely in high-stakes environments, including building purpose-built compliance models and adding data governance layers to maintain accuracy and regulatory alignment. Rohit also explains how personalization (including naming the agent “Archie”) helped drive adoption and trust as workflows move from human-in-the-loop to agent-in-the-loop.
Key Topics Covered:
Episode Timestamps:
00:00 - Introduction to Rohit Khanna and Smarsh
02:44 - What is Smarsh? Compliance technology explained
04:13 - Building AI from the inside out: Proprietary vs partnered models
07:33 - Agentforce implementation journey and challenges
12:26 - Results: 56% deflection rate and productivity gains
15:25 - The power of personalization: Why “Archie” matters
18:21 - Trust and data governance in regulated industries
22:11 - Data governance layers and policy management
25:49 - Human-in-the-loop vs agent-in-the-loop
29:10 - Upskilling teams for the AI-powered future
32:11 - Intelligent agents for financial crime detection
About the Guest:
Rohit Khanna is the Chief Customer Officer at Smarsh, overseeing global customer support, consulting, migrations, managed services, and Smarsh University. With nearly six years at the company, he has led Smarsh’s shift toward AI-powered compliance solutions and manages teams across the Philippines, India, Belfast, Costa Rica, the UK, and the US.
About the Company:
Smarsh is a compliance technology provider for regulated industries, focused on electronic communications archiving, surveillance, and AI-powered financial crime detection. For 20 years, Smarsh has served as a custodian of communications data for major financial institutions, using proprietary AI models and GenAI agents to detect misconduct, reduce false positives, and support regulatory compliance.