Khaavya Shanthi Loganathan

This project included process optimization: I identified and addressed gaps in sales workflows to enhance accountability before automation.
I led the migration from legacy systems to Salesforce, driving adoption through training and stakeholder buy-in and discovering unbilled change orders. I implemented a Salesforce feature to track and notify teams, ensuring accurate invoicing.

Cross-functional alignment involved integrating Field Service Lightning for project tracking and Marketing Cloud for automated customer engagement. I also evaluated and selected Salesforce clouds, balancing business needs and budget constraints for maximum impact, recruited onshore and offshore teams, and selected implementation partners to ensure efficient execution. In addition, I ensured technology decisions aligned with business OKRs while maintaining a cost-effective investment strategy. I also implemented multiple automation workflows, reducing manual effort and improving operational efficiency by 40%.

The Salesforce Sales Cloud to Salesforce Health Cloud migration optimized payer management and healthcare operations, achieving a 35% ROI within the first year. I led comprehensive business process mapping to document features accurately, ensuring long-term scalability and alignment with organizational OKRs. I also defined user personas, security roles, and data access policies, enhancing compliance and operational efficiency across sales, claims, and customer support teams.

The project also involved managing cross-functional collaboration, driving stakeholder alignment from business, IT, and compliance teams, overseeing onshore and offshore teams, and optimizing resource allocation and trade-offs to stay within budget. I extensively leveraged Health Cloud's standard functionalities, reducing customization costs by 40% while delivering an MVP in six months. I improved operational efficiency by 30%, enhanced data accuracy by 25%, and enabled better patient and payer engagement. I also established a requirement traceability matrix to ensure all features aligned with OKRs, contributing to a 20% increase in process automation.

I led the development of an AI/ML-powered intelligent document processing system hosted on AWS, which streamlined underwriting workflows and reduced manual effort by 50%. Previously, underwriters manually reviewed extensive documents, leading to inefficiencies, especially during open enrollment when proposal volume doubled.

The solution leveraged Amazon S3 (AWS S3) for secure document storage and AWS Lambda for real-time data extraction and processing, ensuring scalability and compliance. I collaborated with underwriting, sales, and engineering teams to define business requirements and train the AI model to recognize, classify, and extract key underwriting data. I also established a feedback loop to improve accuracy and ensure compliance with regulatory requirements. We also designed a dynamic rules engine to flag missing data and inconsistencies, reducing underwriting errors by 35%.

The resulting system enabled real-time underwriting criteria validation, reducing back-and-forth communication by 60% and significantly improving turnaround times. As a result, underwriters could focus on complex cases while routine document processing was fully automated.