Smart Governance
At the enterprise level, managing data entry and ensuring data accuracy at scale presents a significant challenge, often leading to high costs and inefficiencies.
Confronted with a $5M annual SaaS spend, low user engagement, and poor data accuracy, we led the development of an intelligent CRUD intake form. This solution reduced costs, enhanced data accuracy, and streamlined data management at the enterprise level.
Empowering teams with a more efficient, customizable, and scalable way to manage data at scale through AI.
PROJECT TYPE
DATE
The Problem
At the enterprise level, managing data entry and ensuring data integrity at scale was a significant challenge. My organization was paying $5M annually for a SaaS solution to handle metric creation and data intake. To reduce costs and improve data accuracy, we developed an intelligent CRUD intake form as an MVP.
However, as we tested the system, we encountered critical issues:
High cognitive load on users due to manual data entry across multiple free-form text and selection fields.
Data inconsistency and errors—spelling variations, missing fields, incorrect formatting, and input mistakes made the data unreliable for decision-making.
Slow data processing and inefficiencies, making it difficult to scale.
The Strategy
Standing up agile teams, automating workflows, and removing unnecessary steps—driving faster execution, reduced complexity, and scalable efficiency.
The Result
Elevated data accuracy to 95%, eliminating manual entry errors.
Reduced data processing time by 80%, significantly enhancing operational efficiency.
Enabled real-time, AI-driven validation and standardization, reducing reliance on human intervention.
Laid the foundation for broader AI adoption, proving GenAI’s value in automating enterprise workflows.
My Contribution
To incorporate GenAI as a core solution, I took the following steps:
Enterprise-Level Feasibility Analysis:
Conducted user research to assess where AI could provide the most impact.
Evaluated the effectiveness of GenAI-powered automation to improve CRUD accuracy and reduce manual intervention.
Conducted A/B testing to measure defect rates and benchmark GenAI-enhanced CRUD against the manual process.
AI-Powered Automation Implementation:
Integrated GenAI into the CRUD system to auto-correct spelling, enforce field validations, and standardize data formats.
Developed a GenAI-assisted chatbot to guide users through form entry, proactively reducing errors before submission.
Enabled real-time anomaly detection, flagging inconsistent or incomplete entries for review instead of manual audits.
Enterprise-Wide Deployment & Scaling:
Designed an AI governance framework to ensure compliance, data security, and auditability for enterprise adoption.
Integrated GenAI-powered data validation workflows into existing business systems, reducing dependency on external SaaS tools.
Led cross-functional collaboration between engineering, data governance, and business teams to ensure seamless adoption.
