Case Study

AI-Assisted Data Conversion, Validation & Cleansing

Using AI-assisted validation to identify data issues earlier, improve conversion accuracy, and reduce downstream testing and schedule risk.

RESULTS

Accelerated data readiness​

Minimized downstream disruption​

Reduced delivery risk and schedule delays​

OVERVIEW

Project

Large-scale modernization initiative

Industry

California State Government​

Client

Large state department​

The Challenge

Critical Knowledge Gaps and Data Complexity Threatened Project Momentum

The client was progressing through a major modernization effort while simultaneously transitioning teams and onboarding new resources. Critical knowledge about legacy data, business rules, workflows, and system dependencies was distributed across teams and documentation.

Data conversion and validation required significant investigation, comparison, and reconciliation before teams could confidently use information for development, SIT, UAT, and production-readiness activities. This created additional risk to an already compressed schedule.

Our Solution

Embedding AI Across Teams to Accelerate Data Readiness and Reduce Manual Effort

An embedded AI Lab worked across data, development, testing, and business teams to accelerate data readiness. AI-assisted processes identified and validated data issues, supported SIT and UAT preparation, and converted recurring validation patterns into reusable automation. 

Once repeatable logic was established, activities transitioned to conventional automation to reduce ongoing AI usage and costs.

Results

Improved Data Quality and Visibility Reduced Manual Effort, Project Risk, and Schedule Uncertainty

The approach improved the quality and visibility of project information while reducing the manual effort required to investigate data issues.

Teams gained faster access to validated information, enabling them to support testing and conversion activities without placing additional burden on already-constrained project resources. More reliable data also contributed to reduced project risk and better schedule predictability.

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