Scaling Infrastructure Intelligence with Gen AI and Human Led Workflows
This 2025 ISG Standout Case Study highlights how WNS enabled a leading global data and insights provider to modernize and scale its infrastructure project database through a Gen AI-driven, automation-first approach.
The client faced challenges in processing large volumes of unstructured data across multiple formats, resulting in inaccuracies, re-work and slower processing cycles. Additionally, reliance on manual workflows limited scalability and made it difficult to establish consistent benchmarks for evaluating partners.
WNS implemented InfoTurf.ai, combining Gen AI-driven data extraction with a structured human-in-the-loop validation approach. This enabled automated ingestion, contextual data processing and continuous accuracy improvement through Subject Matter Expert (SME)-led feedback loops.
The Impact
45–50 percent
increase in productivity and throughput
~35 percent reduction
in average handling time
>50 percent improvement
in raw AI accuracy (from ~50 percent to >75 percent)
~100 percent
output quality with SME validation
According to ISG, this case study demonstrates how hybrid AI–human workflows can significantly enhance data quality, scalability and operational efficiency in complex, data-intensive environments.