Genpact named a Rising Star in the ISG Provider Lens^® 2026 Databricks Ecosystem Partners report

Enterprises want more than a platform migration. They want a governed data foundation designed to support AI initiatives, scale with their business, and help deliver measurable value. That's the work Genpact does every day – and now there's independent recognition to back it up.

In the 2026 ISG Provider Lens^® global Databricks Ecosystem Partners report, Genpact earned Rising Star recognition in two quadrants.

This recognition comes from ISG Provider Lens^® research, produced by Information Services Group (ISG), a leading global technology research and advisory firm.

The 2026 ISG Provider Lens^®  global Databricks Ecosystem Partners study highlights several aspects of Genpact's approach to modernization and managed services.

Modernization and AI/ML enablement

• Industry-aligned data modernization: According to the report, Genpact differentiates modernization from migration through capabilities such as automated schema mapping, ETL (extract, transform, load) translation, validation frameworks, and data product engineering to support governed, consumption-ready architectures

• Industry-led AI approach: The report describes Genpact as embedding AI enablement within business programs across areas such as finance, supply chain, fraud, and pricing analytics, helping connect modernization efforts to operational decision-making

• Structured ecosystem integration: The report notes Genpact's alignment with the Databricks ecosystem through a formalized Databricks business group, co-developed industry solutions, and participation in Brickbuilder validation programs

   

Managed data and optimization

• Modern foundation governance: The report describes Genpact's approach as extending modernization into ongoing governance through Unity Catalog enablement, metadata intelligence, and data product lifecycle management

• Operational efficiency and cost oversight: The report highlights Genpact's use of automation, FinOps practices, and Databricks Unit (DBU) efficiency monitoring to assess workload efficiency and cost performance across Databricks deployments

• MLOps and model governance: The report highlights Genpact's use of MLflow-driven MLOps, version control, and Unity Catalog Feature Store capabilities, with Lakehouse Monitoring supporting drift monitoring and performance oversight

This approach emphasizes continuous optimization through cost-performance reviews and governance controls as an ongoing discipline rather than as after-the-fact support.