RPA UiPath: Back-Office Efficiency and Data Integrity at Scale

The client is the second-largest global sportswear manufacturer and the company behind the "their digital transformation strategy" strategy that committed over a billion euros to digital transformation through 2025. The 2025 results land at tens of billions in net sales, billions in direct-to-consumer revenue, and multi-billion operating profit with strong e-commerce growth. Their loyalty program with over 300M members operates across roughly fifty countries; e-commerce runs in sixty-five. Taller's engagement at this client has run continuously since September 2021, sourced through a staffing partner. The dominant workstream is the North America intelligent process automation program on UiPath — captured in Volume II under the RPA Automation Anywhere case with $2M+ annual cost savings. Adjacent workstreams cover Power BI analytics for the client's tech delivery hub (also captured in Volume II), an eCommerce/B2B/Retail platform-engineering pod under the client's tech delivery hub, Databricks data engineering, SAP integration and functional business-analysis capability, EDI engineering for B2B trading, and the Latin America tech infrastructure team service-management team in Brazil and Mexico. The three validated use cases below land in three of these adjacent surfaces.

The global sportswear manufacturer needed to improve back-office efficiency and data quality at a scale where manual entry both consumed expensive headcount and introduced reconciliation defects. RPA alone does not solve the problem; instead, the underlying data substrate has to be hardened simultaneously, or the automation amplifies existing defects rather than removing them.

Taller's RPA team of developers and data engineers implemented UiPath automations across finance and operations workflows while parallel work hardened the underlying SQL Server and ETL processes for performance and security. UiPath Studio and Robot deliver the automation surface; SQL Server tuning, ETL refactoring, and integration security harden the substrate the bots run against. Source control through Bitbucket and TFS treats RPA artifacts as first-class deployable code, with versioning, code review, and CI/CD discipline that distinguishes a sustainable bot fleet from a brittle one. Python and GitHub augment the team's reach into the more complex transformation logic that exceeds UiPath's native capability.

The RPA program produced more than $2M in annual cost savings.

RPA at this scale only delivers durable cost savings when the data layer underneath the bots is healthy enough that the automation does not amplify existing defects. Once the substrate is hardened, the marginal cost of automating an additional process drops to days of work — the property that converts a one-time project into a self-sustaining program that compounds across the back-office estate.