Retail, QA

From Fragmented QA to AI-Augmented Quality Engineering

Retail, QA

From Fragmented QA to AI-Augmented Quality Engineering

The Client

A large network. A growing need for consistent quality.

The client is a large retailer-owned hardware cooperative with thousands of independently operated stores in the United States and abroad. Its scale and pace of technology change required a more consistent, resilient quality-engineering model across enterprise applications.

The Challenge:

Fragmented QA practices could not scale with the business.

The existing QA model relied on inconsistent processes, fragmented tools, and significant manual effort.

Key challenges included:

  • Release delays that increased time-to-market and business risk
  • Production defects and defect leakage that affected user experience
  • Inconsistent test management and limited QA and test-data governance
  • Undocumented test cases and critical knowledge held by individuals
  • Duplicative manual testing across applications
  • Inconsistent quality gates and limited early defect prevention
  • Multiple automation tools, creating duplication and maintenance overhead
  • Limited functional and automation coverage across critical applications

The client engaged HTC Global Services to establish a scalable quality-engineering operating model that combined governance, automation, and targeted use of AI.

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