Global QSR Speeds Software Delivery With QA Automation

As this leading global quick-service restaurant chain continued rolling out new digital products and features, it needed a way to support faster QA across its engineering organization.

Publicis Sapient worked with the organization to remove the engineering bottlenecks that had been holding back QA automation.

This QSR giant has been rolling out new digital capabilities across locations in 115+ countries. Their massive global footprint runs on multiple localized platforms, requiring customized scripts across the ecosystem. Engineers constantly built customizing tools and tracking frameworks from scratch. As demand for AI capabilities grew, so did pressure on the team to keep pace with shorter and shorter release cycles. And for a regularly short-staffed team, this was a tall order.

Deploying AI-enabled QA for a single engineer or application is one thing. Scaling it across a global engineering organization is far more complex.

This QSR initially attempted the rollout on its own, but the level of customization, governance oversight and required internal coordination placed a significant strain on its already stretched teams. As a result, QA remained a bottleneck, limiting the organization’s ability to improve speed, consistency and quality at scale.

Publicis Sapient introduced Sapient Slingshot to remove the QA bottleneck. Instead of piecing together generic AI components and building governance from scratch, the team used Slingshot to get working automations into production quickly by using the company’s existing knowledge sources.

The work focused on three priorities:

• Pre-built component deployment: Deploy pre-built QA agents instead of building custom automation
• Governance integration: Introduce built-in monitoring and governance from day one
• Engineering augmentation: Provide foundational templates and dedicated engineering support to augment a short-staffed QA team and accelerate deployment

Publicis Sapient worked alongside the company’s engineers to configure the platform, adapt reusable templates and absorb work from a team already at capacity. Slingshot’s AI coding capabilities consistently outperformed the development tools the engineers had been using.

The result was a production-ready approach to QA automation that was built to scale across the business.

The pilot proved the approach could work at enterprise scale. Early results included:

• >75% projected savings
• 100% automation across targeted QA scripts
• Two months from pilot to ready-to-scale solution

QA automation is no longer just another engineering project. Teams now have a repeatable framework with built-in governance and reusable templates that they can use across products and markets without increasing headcount or starting from scratch every time.

The company is now preparing to expand QA automation across thousands of users, giving engineering teams a faster, more consistent way to support software delivery across its global business.