Hi Auto AI Drive-Thru Technology Expands Across Lee’s Famous Recipe Chicken Franchise System After Strong Multi-Location Results

As labor pressures and operational demands continue to intensify across the quick-service restaurant industry, brands are increasingly turning to automation to stabilize core workflows. Lee's Famous Recipe Chicken is now expanding access to Hi Auto's AI Order Taker across its franchise system following successful deployment in 30 live locations.

The expansion reflects a shift from early validation to broader system availability, supported by both infrastructure readiness and consistent operational performance.

Infrastructure Standardization Enables Scalable Deployment

Before introducing AI ordering at scale, Lee's completed a critical internal modernization effort: unifying its POS system and menu database across its restaurant network. This step created a consistent operational framework across locations that previously had varying configurations.

This standardization is essential for AI-driven ordering systems, which depend on structured and uniform data to function reliably across multiple sites.

With this foundation in place, Lee's deployed Hi Auto in 30 locations, including both company-owned and franchise-operated restaurants, to evaluate performance under real-world drive-thru conditions.

Optional Adoption Reflects Franchisee-Centric Strategy

Rather than mandating system-wide implementation, Lee's is making Hi Auto available as an optional tool for franchisees. This ensures operators retain full control over whether and how they integrate the technology into their restaurants.

The approach reflects a broader franchise philosophy that prioritizes autonomy while still investing centrally in tools that improve system-wide performance.

Ryan Weaver, CEO of Lee's Famous Recipe Chicken, described the intent behind the rollout: "Our operators are the backbone of Lee's, and it's our job to give them every advantage we can."

He highlighted improvements observed in early deployments, including reduced labor strain, faster drive-thru throughput, and improved order accuracy.

Strong Performance Metrics Support Expansion

Across participating locations, Hi Auto's AI Order Taker has achieved more than 95% order completion rates and 97% accuracy in live drive-thru environments. These results are particularly important in high-volume settings where small errors can quickly escalate into operational bottlenecks.

Beyond order performance, the system has delivered broader operational benefits. Restaurants report saving three to eight labor hours per day, reducing employee turnover by 17%, and increasing average ticket size by approximately 1.5%.

These outcomes suggest that the system is influencing both efficiency and revenue performance simultaneously.

Labor Is Being Reallocated, Not Removed

One of the key operational shifts introduced by AI ordering is the redistribution of frontline labor. Instead of employees managing both order-taking and service tasks, AI handles the ordering function.

This allows staff to focus more on food preparation, order quality, and customer interaction. During peak periods, this shift reduces stress and helps stabilize operations when demand spikes.

Over time, this restructuring of labor roles may prove as impactful as the efficiency gains themselves.

Hi Auto's Scale Strengthens Confidence In Rollout

Hi Auto operates at significant scale, powering nearly 1,000 drive-thru locations globally and processing more than 100 million orders annually. The platform is also used by approximately 200 franchisees across multiple regions.

This broad deployment base provides validation across different operational environments, reinforcing confidence in its ability to perform consistently at scale.

Hi Auto CEO Roy Baharav has emphasized that the company's goal is to empower operators by improving workflows rather than replacing human roles in service environments.

A Measured Evolution Of The Drive-Thru Model

Lee's approach to AI adoption is incremental and infrastructure-driven rather than disruptive. By first standardizing backend systems and then layering AI capabilities on top, the company is enabling scalable innovation without forcing uniform change.

This allows each franchisee to adopt the system based on their own operational needs while still benefiting from centralized investment in modernization.

As adoption expands, the rollout may serve as a reference point for how QSR brands can integrate AI into core operations while preserving flexibility, consistency, and franchise independence.