Drive-thru restaurants are under increasing pressure to move faster, hire more efficiently, and reduce operational strain on staff. Against that backdrop, Lee’s Famous Recipe Chicken is betting on artificial intelligence, not as a future experiment, but as a current operational tool being extended across its franchise network through Hi Auto’s AI Order Taker.

The rollout marks a significant shift for the 130-plus unit brand, which is now offering AI-powered ordering as an optional capability for franchisees following successful performance across 30 test locations.

From pilot results to system-wide opportunity

The initial phase of adoption wasn’t theoretical. Lee’s deployed Hi Auto in both company-owned and franchise locations to evaluate how the system performs under real drive-thru pressure. Those early results formed the basis for the broader rollout decision.

But equally important was what happened behind the scenes. The company invested in unifying its POS system and menu database, an often-overlooked requirement that becomes essential when scaling AI across a franchise network. Without standardized menus, AI systems struggle to maintain consistency across locations.

This groundwork positioned Lee’s to extend the technology across its system without fragmenting performance or implementation quality.

Franchise autonomy remains central

Unlike more prescriptive technology rollouts in the QSR sector, Lee’s is taking a different approach: access without obligation.

Franchisees are not required to adopt the AI Order Taker. Instead, they are given the option to implement it based on their own operational needs and business conditions.

That balance between innovation and independence is intentional.

“Our operators are the backbone of Lee’s, and it’s our job to give them every advantage we can,” said Ryan Weaver, CEO of Lee’s Famous Recipe Chicken. “After seeing the results Hi Auto delivered in our first 30 stores, including better labor efficiency, shorter lines, a happier team, and guests getting their orders just the way they want them, we wanted to make this tool available to every franchisee who wants it.”

Operational impact becomes the deciding factor

For franchisees, the conversation is increasingly data-driven. The performance of Hi Auto across early deployments offers a tangible benchmark for what AI ordering can deliver in a live restaurant environment.

Key results include order completion rates above 95% and accuracy levels reaching 97%. These figures are not isolated test conditions but reflect real-world store operations across multiple locations.

Beyond order handling, labor efficiency gains are significant. Stores report saving three to eight labor hours per day, reducing turnover by 17%, and increasing average ticket size by roughly 1.5%.

These outcomes translate into both cost savings and revenue enhancement, two critical pressures in today’s QSR environment.

AI as a labor and experience tool

The impact of AI ordering extends beyond metrics. By shifting order-taking responsibilities away from staff, employees are able to focus more on food preparation and customer service.

This redistribution of tasks helps reduce burnout during peak periods and improves consistency at the front line, an increasingly important factor in a tight labor market.

Hi Auto’s broader ecosystem strengthens credibility

Hi Auto brings scale to the partnership. With nearly 1,000 drive-thru locations globally and over 100 million orders processed annually, the company has already tested its AI Order Taker across diverse operational environments and franchise systems.

Hi Auto CEO Roy Baharav described the partnership as aligned with franchise empowerment, noting that effective technology investments should strengthen operators rather than replace them.

A controlled evolution of the drive-thru

Rather than a disruptive overhaul, Lee’s approach reflects a controlled evolution of its drive-thru operations. The combination of infrastructure modernization and optional AI adoption allows franchisees to move at their own pace while still accessing proven technology.

As adoption expands, the system may serve as a case study in how mid-sized QSR brands can integrate AI without forcing uniform deployment.