The modern drive-thru is under pressure from all sides. Customers expect faster service, operators are struggling with labor shortages, and order complexity continues to rise. In response, Lee’s Famous Recipe Chicken is expanding access to Hi Auto’s AI Order Taker across its franchise network after early deployments demonstrated measurable improvements in efficiency and accuracy.
Rather than presenting AI as a radical reinvention, Lee’s is integrating it as a practical operational tool built on top of a newly standardized system.
Standardization First, Automation Second
Before introducing AI at scale, Lee’s focused on a foundational challenge that many franchise systems face: operational inconsistency. The brand unified its POS system and menu database, creating a single source of truth across locations.
This step is critical for AI-driven ordering systems, which rely on consistent menu structures to function accurately. Without it, scaling across a franchise network would introduce variability that undermines performance.
Once this infrastructure was in place, Lee’s tested Hi Auto in 30 locations, combining company-owned and franchise restaurants to ensure the system could operate reliably in real-world conditions.
A Franchise Model Built On Flexibility
Instead of mandating adoption, Lee’s is offering the AI Order Taker as an optional tool available to all franchisees. This decision preserves operator autonomy while still giving access to a system already proven in live environments.
The strategy reflects an understanding that franchisees operate under different labor conditions, market pressures, and investment timelines.
Ryan Weaver, CEO of Lee’s Famous Recipe Chicken, described the intent clearly: “Our operators are the backbone of Lee’s, and it’s our job to give them every advantage we can.”
He pointed to early results from 30 stores, highlighting improvements in labor efficiency, reduced wait times, improved employee morale, and more accurate orders.
Operational Metrics That Influence Adoption Decisions
For franchise owners, adoption decisions are increasingly driven by measurable outcomes rather than technological novelty.
Across participating locations, Hi Auto’s system has delivered more than 95% order completion and 97% accuracy in live drive-thru environments. These results are significant in high-volume settings where even small inefficiencies can cascade into longer wait times and lost revenue.
Beyond order handling, the system has produced additional operational gains: labor savings of three to eight hours per day, a 17% reduction in employee turnover, and a 1.5% increase in average ticket size.
These combined effects position the technology as both a cost-control and revenue-support tool.
Changing The Nature Of Drive-Thru Work
The introduction of AI ordering does more than improve efficiency; it changes how labor is used inside the restaurant.
By taking over order-taking responsibilities, the system allows employees to focus on food preparation and customer interaction. This shift reduces cognitive load during peak periods and helps stabilize operations when demand spikes.
Over time, this redistribution of labor may prove just as impactful as the efficiency gains themselves, particularly in a sector where staffing consistency remains a persistent challenge.
Hi Auto’s Scale Reinforces System Confidence
Hi Auto brings significant operational experience to the partnership. The company powers nearly 1,000 drive-thru locations globally and processes more than 100 million orders annually. It is also used by approximately 200 franchisees across multiple regions, giving it exposure to a wide range of operational conditions.
This scale provides a strong foundation of reliability, which is essential when deploying AI systems into high-volume customer environments like drive-thrus.
Hi Auto CEO Roy Baharav has emphasized that the company’s approach is centered on empowering operators rather than replacing them, aligning closely with Lee’s franchise-first philosophy.
A Gradual Shift Toward AI-Native Operations
Lee’s approach reflects a gradual evolution rather than a sudden transformation. By combining backend standardization with optional AI adoption, the brand is building a framework where technology can scale without disrupting franchise operations.
This allows each operator to move at their own pace while still benefiting from system-wide investment in infrastructure and innovation.
As more franchisees evaluate the system, the rollout may become a reference point for how mid-sized QSR brands can introduce AI into core workflows without compromising operational flexibility or franchise independence.
