Weak store visibility squeezes QSR margins

Limited oversight and costly audits reduce efficiency and drive profit leakage

Quick-service restaurant (QSR) operators are turning to artificial intelligence to manage rising labour costs and weak store-level visibility, as heavy reliance on part-time staff strains margins. Tony Do, founder and CEO of Palexy, said in an interview at the QSR Media Asia Conference & Awards 2026 last March 2026 that operators are increasingly using automation to improve forecasting, monitor compliance, and reduce manual oversight.

Labour remains the most pressing issue, with many QSRs operating with “70–80% part-time staff,” making scheduling and cost control difficult. Without accurate planning, overstaffing or understaffing directly impacts profitability.

To address this, operators are replacing manual forecasting with data-driven systems. In the past, managers “relied on experience for forecasting,” but “right now, we rely on AI,” using inputs such as weather, promotions, holidays, and store-level patterns to predict demand. This allows businesses to align staffing more closely with actual traffic and improve “transaction per labour hour,” a measure of how much revenue each worker generates.

At the same time, limited visibility across stores is affecting execution. Do said “one of the missing pieces is the visibility of operations,” with traditional auditing models proving costly and inconsistent. “Auditors cannot visit the store all the time. It's expensive,” he added.

Palexy’s system addresses this by analysing existing CCTV feeds to track operations such as service speed, staff activity, and compliance with procedures. Rather than installing new hardware, the company builds “a brain for the camera to understand what's happening in the store environment,” enabling continuous monitoring.

Automation is also replacing manual review processes. Operators can reduce reliance on staff reporting, as “instead of hiring auditors, they can automate that,” with AI systems reviewing store activity and flagging issues in real time.

Adoption, however, comes with challenges, including system integration and data management across large store networks. Smaller operators may also face cost constraints in deploying these tools.

The shift reflects growing cost pressure across the sector. “In order to control labour costs, you need technology. You need AI,” Do said.

As competition tightens, operators that combine demand forecasting with real-time monitoring are better positioned to control costs and maintain service standards, whilst those that lag risk higher expenses, weaker execution, and lost sales.