The AI Gap Between Restaurant Chains and Independents
AI adoption widens gap between restaurant chains and independent operators.
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The article discusses a study indicating that 78% of hospitality chains use AI for business intelligence, with plans to expand in the next two years. In contrast, independent restaurants continue to rely on manual methods for competitor pricing and performance tracking, highlighting a growing technological gap.
Why It Matters
This indicates that franchisees relying on outdated methods may struggle to remain competitive, which could affect their unit economics and overall system performance.
The gap between restaurant chains and independent operators in utilizing artificial intelligence (AI) for business intelligence is becoming increasingly pronounced, according to a 2025 study by h2c GmbH in collaboration with Cloudbeds and Apaleo. The study, which surveyed over 11,000 properties, reveals that 78 percent of hospitality chains are using some form of AI technology, with 89 percent planning to enhance their AI utilization within the next one to two years. In contrast, independent operators are lagging significantly, as many continue to manage operations manually without the benefits of advanced data analytics typically enjoyed by larger chains.
The technological disparity is largely attributed to the complexity and cost of enterprise AI, which is often not feasible for single-location restaurants. Such solutions generally require dedicated IT resources, hefty software investments, and an integrated POS-and-PMS system, resources most independent restaurants do not possess. Consequently, many are left to rely on manual processes for competitive pricing and business analysis, which can hinder their operational efficiency and decision-making.
However, no-code automation is emerging as a viable option for closing this gap. This approach allows operators with no technical background to establish effective intelligence pipelines using readily available tools. The essential architecture for this automation includes a database for holding business and client information, a web-data retrieval tool for gathering competitive insights, a language model for synthesizing data into actionable summaries, and an automated delivery mechanism for regular report generation.
