We’ve seen the headlines about Chipotle, White Castle, and other restaurant chains embracing a “digital-first” strategy and going all in with artificial intelligence. These are great headlines, but are they more than just empty buzz?
The restaurant industry as a whole is a perennial laggard in new tech adoption in general and artificial Intelligence in particular. The reasons for this shouldn’t be a mystery. Restaurants are 90 percent-plus independents, labor-intensive, built around human processes that are less than replicable, and have slim (if any) profit margins. These industry qualities create a palate that is muted for internal propensity to embrace tech, while posing challenges for external technology developers and partners.
The standard adoption curve demonstrates the journey toward market saturation (near complete adoption). While AI may be in the early majority stage, meaning 16–50 percent of industry are leaning heavily on AI to advance their capabilities, build efficiencies, and drive growth, the food and restaurant industries are far behind. For food and beverage industry vets, this is not news, the restaurant industry is historically late to the tech party, preferring to sit on the sideline until demonstrable benefits to top and/or bottom line are vetted by others with deeper pockets (and margins).
Why and where do we lag?
According to McKinsey, use of AI for at least one business function has increased from 33 to 65 percent in the last year, while retail markets have less than 8 percent of respondents using AI regularly for work. Of those using Gen AI regularly at work, the bulk of the use-case if for marketing and sales purposes.
Depending on the source, there are three, eight, and up to 13 ways restaurants can use AI. From facial recognition to automated order processing to predictive analysis, the “advantages” are well stated, but not well vetted. Of those using AI regularly at work, the bulk of the use-case is for marketing and sales purposes. For independent restaurants, specifically startups, with a minimal marketing budget and inconsistent cash flows, even investing in AI for marketing may be a stretch.
Outside of capital considerations, why don’t restaurants use more tech and AI?
While Nvidia and others continue to produce more chips, global firms like Amazon, Google, and Meta are buying the bulk of them. Follow the money and you’ll find gaming, entertainment, and social media, but you won’t find restaurant AI applications. Facial recognition in restaurants is a fun hypothetical, but who would spend money to develop these technologies given the increased cost associated with the demand spike? While chains may have some internal capabilities, startups are the likely source of these restaurant-centric applications.
Attracting entrepreneurs to this AI-restaurant space can be a challenge. While there isn’t a litmus test for what market to enter with which product, a system designed by David Saunders from University of London is a good tool to point to the gaps (Mellor, 2009). The “Ten-Dimension Rating Scale” allows future founders to evaluate tech-based ideas prior to launch. Each dimension, listed below, is rated on a 5-point scale.
Restaurant tech, from a founder and investor point of view, may struggle to earn acceptable scores in many categories.
Readiness: While a score of 5 (tech is bug-free, well proven) is ideal, the scores are likely to trend towards a 0 (works in a lab, not field tested).
Anticipated profit margin: To score a 5, investors are looking for 70 percent-plus margins. Realistic profit for a beta or rev. 1 product in this space is likely below the 0 score (15 percent) and may even be negative (paid placement).
Access to market: A high score here indicates a centralized customer list and established distributorships. Independents lack a central hub with no mainstream tech distributor. A score of 0 is likely (substantial barrier to reach customers).
Customer conservatism: Full points are awarded for customer groups that are open to innovation and are, by nature, experimental. Realistic scores here are toward a 1 (“prefers tried-and-trusted methods while resisting new ones for years).
Experience of Team: A score of 5 here would indicate the founders have not only led tech firms, but also have significant experience in the food and bev space. Given most independent restaurant owners don’t exist in tech heavy environments (Silicon Valley), the score here will trend toward a 1 or 0 (“have a network or regular contacts in industry through joint research projects”). We tend to see restaurateurs sit at the board level, but this is far removed from the day-to-day grind of product development, launch, and customer success.
These 5 categories can tank potential investments and exploration into the restaurant tech space, making it unlikely that external AI startups will dig restaurateurs out of the laggard category. The most recent Forbes AI 50 startup list seems to confirm this assumption. While healthcare, defense, and video gaming get reinvestments, retail and hospitality are again left in the dark.
Sit and wait?
While the big guys play around with internally born or collaborative ventures into AI, what can the other 90 percent of non-chain restaurants do? Explore on your own with gen AI; Have the management team get together and spitball ideas around how to use (largely free) tools from Meta AI, ChatGPT, Gemini, and other LLMs that are available on your phones or PCs. Potential LLM applications to roundtable include:
- Analyze your inventories, turn, and waste.
- Dive into menu and ingredient profitability.
- Explore employee absentee and turnover rates.
- Develop customer appreciation and retention programs.
- Check your marketing materials for cohesive voice.
If you need ideas, just ask the model. But, by starting small and developing your tech-adoption muscles, you can change the laggard status on the curve.
Dr. Nathan Libbey is an Instructional Associate at Northwestern University’s Kellogg School of Management. He brings extensive industry experience to his roles in academia and technology, having held leadership positions at Global 50 and Fortune 500 firms including Nestle and Cintas. Nathan has also served as VP, Advisor, and Director for several tech startups, ranging from AI to robotics. He has a Doctorate in Law and a Master of Science from Northeastern University, Boston.
