Machine Learning For Restaurants and Food Services- Examples, Challenges, and Trends
Machine Learning and AI Challenges for SMBs in Food Services
- Most Innovation is for Bigger Firms
- AI is Resource Intensive, and Complex
- Food Services Isn’t a Machine Learning Innovation Hotbed, For Now
Vendor applications, in food services and other sectors, are costly and exclusively developed for larger firms. These specialized applications, such as robotics for the food service industry or video intelligence for evaluating food quality, are very complex and call for on-staff machine learning experts with plenty of experience, lots of resources, and/or massive amounts of data (neither of which are common for small or mid-sized food services businesses).
The amount of talent, money and time needed to develop AI and robotics applications is much higher than most businesses anticipate. Like Google or Facebook, SMBs are not in a position to recruit the top AI talent right out of college and pay them handsome salaries and creative benefits. Data science talent is likely hard to come by or nonexistent, even for restaurants with more than a hundred locations. It is challenging to cultivate and develop from within a food service business when AI is not a core or mission-critical component of operations.
In contrast to industries like eCommerce, pharmaceuticals, banking, and telecommunications, among others, the food services sector receives little attention as a distinct niche for AI. Food services are not attracting the limited AI talent that does exist, and the industry is unlikely to experience the same level of innovation as many of the sectors mentioned above. This does not imply that there won’t be innovation in the food services industry, but rather that we should anticipate a slower rate of innovation and adoption.
Use Cases of Machine Learning and AI in the Restaurant Industry
- Robotics
- Street Kiosk Technology
- Chatbot / Conversational Interfaces
- Customer Reccomendation Platforms
- Analytics Solutions and AI
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