Successful artificial intelligence (AI) implementation depends on establishing a strong foundation of data quality, process understanding and cybersecurity. All of these things are essential to successful kitchen operation, but how many of your operations collect that data, and how many have it in a form that can be interrogated.
- AI in kitchen settings is a challenge that requires trustworthy data and structured processes, not just software purchase decisions.
- Start AI projects by defining the decision to be improved, then identify relevant data sources and ensure data quality and contextualization before modeling.
- Establish a secure OT-to-analytics architecture with proper data pathways, network segmentation and cybersecurity measures to protect plant operations.
AI is becoming one of the most discussed topics in catering operations, but for many facilities, the practical question is not, “What AI tool should we buy?” The better question is, “Is our facility prepared to use AI in a way that is safe, useful, and provides a positive return on our investment?”
For management, maintenance, and operations personnel, AI should not be viewed as a replacement for process knowledge. It should be viewed as a tool that can help facilities make better decisions using the data they already generate. A modern kitchen produces large amounts of information through PLCs, distributed control systems, lab systems, maintenance systems, production accounting tools, and ERP platforms. The challenge is that this data is often fragmented, inconsistently named, poorly contextualized, difficult to trust or not even gathered. Before catering facilities pursue AI initiatives, they need to establish the fundamentals: trustworthy instrumentation, structured data, secure architecture, clear ownership, and disciplined validation.
Software matters, but it’s only one decision
Many organizations approach AI as though it is primarily a software acquisition. They evaluate vendors, compare features, and look for platforms that promise predictive maintenance, optimization, anomaly detection, or automated recommendations. While software matters, it is rarely the limiting factor in early foodservice AI projects.
An AI-ready facility has more than a history and a collection of process tags. It has a coherent data environment that connects equipment, process areas, production events, lab values, maintenance activities, and business outcomes. It has a controls and network architecture that allows operational data to be moved securely to analytics environments. It has catering staff who understand the process well enough to challenge model outputs. It has cooks who trust the system because they were involved in its development, and it has management systems that define how models are approved, monitored, changed, and retired.
AI readiness requires several layers to work together, are you ready? An AI-ready facility depends on multiple layers working together.
Field instrumentation and analyzers
Flow, pressure, temperature, pH, conductivity, Brix, moisture, vibration, motor current, lab measurements, and other source signal
Control systems
PLC, DCS, SCADA, HMI, batch systems, alarm systems, and control loop data
History and contextualization
Time-series data, asset hierarchy, tag metadata, event context, and batch or lot alignment
Operations systems
MES, LIMS, CMMS/EAM, ERP, production accounting, downtime tracking, and quality systems
Analytics platform
Industrial data platform, data warehouse, BI tools, and governed data access
AI/ML layer
Soft sensors, forecasting, anomaly detection, optimization, decision support, and, eventually, advanced control
Governance and security
Access control, model approval, cybersecurity review, validation, auditability, versioning, and lifecycle management.
These are the range of sensors, controls, and data required. All your equipment will have some of the sensors and controls but you may not be aware or if you are aware not utilizing them.
One of the most common mistakes in industrial AI projects is starting with the model instead of the decision. Teams may begin by asking whether they should use machine learning, neural networks, generative AI, anomaly detection, or optimization algorithms.
The first question should be: What decisions are we trying to improve?
Once those decisions are defined, the next questions become clearer. Who will use the output? How often is the decision made? What data is available before the decision must be made? What is the economic value of making the decision better? What is the risk if the recommendation is wrong? Should the model advise, alert, predict, optimize, or control?
Establishing operator trust – and strong data quality
For projects early on in a foodservice facility’s AI journey, most operators should focus on advisory or diagnostic AI rather than autonomous control. A model that predicts an abnormal condition, highlights a likely cause, or recommends an engineering review is much easier to validate and govern than a model that automatically changes setpoints. Closed-loop, AI-assisted control may eventually be appropriate in certain cases, but only after a facility has established strong data quality, cybersecurity, model validation, change management, and operator trust.
AI has potential in catering facilities, but the path to value is not magical. Facilities need reliable instruments, clean data, process context, secure architecture, operational buy-in, and disciplined validation.
The most successful AI initiatives will not begin with the most advanced algorithm. They will begin with a clear operational problem, a measurable business case, and a practical understanding of the data required to support better decisions.
Foodservice operators need the skills of process controls engineers; this creates an important opportunity. Controls personnel understand the equipment, the process, the instrumentation, the automation systems, and the consequences of poor decisions. That knowledge is essential. AI may provide new analytical capabilities, but process expertise is what makes those capabilities useful, safe, and valuable.
Few catering operations have the capacity or are of a size to embark on an AI strategy and those that are often run batches guided by a spreadsheet or a similar tool. And even where a large continuous batches are produced there is little or even no data collected on the process.
It may be time to rethink whether it is the right time to embark on an AI journey and whether there is an easier way of finding out if there is a better business case or an effective maintenance program in a foodservice business. And for those that are thinking AI is the way to improve the foodservice business, the same applies, follow the rules otherwise it will just be an effective change management.
Tim Smallwood FFCSI
