Do the latest AI models actually solve challenges or industry pain points better than proven systems already do?
The headlines might be focused on generative AI, but what does Artificial Intelligence (AI) really mean to food processors? Most food processors today are already using AI-enabled systems such as vision inspection, yield optimization, or process monitoring in their processing lines — it’s been improving efficiency, increasing yields, and helping meet compliance for decades. The question is whether the latest AI models will actually solve challenges or industry pain points better than proven systems already do.
AI in food processing isn’t just about the recent buzz; it represents decades of continuous evolution. Let’s explore what AI is beyond the recent hype of generative AI, so you can make investments and decisions based on what delivers results, rather than the newest trend.
The computational search for AI has been happening since before the 1950s. While there is a difference today with advances in computing power and programming knowledge, it is important to remember that not all AI is the same, for example, vision-grading vs generative AI. Like any industry, the terminology can be general, confusing, and often varies depending on who you talk to too.
Want to check the terminology before you dive in? Click to go straight to the glossary.
Software and AI represent different approaches to solving problems with computers. While AI is currently the headlining act, it is, technically, a subset of software. Software and AI have shaped and supported the efficiency and quality of food processing since the 1990s.
Software is explicitly programmed to follow rules and logic pathways. In traditional software programming, the behavior is predictable; the same input always produces the same output. For example, in a calculator 1 + 1 = 2, every time.
Most modern AI is trained on datasets to recognize patterns; it then uses those patterns to handle new, previously unseen situations. For example, recognizing product defects, or predicting equipment failure.
In food processing, these AI approaches are often applied to three key operational domains:
Yes, there is a difference between ‘software systems and AI models’ and ‘software and AI,’ welcome to the confusion of terminology overlap.
Here is where the distinction is crucial especially in food processing, as it gets to the heart of how AI technology is structured, deployed, and accessed by users.
An AI model is essentially a mathematical representation of learned data. If we look at the recent Chat GPT (a version of a large language model (LLM)) it is a generative AI model, a collection of algorithms, and neural architectures that have learned patterns from training data.
While a software system is the application or platform that users actually interact with. It includes the AI model but encompasses the user interface, data processing, security systems, databases, servers, networking components, and traditional software engineering that makes the model accessible and useful.
The latest computational power software systems and AI models could result in robots that adapt to variations in products, communicate with each other, and learn new tasks through demonstration.
For example, a vision system AI model learns to recognize defects and grades of quality; the software system enables operators to see and interact with the visions system on a dashboard.
In food processing this layered approach has several advantages:
And is closely integrated with physical hardware and control systems:
A timeline
The main uses of software systems and AI models in food processing have been in process optimization, quality control, predictive maintenance, and robotics. Below we unpack the timeline of development within each domain.
Process optimization: 1980s to the Future
For most plants, process optimization AI doesn’t mean replacing operators — it means tuning existing set-points to maintain yield and reduce downtime when ingredients or environmental conditions change.
In practice, this helps processors minimize waste, manage variability in raw material, and ensure compliance with product specifications in real time.
| Period | Key development | Details/examples |
|---|---|---|
| 1960s | Digital foundations - SCADA systems: process monitoring | First digital monitoring systems transmit small data sets (e.g., temperature, pressure, valve status) from remote sites to central monitors. Transmission took 5 - 10 seconds per report. |
| 1969 | Programmable Logic Controllers (PLCs) | Early PLCs (1 KB memory, ~ ~ $20,000 in 1969) make things more sophisticated. |
| 1980s - 1990s | Expert systems & rule-based AI | Captured human knowledge with 'if-then' logic decision-making in specific processes. Predictable, auditable, non-learning systems. 👉 Practical impact for processors: Expert systems allowed plants to codify expert know-how for tasks like recipe adjustments, temperature control, or batch sequencing - improving consistency without needing full automation. |
| Governance & validation | Safety systems & compliance | Rule-based, expert systems remain the backbone of safety in today's modern food processing. For example: HACCP compliance systems that automatically shut down production if temperature falls below minimums to ensure food safety compliance. 👉 Just like any control system, AI tools require documentation performance testing and audit trails to meet HACCP, GFSI, and ISO 22000 expectations. |
| 2000s - 2020s | Machine learning for process optimization | Algorithms began handling multiple variables at once to manage complex non-linear relationships between different processing factors. Enabled by the internet of things (IoT) and real-time control systems. This allowed AI to monitor and adjust processes for more efficient and consistent production. 👉 For example, machine learning algorithms analyze camera and sensor data to optimize cutting, reduce waste, and improve yield. |
| Future - possibility | Predictive optimization | Software and AI models could have predictive ability that considers supply chain factors, weather, and demand forecasting. This level of process optimization could help to meet growing food demands while tackling challenges of yield optimization and environmental unpredictability. |
Deep-learning inspection systems reduce manual sorting and rework, particularly in visual tasks like surface defects, color consistency, or fill levels. In food production, they perform tasks like grading, defect detection, and product classification at production-line speeds.
| Period | Key development | Details/examples |
|---|---|---|
| 1990s | Vision systems introduced | Cameras integrated with industrial software systems, conveyor belt controls, and rejection mechanisms. Image analysis algorithms (non-AI) could identify defects based on pixel patterns, color variations, or size. |
| 2010s | Deep learning for real-time inspection | Higher resolution imaging, faster computer processors, and integration with SCADA enabled real-time quality control response. Convolutional Neural Network (CNN) deep learning AI models began automatically learning visual features from product images, identifying subtle defects more consistently than human inspectors. By the late 2010s, these systems were being adopted into industrial food processing lines for automated inspection and sorting. |
| 2020s | Refined deep learning with multi-sensor input | Improved accuracy of established CNN-based detection systems. Software systems supporting AI vision models can combine image analysis of visual data with other sensor inputs such as temperature, moisture and pressure, allowing for more precise and reliable quality control decisions in real-time. |
| Point of interest | Vision transformer models (ViT) gaining interest | Most AI systems for quality control in food processing still rely on CNN-based deep learning, even as newer vision-based generative models (ViTs) introduced in 2020, are gaining attention in the mid-2020s. Many industrial solutions focus on optimizing these CNN models for real-time performance, using AI-driven machine perception to interpret sensor data and guide automated actions in the production line. |
| Trade-offs | Deep learning vs machine learning | Deep learning offers high accuracy on complex visual defects, but requires large amounts of labeled training data, and it needs powerful, specialized computing. All of this means it is more expensive to implement and maintain. Machine learning remains cost-effective for simpler quality control tasks. |
Multiple JBT Marel solutions use CNN-based deep learning to inspect fish fillets, detecting bone fragments, discoloration, and size variations that would be impossible for human inspectors to catch consistently at production speed.
Predictive AI helps maintenance teams move from calendar-based to condition-based servicing — extending equipment life while avoiding unplanned stoppages.
Machine learning models detect patterns that precede failures, triggering maintenance only when required. This reduces downtime, prevents over-servicing, and supports better resource allocation across the facility.
| Period | Key development | Details/examples |
|---|---|---|
| 1990s - 2000s | Early condition monitoring | Basic statistical AI models analyzed vibration and temperature data for alarm-based maintenance – mostly reactive. |
| 2010s | Proactive maintenance with AI | Computerized maintenance management systems (CMMS) with mobile interfaces combine with AI models that analyze multiple sensor streams to predict equipment failures and allow technicians to schedule proactive maintenance. |
| Future | System-level predictive optimization | AI could model interdependencies between machines to optimize maintenance schedules and keep lines operating. |
By combining machine vision and motion control, robots in food processing can adapt to product variations, handle delicate items, and optimize cutting or portioning tasks.
| Period | Key development | Details/examples |
|---|---|---|
| 2000s – 2010s | Basic robotic control systems | Robots perform repetitive tasks (picking, placing, simple cutting) guided by simple AI and production scheduling systems. |
| Future – theoretical application | Adaptive, collaborative robotics | The latest computational power software systems and AI models could result in robots that adapt to variations in products, communicate with each other, and learn new tasks through demonstration. |
AI is an established part of modern food processing, though its real-world use looks quite different from the futuristic expectations often portrayed.
While emerging generative AI models are drawing attention, the backbone of AI models and software systems in food processing remains reliant on narrow, purpose-built systems designed to deliver reliable outcomes in harsh, time-critical environments. These proven, well-tested technologies and models prioritize consistency, interpretability, and integration with legacy systems — all essential in regulated production settings.
AI implementations in the 2020s predominantly use CNN architectures developed in the mid-2010s, with some research exploring newer transformer-based systems — the foundational architecture of today’s Generative AI models introduced in 2017 — for specific applications.
Generative AI (such as an LLM) in food processing is currently limited to administrative tasks: documentation generation, recipe optimization in R&D environments, and regulatory text analysis. There are also experiments using Generative AI-driven systems – typically LLM integrated helpdesk systems, not core AI models in food processing - to handle service requests for faster, more effective escalation.
Overall, most AI used in food processing today is deep learning AI models — especially CNNs — that are 5 – 10 years old. Their reliability, proven performance, and ease of validation for compliance make them particularly well suited to meet the strict standards of the food processing industry.
There are a number of practical reasons for this lag of uptake in the industry:
👉 How AI is implemented: AI model deployment typically follows five steps: data collection, model training, system integration, validation and continuous improvement. For food processors, this means collaboration between operations, IT, and QA to ensure the system performs reliably before going live.
A question gaining momentum around the latest Generative AI models is the large quantities of water (for cooling) and energy needed to operate data centers to support them. As part of the Water, Food and Energy (WEF) Nexus, food processors need to consider the trade-off between production efficiency and sustainability. Does the marginal product quality improvement justify the extra energy, water, and operational costs?
Often optimizing existing CNN systems or traditional machine learning models can deliver better ROIs.
As an example, training a mid-sized, large deep learning model can consume more electricity than five US homes use in a year, while providing processors with minimal product quality improvement.
Considering all the general and misleading information on AI, how do processors in the meat, fish and poultry industries decide what is right to meet business needs today and in the future?
| Your need | Appropriate technology | Why |
|---|---|---|
| Safety compliance, emergency shutdowns | Expert systems (1980s technology) | Deterministic, auditable, regulatory approved |
| Simple quality classification - size, color thresholds | Traditional machine learning | Lower cost, easier to maintain, sufficient accuracy |
| Complex defect detection, variable products | CNN-based deep learning | Proven technology, handles variation well |
| Documentation, recipe optimization, regulatory text | Generative AI - PILOT CAREFULLY | Administrative applications ONLY |
| Real-time process optimization | Traditional machine learning + Expert systems | Faster, more reliable than deep learning |
By understanding the timeline of AI models in food processing, processors have valuable tools to separate the hype from reality. For example, be cautious of any claim that an AI model ‘explains itself.’
When you’re ready to invest in AI-based technology look for a vendor that:
This is where we come back to the start of the article; the AI buzz compared to the AI reality. At this moment in time, global investments in AI are extreme, as are the predictions of AI possibilities. But underneath the noise and excitement of the new, there are question marks being raised by experts working with this technology, including the plausibility of predictions of artificial general intelligence (AGI), the environmental cost, and the source and reliability of the data needed to train the systems.
There has been a long, broad evolution of AI technologies within food processing technology. Since the 1990s multiple AI models and software systems advances have offered better:
Integrating the latest AI models into food processing solutions is still very much in its infancy, and we may see it provides very minimal benefits to yield but a large increase to energy and water costs, when compared to current, long-standing AI integrations.
Despite the hype, generative AI has not replaced or improved upon proven deep learning systems for the production environment of line quality control, process optimization, or predictive maintenance. The computational costs and ‘black box’ nature make it unsuitable for safety-critical applications.
JBT Marel is exploring the opportunities of new AI developments, while building on the proven software systems and AI models that drive our solutions and provide reliable, scalable and accurate results for food processors.
If you’re interested in the technical classifications and terminology used throughout this article, here’s a deeper dive into how AI categories are structured.
AI is a general term encapsulating any computing system that aims to be at the level of human intelligence: able to think, solve problems, understand, learn, and apply knowledge across a wide range of topics and domains. The ‘holy grail’ of AI is known as Artificial General Intelligence (AGI), or strong AI – when a machine possesses human–level cognitive abilities. While the latest iteration, generative AI, feels close, a closer look shows we’re a long way from AGI.
Modern AI in the mid-2020s falls within the category of narrow AI – narrow AI systems are designed to perform a specific or narrow range of tasks. For example, a self-drive car uses narrow AI, it can drive a car and recognize objects to avoid, but it cannot balance your accounts. Narrow AI is our current AI reality.
Under the narrow AI umbrella, there is a hierarchical relationship with two branches relevant to food processing:
Artificial Intelligence – the overarching field
Narrow AI – current reality of AI: excels in one area but cannot transfer knowledge to other domains
Knowledge based AI (rule-based)
Expert systems - unlike machine learning, deep learning and generative AI, which learn patterns from training data and make predictions on learned patterns, Expert systems AI are rule-based. They are designed to capture the knowledge of human experts for specific food processing tasks and use logical inference to apply rules to new situations. They don't learn from data; they apply pre-programmed knowledge.
Data-driven AI (Learning-based):
Machine learning – subset of narrow AI: trained algorithms that learn from patterns. Traditionally, for simple classification tasks. In food processing this looks like a Quality Control system that classifies products as good or bad base on direct measurements (size, color values, weight)
Deep learning - subset of machine learning: trained algorithms that work like neural networks with multi-layered pattern recognition to make classifications based on complex combinations. In food processing, this could be a vision system that learns the patterns of a ‘normal’ product appearance so thoroughly it can detect any anomaly including those it has never seen before.
Generative – application category: trained systems that create content (text, images, audio, code). It can use both deep learning and traditional machine learning approaches. In food processing, this is currently being explored for use in optimizing recipes and generating documentation.
While these narrow AI systems feel quite intuitive and intelligent when we use them, they are not employing reasoning, the algorithms behind each model are making choices and predictions based on the patterns it has been trained on, to perform a specialized function.
And that’s where food processing is today, using proven narrow AI systems that deliver practical, measurable value to solve industry pain points, while exploring the possibilities of the latest developments to continue improving food processing operations and results.