For the past decade, industrial operations have stood at the cusp of a technological revolution. Industry 4.0, powered by AI, promises to make facilities smarter, more efficient, and more resilient. Many are already on board. According to Rockwell Automation’s State of Smart Manufacturing Report, 85% of respondents either invested in or planned to invest in AI and machine learning (a subset of AI), in 2024. Still, for many leaders the decision to adopt AI raises questions about how it works, how to manage cybersecurity risks, and how to ensure the benefits outweigh the cost.
What we do for our clients is peel back the layers and provide a practical perspective on industrial AI to help them make these decisions with confidence.
When we talk about industrial AI, we’re most often referring to the technology that uses machine learning and data analysis to make sense of complex, real-world production environments. GenAI or agentic AI systems also have a place within the scope of industrial AI, but this blog will primarily focus on ML-based technologies. Most applicable are the tools that solve very specific problems: digging into streams of sensor data, control logs, and maintenance records to reveal patterns and insights that may not be spotted otherwise.
For example, an AI tool could be applied to a compressor on a chemical plant. Over time, the model would learn how that compressor behaves under different conditions by tracking vibration, temperature, and pressure. When the data starts to suggest subtle signs of wear, the system can flag a potential problem weeks before a traditional alarm would. This early warning lets maintenance teams intervene at the right moment instead of experiencing costly, unexpected downtime.
One of the most powerful uses of AI is predictive maintenance. Instead of sticking to a fixed schedule, which might result in costly over-maintenance or missing critical failures, machine learning techniques predict when a part will actually need attention. Algorithms aid in anomaly detection. By studying historical failure data alongside real-time sensor data, AI models can forecast problems.
The models behind these predictions use a mix of techniques.
Anomaly detection, for example, could rely on either supervised or unsupervised learning. A supervised model is trained on data that has been prelabeled as “normal” or “anomalous,” and the model learns what constitutes an anomaly and flags outliers in new data. An unsupervised model would be fed only unlabeled data and look for unusual patterns or outliers on its own, without having seen labeled examples.
A predictive maintenance model would be trained on historical equipment sensor data paired with failure or maintenance‐needed labels. The model learns those failure signatures and then predicts upcoming maintenance needs on new data.
Physics-informed machine learning models can inform equipment replacement by using parameters based on physical laws about material degradation to reduce “noisy” data and create more accurate forecasts.
Computer vision can assist with quality control by analyzing production data and images to catch defects earlier and adjust processes on the fly. At a food plant, for instance, AI-powered cameras can spot packaging flaws faster than manual inspectors could. Meanwhile, the system tweaks oven temperatures to ensure every batch meets the same standard.
Bringing AI into an industrial facility means connecting more devices and systems, which naturally expands potential vulnerabilities. Cybersecurity must be a core part of planning.
Protecting your AI and control systems starts with segmented network design to help keep threats contained. Instead of having one open network where all devices can talk to each other, it is a best practice to break up the network into logical segments based on function. That way, if a device were compromised in one segment, the potential spread of malicious behavior is limited.
Data encryption is another critical layer. Whether data moves between sensors and servers or resting in databases, it should be protected against interception or tampering. By adopting a zero-trust approach, users will not assume a device or user is safe without verification. Controls like multi-factor authentication will also ensure only authorized people access sensitive systems.
These principles also apply to the devices themselves. Every sensor or edge processor running AI models needs to be kept up to date with security patches. Security can also be supplemented by specialized intrusion detection systems that watch for unusual activity that could signal an attack.
AI models themselves need safeguarding too. Attackers could try to feed them misleading data or alter their behavior. Regularly auditing model outputs and maintaining strict training data controls helps keep your AI trustworthy.
Additionally, having a clear plan for incident response is essential. That means knowing how to quickly isolate problems and recover, with round-the-clock monitoring to spot threats early.
AI’s insights are only as good as the data they’re built on. Many facilities collect vast amounts of information but struggle to put it to effective use. In fact, only 44% of respondents to Rockwell Automation’s survey indicated that data collected at their organizations were used effectively. The first step is ensuring you have reliable sensors capturing the right parameters at the right intervals.
Next, these data streams must be stored in systems designed for industrial environments such as historians that record time-series data accurately and securely. Alongside this, it is crucial to establish strong data governance. Clear guidelines about who owns data, how it’s maintained, and the quality standards will help keep everything on track.
Cleaning the data is typically another required process. Automated routines that spot and fix errors, fill gaps, or adjust for sensor drift make sure the AI is learning from trustworthy information. Adding context helps too. Maintenance logs, environmental factors, and operational notes enrich the data, allowing AI to understand not just what is happening but why.
No technology decision is free of cost. AI implementations require investment in development, hardware, data infrastructure, and skilled personnel. Development covers everything from designing algorithms to validating models. Infrastructure involves servers, edge devices, and network upgrades. Properly utilizing these tools can require data scientists, IT and OT experts, and cybersecurity professionals. Maintenance isn’t a one-time expense either, as models need retraining, software must be updated, and security measures must be maintained.
But when you look at the bigger picture, adoption can deliver returns that justify the upfront effort. For example, if initial development costs $200,000 and annual maintenance and server costs are $50,000, the annual investment over 4 years averages to $100,000. While it is true that AI would often be managed by a data scientist, new tools can make feasible what was previously only capable through the work of multiple staff data scientists. In this way, AI can prove to be a more cost-effective solution in the long run. You reduce downtime and maintenance costs, improve product quality, make your workforce more efficient, and avoid expensive cybersecurity incidents.
Over 70% of respondents surveyed by Reuters indicated that they either currently use industrial AI for predictive maintenance or will within the next 3 years. But before jumping in, take a clear look at your current systems and capabilities. Understanding your data maturity and security posture sets a realistic starting point.
Among digital priorities, AI and ML are considered part of the advanced toolset. Without a cybersecure networking and communication infrastructure or a reporting system that has access to operational data, these more advanced tools cannot be implemented in a cost-effective way. As transformative as intelligent asset management or digital twins can be, they are incremental improvements for digitally mature facilities.
For the most worthwhile investment, this is what we suggest:
Navigating AI’s complexity and risks is easier with experienced partners. Enterprise Automation provides end-to-end support, from readiness assessments through deployment and staff training. Our approach focuses on practical, secure, and user-centered AI adoption, helping your facility unlock potential without unnecessary risk.