The tech industry strives continuously to advance AI innovation for manufacturers – but the problem isn’t easy, and the stakes are high. Quality, efficiency and even safety are at stake, and if we’re going to use AI solutions in manufacturing with confidence, we need to find the right mix of tools and understand what’s needed to make them trustable.
Done right, Reliable AI is all about running the perfect mix of existing AI models to glean the trustworthy insights we need to unlock powerful strategies. For the future of modern manufacturing and production health – this mix is industrial AI, causal AI and generative AI (genAI).
Industrial AI: The Bread and Butter
Purpose-built industrial AI has been a key player in the manufacturing industry for its proven ability to precisely predict machine failures – which has helped many manufacturers around the world meet key business goals and keep their factories running smoothly. This particular form of AI is modeled with neural networks.
The data sets have to be big, accurate and well tagged. Machines don’t fail every day, so it takes a ton of machine run time – and a variety of machines – to get enough data to predict with confidence. The good news is these tools are in place today to give manufacturers more and more use cases and capabilities to help run a smarter factory.
While industrial AI may be well established in manufacturing environments at this point, it’s not done pushing the boundaries of innovation – cutting-edge industrial AI models will continue to be the backbone of manufacturers’ AI strategies. Most importantly, this kind of AI has driven measurable results and, therefore, is the reason manufacturers feel comfortable exploring new kinds of AI in the first place.
GenAI: A Tool, Not an Outcome
It’s no secret that genAI has a history of hallucinating. However, when used correctly, it’s still an effective tool for the manufacturing industry. Its accuracy is solely based on the control of the model – so the humans in charge of developing these models have endless opportunities to make this technology not only useful, but reliable.
Understanding how to make genAI reliable means understanding how it works – and data is key here as well. As the name suggests, GenAI’s large language models work off of large amounts of data. They then learn the patterns and try to predict what’s next. Yet if the GenAI model isn’t trained on data rooted in a manufacturer’s real-world environment — including inputs tied to machine health, like maintenance logs and repair procedures, as well as process health indicators such as conveyor speeds and heating temperatures — it risks generating inaccurate insights that compromise production reliability and erode trust.
Therefore, in order to run an impactful genAI model, you must ensure it is trained with large troves of quality, domain-specific data. Ideally, you want some traceability to see what steps it took and what sources it used to reach its conclusions. The newer models offer more of those capabilities, so you can not only gain confidence in the result but have an audit trail to help understand what happened and why, if you ever need it.
While it is improving by leaps and bounds, genAI’s potential will not be realized overnight – all good things – and technologies – take time. Safety is a top priority in the industrial sector – and given the fact that genAI can still hallucinate, deploying accurate models will take more time in manufacturing than in other industries.
Proven experience and access to quality data from your provider will be crucial. However, when AI agents do arrive for manufacturers –they’ll have the opportunity to truly understand the health of their machines with the ability to dig deeper into any issues.
Causal AI: The Key to Cause-and-Effect
Causal AI models are explainable, transparent, and safe – offering a good option for manufacturers when looking into the factory floor’s future. By leveraging data from factors like maintenance, production, operations and quality, Causal AI is able to examine cause-and-effect relationships between what the system produces and what we see on the machine.
This allows us to find true causality – as opposed to with genAI, we’re able to see the correlations. We must be able to understand how things impact each other and what changes need to be made.
The relationship between Causal AI and genAI will be critical for manufacturers – with causal AI, they can see correlations they may have missed and have never seen before. Coupling that with genAI will help explain these complexities that are machine faults. Of course, manufacturers can’t overlook industrial AI —which establishes a foundation for highly accurate and reliable AI insights.
Together, this AI trio combines a robust, explainable and reliable AI engine that will propel reliability professionals into a smarter future.