How to Deploy Artificial Intelligence in the Industrial World

picIndustrial companies want their assets to generate more revenue without investing further in infrastructure upgrades. It makes sense because the upgrade is extremely expensive! They need to leverage existing systems to achieve this. Large assets usually come with sensors that are collecting data about basic metrics like temperature, pressure, and so on. If they have a system that can intelligent assess the conditions that affect manufacturing processes, then the operators can make decisions based on that information. How do we achieve that? How does it work in practice? 

The magical combo of AI and IIoT

Artificial intelligence and IIoT can combine together to make this a reality. This allows operations to improve with little or no manual analysis from personnel, leading to lower costs and downtime, and the ability to produce faster, as well as a slew of other benefits.

Large data sets are too time-consuming for a system to process. AI is used to find correlations and the root cause to specific events based on patterns in data. If we add in a software system that monitors the performance of assets, then AI algorithms can offer advanced analytics that deliver a clear view of business outcomes.

It’s an exciting time and a lot of companies are ready to rush right in. But if AI was that simple to implement, everybody would already be on board. Since it’s an evolving field, we need to be careful about how we deploy it. If it’s not done correctly, people might walk away with the impression that AI can’t do much or that it’s not effective. Before turning these new technologies loose, consider the following questions.

What are the areas of impact?

Not identifying a key pain point to solve is a reason many AI deployments don’t pan out in an industrial setting. You have to know what you’re trying to achieve and make sure that the whole team is fully aware of it. This will enable you to continue working on it despite encountering obstacles. Here are a few examples of critical use cases:

  • Minimize unplanned downtime
  • Reduce energy costs
  • Reduce chemical costs
  • Increase efficiency of work orders

Do we have enough data and domain expertise to support this deployment?

When it comes to data, there are three key elements that make up the backbone of an AI project: size of the dataset, quality of the data, and accessibility of that data across systems.

AI projects use historical data in order to train algorithms to predict future outcomes. More data is better! While less data poses challenges, project goals can still be met in a meaningful way. Even gaps in data can be overcome using data science techniques. It’s important to know what you have to work with.

In addition to data, we really need domain experts. There needs to be a strong collaboration between data scientists and domain experts who understand the industrial processes. This allows the deployment to work really well because domain experts will provide more context that will the AI models really powerful.

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