During the first half of 2021, we hosted a series of webinars and event presentations (which can be viewed on-demand here) to help industrials learn more about AI, how to get started, and the real-world results that companies are achieving today. These interactive sessions opened the floor to questions from attendees. Below are the five most common questions that we have been asked in 2021 about AI for the industrial sector.
Before you get started with AI, it is crucial to assess whether or not you need AI. The end goal needs to be clearly identified before you even get started. Then, depending on the problem you’re trying to solve, you can choose to go with AI or a traditional advanced process control algorithm. Once the use cases are clear, you need to see whether you have the right quality and quantity of data that can be used for the project. Once that’s in place, along with clearly charted business value that can be created, you can start executing AI within the organization. Check out this blog to learn more about how to determine if AI is right for your operational challenges.
The most significant change required within the enterprise for a successful AI implementation is ensuring deep collaboration between operations staff and the data teams. Typically, the two teams work in isolation, and unless there’s a deep collaboration between the two sides, AI will lack the process insights to derive the value that both sides are looking to achieve. This blog goes into more detail about why organizational culture is critical to AI success.
When you think about AI in industrial setups, the most common application that comes to mind is predictive maintenance. However, AI can be used in several other areas within industrial operations, including quality checks, energy optimization, cycle optimization and process optimization to ensure quality control and less defects.
You can download our e-book, Top 10 AI Use Cases for Industrial Operations, to gain greater insight into how AI can help to transform your business.
According to Gartner, 85% of ML projects fail. Worse yet, the research company predicts that this trend will continue through 2022. The three key reasons that lead to the high failure rates include:
However, all is not lost, and industrial companies are bucking the trend. Check out this industry paper where a food manufacturer has implemented AI to optimize its boilers which has lead the company to cut annual energy consumption by 4% and reduce carbon emissions by more than 9 million pounds per year.
The Canvass AI platform puts the power of industrial AI in the hands of engineers by providing an easy-to-use platform to apply, explain, and scale AI across their operations - without requiring coding expertise or relying on third party consultants. It is purpose built to address industrial operational challenges, such as reducing carbon emissions, lowering costs, and energy optimization.
Canvass recognizes that industrial engineers have unparalleled knowledge about their operational problems within their organizations and by bringing AI within their reach empowers the entire workforce to augment their expertise with data-driven insights. Within a single interface, Canvass AI Platform empowers industrial engineers to:
Canvass AI recently announced the latest release of Canvass AI v4.1. Check out this FAQ to learn about what’s new in the platform.
Ready to find out how Canvass AI can help you take your business to the next level? Get in touch with us to learn more.
