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"Why Generic AI Fails in the Chemicals Sector"

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The artificial intelligence (AI) market is growing at lightning speed. However, AI platforms come in different shapes and sizes that may not suit the industrial environment or the right power users. For example, some AI platforms are built for cross-vertical purposes, such as health, finance, logistics and retail. While these platforms may provide a horizontal offering, industrial companies need the depth of industrial-specific expertise that can manage the volume, variety and velocity of industrial data, and the variability that is typical in industrial environments.

Not all AI platforms can stand up to the complexities of the chemicals sector. So, when it comes to empowering the chemicals industry, generic AI platforms will simply fail. What’s more, some AI platforms are built for data scientists. However, the demand for data scientists globally far outstrips supply, and not all industrial companies may want to invest in teams of data scientists to fuel their AI strategy. By taking this approach, industrials also risk leaving the subject matter experts on the sidelines during the digital transformation of their operations. Instead, chemical companies should consider an Industrial AI platform that is built to support industrial operational environments and empowers industrial operators.

In this webinar, learn about the key capabilities that chemical companies need when they implement an Industrial AI platform.  Join Forogh Askari, Industry Lead at Canvass AI and Lloyd Colegrove, Dow retired, as they discuss the key challenges facing the chemicals sector, how Industrial AI can solve them, and how chemical engineers can successfully adopt and scale it across their operations.

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