Brianna White

Staff member
Jul 30, 2019
Most industrial organizations don't need to be sold on AI. They know its benefits, its value for being competitive, and that it's critical to their existence as a business. Consider some of the findings from a 2019 Accenture study, where 84% of C-suite executives said AI was essential to achieving their growth objectives, and 75% added that failing to scale AI across their organization will lead to them going out of business in five years.
So, the will to adopt AI is there; nobody still needs to hear the sales pitch. What's needed, though, is guidance on just how and where to get started.
How do you make AI real in the industrial space? With Industrial AI — AI solutions purpose-built for industrial sector applications. The issue organizations run into with adopting and scaling AI across the enterprise is this notion that AI needs to be applied to every system and business process right away. But implementing AI isn't like flipping a switch. If your measure of success is going to be based on turning an AI-less organization into an entirely AI-powered one overnight, you're almost certainly going to fail. Instead, start smaller and evolve your roadmap incrementally — with AI embedded into specific industrial applications underpinned by an ROI-driven use case. This ensures a more gradual transition, one that's easier to scale and quicker to prove value.
These Industrial AI applications need to be guided by domain knowledge and carefully chosen for purpose-fit, tangible use cases. While needs will vary between plants and the business problems you want to resolve, here are a few choice use cases that might help you start your own Industrial AI strategy — and reap a faster time-to-ROI from it.
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