Companies we work with often talk about “failing fast” – learning what doesn’t work and moving on without delay. Yet in the same breath, there’s still the real fear of simply not delivering what was promised or wasting valuable resources. While artificial intelligence has been touted as a promising new technology for a number of business applications, it’s also very likely that you are hesitant to make a significant investment in the technology due to the fear of those very risks for your company.
The promises of artificial intelligence continue to receive a healthy spotlight across numerous industries including our manufacturing communities. While AI comes in many forms, keep in mind that, at its core, AI is a host of technologies and algorithmic approaches that makes it possible for machines to learn from new data, adjust to new inputs and perform human-like tasks. Here are a few examples of things that AI is doing:
Predictive adjustments of blast furnace temperature settings so teams can free themselves from manual monitoring and adjustments
Natural language processing to absorb and spot patterns in multilingual customer logs that may signal upstream problems on manufacturing lines
Computer vision to spot missing, minute components that line operators often can’t spot
What do these AI use cases have in common? And further, how should manufacturers define AI? From an outsider’s view, AI allows computers to learn from new information and perform tasks requiring human-like intelligence. From an insider’s view, AI entails deploying a model that learns. You prime the model with known data and the model makes predictions and recommendations using new data and feedback loops allow the model to learn new patterns. AI is less about optimized models and more about optimizing the feedback loop for optimized outcomes.
From a manufacturer’s perspective, the first implication is that AI does not constitute a traditional waterfall IT project. To find success with AI, manufacturing leaders must rethink their approach for deploying AI onto the manufacturing floor.
Supporting successful AI strategies within your manufacturing community will rely heavily on the disciplines that lead up to a model’s deployment. Drawing on practical and recurring field observations from our SAS Institute colleagues who have worked with our customers, we offer seven “do’s and don’ts” to consider as you evaluate your path forward to deploying AI technology… To continue reading, CLICK HERE.