Schneider Electric: Top 3 challenges of adopting AI

από | 20/08/2018 | ESG/Sustainability

AI – From big bang to business outcomes:

PAVING THE WAY FOR ARTIFICIAL INTELLIGENCE’S REAL VALUE

Sensors are everywhere

In 2012, 4.2 billion sensors were shipped for industrial use; in 2014, that figure skyrocketed to 23.6 billion.1 The Internet of Things has changed the industrial landscape, promising improved efficiency and production for everyone from shoe makers to milk processors to refineries to power plants.

It’s clear that companies do not need more data, however. In fact, “70% of captured production data goes unused.”2 Nor do companies want more data. Instead, they want to extract the trapped value that already exists by leveraging collected data to solve specific problems in much more proactive ways. Analyzing static graphs and charts no longer is sufficient in our ever-changing digital economy.

Transforming industry

AI’s value in the industrial space is undeniable. It has the potential to skyrocket rates of profitability in manufacturing by an average of 39 percent by 20354, for AI brings a fundamentally different approach to decision making that ultimately will produce better results. It uses data to learn patterns that may not be obvious to a labored human eye faced with hundreds of parameters and inputs vs. just a handful of data points in the pre-digital days. As a result, AI presents a great opportunity to augment the essential human expertise of asking the right questions based on the specific needs of the environment and context. This learning is placed into a trained model, which can be deployed as close to the action as possible, transforming both the rate and the accuracy of prediction and decision making.

Rather than relying on rules encoded by humans, AI uses data to learn patterns that may not be obvious to a labored human eye.

But general AI models are not enough; instead, models must be applied specially to realistic applications, including energy management, asset performance, and operational productivity, to make AI worth the financial and human resource investments — not to mention the time. Bear in mind that AI technology is just the foundation. Actionable insights gleaned from continually training and re-training models hold the real key to the data-driven decision-making behind valuable business outcomes.

For example, Rolls-Royce has 13,000 commercial aircraft engines in service around the world. Microsoft enables Rolls-Royce to improve maintenance more proactively and precisely. First, Rolls-Royce collects and aggregates data from disparate and geographically distributed sources at an unprecedented level. Then Microsoft enables Rolls-Royce to analyze that data and perform data modeling at scale to accurately detect operational anomalies. The value here is that customers can plan relevant actions in real-time, using pilot-friendly dashboards to inform on-the-spot decisions and operations.

Here is where artificial intelligence (AI) can make IIoT investments pay off ($105 billion globally in manufacturing operations and another $45 billion in production asset management in 20173) — by giving companies dynamic tools to make better business decisions. That’s the beauty and magic of AI.

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