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Why your AI strategy needs more than data

By Remi Duquette, Vice President Innovation and Industrial AI at Maya HTT

The artificial intelligence (AI) revolution has been underway since about 2016. As a result of great increases in computational power, AI no longer belongs to the realm of media hype and science fiction. Today, AI offers concrete benefits in all areas of engineering, manufacturing, and operations. From deep neural networks and long short-term memory (LSTM) algorithms to reinforcement and physics informed neural networks (PINN), the possibilities are endless, and the real-world applications are only just beginning to truly be exploited. Industry data holds gold nuggets; AI is the key to finding and using them.

As companies seek to take advantage of AI, one of the first challenges is how and where to start.

Simcenter partner, Maya HTT have collated insights and advice from leading experts in applied industrial AI to deliver a no-fluff rundown of what you need to know and do to prepare the right way.

By some accounts, as many as 85% of AI projects fail. Many more companies run into problems with their data. Data quantity is important, but so is quality. Having an AI goal is not enough to succeed. Strategy and preparation are key.

Identify goals & establish a strategy Business goals, not AI technologies, must drive the AI roadmap. Leverage only those AI technologies that align with and serve the business goals throughout the company to increase overall efficiency. Strategy helps AI become a cellular reality across the enterprise. Without strategy, AI remains, at best, a set of more-or-less successful projects.

Start Small: Early Results Are Important Plan first to apply AI in small ways. Find early business challenges for which an AI approach makes sense and where existing data is good and has few gaps. The end goal is enhanced accuracy, reliability and efficiency, and naturally, innovation.

Choose the Right AI Partner A great deal of the hesitation organizations have about adopting AI stems from not knowing how to proceed. With the rapid pace of technological advancement, both in AI and IoT/IIoT, it is difficult for organizations to keep up. Understanding the challenges, pitfalls, and change management preparation required is critical.

The right partner should:

  • Provide knowledgeable support,
  • Be the right fit, and
  • Identify any blind spots.

It’s all about data As many organizations understand that data plays a key role in AI, the primary concern when planning to implement an AI strategy is often to gather enough data and high quality data. Although it’s true that the IoT and IIoT produce a staggering amount of data, that alone is not enough to get started. Successfully launching AI, automation, and machine learning requires the right data, and clean data.

Plan for Change Management Corporate leaders who want to help their organization reap the benefits of AI should incorporate change management considerations in their AI strategy and solution. The assistance of a knowledgeable support partner early in the process can help leaders proactively manage employee expectations about their changing realities and ensure a smooth implementation of their AI strategy.

AI - Now & For the Future AI, machine learning, and deep learning will have a major impact on all companies in the near future. Successful implementation of this new technology cannot happen with a start-and-stop or piecemeal approach.

AI is a powerful and accessible set of new technologies that organizations of all sizes can use to remain competitive that begins with setting business goals, strategy, and small wins. Don’t underestimate the need for change management. With a strategic approach, you can benefit from AI now and in the future Discover the potential AI holds for manufacturing, and find out how you can maximize AI success and ROI.

Take the first step into your AI future.

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