Today’s product development cycles are increasingly complex. In the past, connections throughout product creation were limited. Someone thought of an idea, they designed it as a prototype, and, if it worked, they built it to scale. There was no connection between what was imagined and what was physically created. Today, we have digital threads that maintain the connection through every phase of the product life cycle and the potential for data analytics is huge throughout this process. When compared to data scientists, engineers are relatively plentiful. According to the EU, there are 3 million mechanical engineers in Europe alone. More importantly, by definition, mechanical engineers have an aptitude for mathematics and are familiar with interpreting data. Both are core skills for any aspiring data scientist. What engineers traditionally lacked was coding skills but the invention of low-code / no-code data science solutions have changed all that. Hybrid AI, the combination of physics and data science, is the key to unlocking the potential of artificial intelligence in industrial settings.
Early adopters are already reaping the benefits of the convergence between data, AI and engineering. We cannot wait any longer by complaining about resource and budget constraints. We need to start small with low-complexity, high value, high visibility use cases. In 2020, Gartner suggested that within five years, more than 50% of products would come with a digital twin. Previously only available to businesses with vast R&D teams, the recent advancements in data integration and general democratization of machine learning has meant that this technology can become more widely used We will learn how Altair clients like Ford, Rolls Royce, Leonardo and Prodrive are pioneering the use of low-code machine learning solutions to upskill their engineers and democratise the power of this technology enterprise-wide