2. Simulate everything: Seize AI’s virtual power
It’s close to unanimous: 96% of survey respondents plan to use AI simulations, such as digital twins, this year. AI simulations are powerful, because they can do more than provide detailed, real-time insights into current performance. They can increase the speed and help lower the risk of your future operations. By modeling huge numbers of scenarios in parallel, simulations let you quickly project likely events and “game out” your leading real-world actions without taking any real-world risks. For example, when you bring together simulations of suppliers, customers, competitors and the weather, you can better predict supply chain pricing dynamics and disruptions — and have a plan in place to navigate them.
A holistic approach offers a particular advantage for more complex simulations, such as forecasting market conditions and addressing supply chain challenges. With time, simulations may also help overcome talent challenges. Already, nearly two-fifths of “holistic” companies are using AI simulations to help hire and train employees. AI-powered virtual reality simulations enable better virtual recruiting, access to talent in far-flung geographies, better monitoring of remote workers and the upskilling of even hands-on roles.
What companies can do
Create synthetic data. Machine learning models require huge amounts of data — which simulation models can create. For facial recognition, for example, instead of acquiring images of faces from multiple angles, contrast levels and brightness, simulations can generate them to train machine learning models. Synthetic data, which AI simulations can provide, can turbocharge other AI and analytics initiatives.
Make digital twins a platform too. To effectively use AI’s power to create business-relevant simulations, consider (as part of AI’s integration with data platforms and cloud) making digital twins a platform capability — to help make sense of your various data sets in the context of your business, your customers and your products.
- Align your specialists. Your simulation specialists very likely have an engineering background, while your data scientists will typically be more experimental scientists. Bringing these specialists together with each other and with business leaders is key to solving simulation problems.