New research from leading Gartner analysts has highlighted the enormous value that can be unlocked through the combination of artificial intelligence (AI) and simulation.
Writing in Innovation Insight: AI Simulation, analysts Leinar Ramos, Anthony Mullen, and Pieter den Hamer explain that AI and simulation are increasingly deployed together to enable more versatile and adaptive systems. They recommend that leaders in data and analytics teams in all industries combine these technologies to improve machine learning outcomes, enable more sophisticated decision intelligence, and accelerate the process of business optimization.
Beginning from the assumption that the majority of AI models will be trained in simulated environments by 2030, the Gartner analysts identified four key findings for data and analytics leaders:
The analysts present two different ways to combine AI and simulation – Simulation-Assisted AI and AI-Assisted Simulation – with both demonstrating three distinct use cases.
The analysts explored those use cases in some detail. For example, the ‘Simulation and optimization’ use case was proposed to be a good fit for both financial service organizations (which might make trading simulations more realistic by incorporating AI agents) and manufacturers (which might apply AI to analyze large volumes of simulation data to predict failures in equipment as part of a digital twin).
With the value of combining AI and simulation clear, the analysts made some specific recommendations for industries ranging from manufacturing, logistics, and supply chain, through asset-intensive industries like the energy and utilities sector, to retail, healthcare, financial services and more.
The analysts made four primary recommendations for data and analytics leaders responsible for AI initiatives:
The utility of synthetic data for businesses, particularly in manufacturing, supply chain, and asset-intensive organizations, has already been established, and AI is a natural and complementary addition to a synthetic data project.
Simulation projects can benefit from AI, too, especially when it comes to the speed of execution for the most complex simulations or the application of generative AI to fill gaps in simulation models.
At the business level, it is important that the teams responsible for AI initiatives and simulation initiatives work together. By combining AI and simulation into a single technological approach, organizations can deploy the technology more broadly across their business and scale these initiatives to recoup the investment in cutting-edge decision intelligence more quickly.
While the analysts are convinced that the combination of AI and simulation presents an opportunity for enterprises to unlock trapped value and drive better decision making, they also recognize that these two technologies need not be fenced off from other tools. Graphing, NLP, and geospatial analytics are just some of the other approaches that could be added to an AI-Simulation pairing, and the analysts recommend leaders in data and analytics favor vendors that provide platforms capable of integrating AI, simulation, and more.
