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Gartner Highlights Powerful Combination Of AI & Simulation

Gartner Highlights Powerful Combination Of AI & Simulation

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.

Four Key Findings

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:

  • Even if many organizations treat AI and simulation as distinct technological capabilities, AI initiatives can benefit greatly from simulation through the generation of synthetic data or using simulation environments for training and testing models.
  • Simulation, too, can be accelerated, optimized, and augmented by AI, and this is a reason that simulation platforms are increasingly incorporating AI capabilities into their platforms.
  • Many of today’s AI initiatives in the industry are narrowly focused and only leverage data for a specific use case. This lack of reusability leads to inefficiencies, technical debt, and an incapacity to scale across a business.
  • General-purpose platforms that combine AI and simulation are emerging, though most of the market remains fragmented and focused on specific niches.

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.

Four Primary Recommendations

The analysts made four primary recommendations for data and analytics leaders responsible for AI initiatives:

  • Complement AI with simulation to optimize business decision making or to overcome a lack of real-world data by offering a simulated environment for synthetic data generation or reinforcement learning. 

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.

  • Complement simulation with AI by applying deep learning to accelerate simulation and generative AI to augment simulation. 

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.

  • Create synergies between AI and simulation teams, projects and solutions to enable the next generation of more adaptive solutions for ever-more complex use cases. Incrementally build a common foundation of more generalized and complementary models that are reused across different use cases, business circumstances and ecosystems. 

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.

  • Prepare for the combined use of AI, simulation and other relevant techniques, such as graphs, natural language processing or geospatial analytics, by prioritizing vendors that offer integrated platforms based on composite AI and decision intelligence principles. 

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.