Step by step, AI has been permeating virtually every application we use. From consumer-facing interactions to more advanced predictive B2B analytics, AI and ML algorithms consume increasing amounts of data. While thousands of companies have begun collecting data in vast quantities, the problem is that these data take a while to clean and prepare for AI consumption.

The efficacy of an AI system depends on the quality of the data it’s trained with. Real-world data comes with significant restrictions regarding its use and is limited in variance. As a result, the number of scenarios in which any given algorithm can be trained is often limited.

Synthetic data sets have begun making an impact in industries where AI use is critical. Here’s how four big sectors are using synthetic data to power their AI applications.


There is, arguably, no industry where the use of AI is making a bigger difference than in defensive systems. This sector has become increasingly reliant on diverse AI use cases from risk assessment and threat mitigation to preventing the loss of human life. Given the chaotic nature of battlefields and threat scenarios, training AI using solely real-world data is impractical.

For starters, the frequency with which incidents occur is unpredictable. Secondly, it’s impossible to train ML algorithms to recognize every permutation of an extreme situation. Synthetic datasets generated based on real-world data sets or simulated scenarios can help defense departments build AI systems to respond to any threat imaginable.

A big reason for this is synthetic data’s flexibility. “You can create synthetic data for everything, for any use case,” notes Don Herman, co-founder and CEO of synthetic data generation company OneView, “which brings us to the most important advantage of synthetic data – its ability to provide training data for even the rarest occurrences that by their nature don’t have real coverage.”


Energy and infrastructure development companies face regular headaches monitoring their assets and the progress of their projects. Manual processes require employees to fly out to the site and report progress back to their teams. A more modern approach is to use satellite imagery or drone footage.

However, even these systems have a manual bent to them. Employees have to review footage and make sense of imagery that might not be of the highest quality. The process is time-consuming and inefficient. AI usage is increasing in infrastructure monitoring, but the lack of real-world data is a stumbling block.

“The reality is that the cost of quality data acquisition is high, and this is acting as a barrier preventing many from considering AI deployment,” writes Darminder Ghataoura, Fujitsu’s AI lead. “To tackle this challenge, organizations are increasingly looking towards synthetic data to address the data shortfall that is preventing AI adoption.”

The data quality gap is especially poignant in infrastructure modeling projects. Assets such as pipelines, roads, power lines, and solar panels vary significantly and can change over time in numerous ways. 

Synthetic data is the easiest way for companies to quickly create a large number of situations and prepare their AI systems for real-world use. Companies train their algorithms on datasets that contain both extreme and normal situations, thereby allowing AI-monitoring of critical infrastructure for damage and adverse changes.

Insurance and Asset Monitoring

Another industry that places a premium on asset monitoring is the insurance business. Specifically, reinsurers underwrite policies that cover critical facilities, and monitoring their status is essential to enforcing the complicated terms and conditions that accompany these contracts.

Given that an insured asset can take any form, relying on real-world data to train ML algorithms is time-consuming and inefficient. An insurance company cannot wait for disaster to strike and then use that data to train its algorithms. The idea is to prevent disaster from striking in the first place and mitigate situations that cannot be prevented.

Like infrastructure companies, insurance organizations use camera footage to monitor asset conditions, but the process is fraught with error. It’s impossible to constantly monitor asset changes over time and deduce patterns from them manually. Therefore, risk assessment errors, insurance fraud, and instances of preventable damage fly under the radar.

Using synthetic datasets, companies can simulate many scenarios, no matter how unlikely they are to occur, and train their AI systems to spot their possible onset quickly. As a result, insurers and their clients can mitigate any damage or rectify processes that might be causing it.

Urban Planning

As our cities become more sophisticated, it increasingly makes sense to use AI for addressing common issues that afflict densely populated areas. Problems such as crime, traffic jams, air quality, municipal service delivery, and public transport efficiency require considerable resources and foresight.

Developing solutions to these issues often requires working through multiple complex scenarios. For example, creating ideal traffic routing mechanisms is a tough task when done manually. “One vital aspect here is to record, analyze and simulate human mobility behavior,” explains Arno Klamminger from the Austrian Institute of Technology. “We also need to assess and evaluate the effect of planned measures on individual infrastructures or the entire transport system.”

AI usage is optimal for such scenarios, and synthetic datasets can present many situations for ML training. With different solution paths and scenarios accounted for, it becomes easy for AI systems to model and identify optimized solutions. The result is smarter cities and a better quality of life.

Synthetic but practical

Synthetic data is the best solution to enhance AI adoption in the real world. Thanks to the ease with which it can be generated and its numerous use cases, synthetic data plays a major role in enhancing AI development in businesses.

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