Artificial intelligence is already reshaping the way organizations operate across Australia, from transport networks and retail environments to healthcare and critical infrastructure. But as adoption accelerates, a less visible challenge is emerging beneath the surface: the availability of high-quality data.
The effectiveness of an AI system is determined by the data used to train it. There is a saying that goes, “You are what you eat.” The requirements become even more demanding in the world of video analytics, where systems must interpret complex real-world environments. Models require large, diverse, and representative datasets to provide accurate and reliable results. Without these, even the most sophisticated algorithms are relatively worthless.
For this reason, synthetic data is emerging as a game-changer in the world of video analytics.
Data bottlenecks slowing AI progress
Australia is no stranger to rapid growth in AI. Organizations are increasingly incorporating AI into their operations at every level to improve decision-making, whether it’s optimizing traffic flow, improving retail store layouts or improving safety. Recent statistics show that data assets have grown by 30% in the past 12 months.
However, many face a common constraint: accessing the right data at scale.
Training AI models using real-world video data isn’t always easy. Privacy considerations, regulatory obligations, and practical limitations all play important roles. In sectors such as healthcare, aged care and critical infrastructure, all of which are highly relevant in Australia, the collection and use of large amounts of real-world footage can raise ethical and legal concerns.
Especially as regulations such as the updated SOCI Act and APRA’s CPS 230 and 234 take effect, administrators are under pressure to keep data safe and secure and ensure that individual privacy is protected.
There is also the issue of representation. Real-world datasets are often incomplete or biased, reflecting only a subset of possible scenarios. This can lead to gaps in the actual performance of AI systems, especially in edge cases where accuracy is most important.
Synthetic data as a strategic enabler
Synthetic data offers an attractive solution.
Simply put, synthetic data is data that is at least partially artificially generated and replicates real-world situations. For video analytics, this means creating simulated environments and scenarios that LLM and AI models can learn from without relying solely on real footage.
This approach offers several advantages.
First, address privacy and compliance challenges. Synthetic datasets can exclude identifiable individuals, allowing organizations to train AI models without exposing sensitive information. This is an increasingly important consideration as Australia strengthens its regulatory framework around data protection and operational resilience.
Second, it increases data diversity. Synthetic environments can be designed to include a wide range of scenarios, behaviors, and conditions far beyond those typically captured in real-world datasets. This diversity and richness of input data results in more robust and reliable AI models.
Third, development will be accelerated. Synthetic data can be generated quickly and at scale, reducing the time and cost needed to build and improve AI systems.
From theory to real-world impact
The true value of synthetic data becomes even more apparent when applied to safety-critical environments.
Globally, we are already seeing examples of synthetic data being used to train AI systems to detect accidents such as people falling into waterways, scenarios that are difficult, dangerous, or unrealistic to capture in real life.
By simulating these events, developers can build models that respond more quickly and accurately in real-world situations, ultimately improving emergency response outcomes.
In the Australian context, the implications are significant.
From monitoring congested transportation hubs to enhancing the safety of public spaces and critical infrastructure facilities, synthetic data can help organizations prepare for rare but high-impact scenarios. This allows for a level of readiness not achievable with traditional data collection methods.
Balancing innovation and responsibility
As with other AI advances, the growth of synthetic data must be carefully managed.
While this certainly helps address many of the challenges associated with real-world data, it also introduces new considerations. Synthetic datasets must be carefully designed to accurately reflect real-world conditions. Poorly constructed data can lead to flaws in models and undermine trust in AI systems.
This is where an open platform approach can play an important role. By enabling interoperability and transparency, organizations can use synthetic and real-world data sources, validate results, and continuously improve the performance of their AI models.
The future of intelligent video
Looking to the future, synthetic data will play a fundamental role in the evolution of AI-driven video.
As technologies such as digital twins, edge computing, and advanced simulation environments mature, organizations will be able to model entire systems, from city infrastructure to major industrial operations, and train AI against these virtual environments before deploying them in the real world.
This is a huge opportunity for Australia. With increasing focus on smart cities, infrastructure resiliency, and public safety, synthetic data can help accelerate innovation while maintaining the high standards of privacy and governance that must now be followed locally.
AI’s moment of truth
The next stage of AI will not be defined solely by algorithms, but by data. How is it created, how is it managed, and how is it used?
Synthetic data represents a change in how an organization thinks about its challenges. This moves the conversation from limitations to possibilities, enabling AI systems that are not only more capable, but also more ethical and scalable.
For those working in video technology, the message is clear: the future of AI is not only captured, but created.
