Synthetic Data’s Impact On AI

Aditi Godbole – AI/ML Expert optimizing enterprise software for efficiency, innovation & data-driven decision-making.

One important fact that business leaders of today are well aware of is that “data” is the glue holding this digital ecosystem together. Yet, data presents the biggest hurdle for many companies in making progress on their products. Access to high-quality, usable data remains elusive for effective AI adoption. Companies struggle with the high cost of acquiring and labeling datasets, regulatory restrictions and privacy concerns, which slow down AI innovation.

Synthetic data—artificially generated datasets that mimic real-world data—offers a fast, secure and cost-effective approach. Healthcare and insurance companies like Anthem Inc. train fraud detection models and AI-driven healthcare solutions while maintaining HIPAA compliance. Autonomous vehicle operator Waymo and others use synthetic driving simulations to train AI for dangerous or rare scenarios. Banks generate synthetic transaction data to train fraud detection models without exposing real customer data.

However, synthetic data is not a universal fix. Its effectiveness depends on how well it is generated, validated and integrated into AI workflows. Let’s dive into how synthetic data can be used and the potential risks involved.

The Challenges Of Relying Solely On Real Data

Before diving in, let’s review the key challenges enterprise AI initiatives face. In most instances, delays stem not from a lack of innovation but from limited access to usable data. Many assume that AI success depends on vast amounts of real-world data, and at the same time, acquiring and utilizing that data is becoming increasingly difficult. The real driver of AI effectiveness isn’t just volume—it’s access to high-quality data.

Now, let’s explore the major barriers to data access and the risks associated with poor-quality data:

• Regulatory Barriers: Industries like healthcare, finance and consumer tech face strict data laws such as GDPR and HIPAA. Even when data is available, privacy concerns limit its usability.

• The Cost Of Data Collection: Acquiring, storing and labeling data is expensive. In manufacturing, labeling defect images for AI-driven quality control can take months.

• Bias In Ream Data: Hiring algorithms, loan approvals and medical diagnostics have shown biases inherited from historical data. In this situation, simply collecting more data does not fix the problem—it can worsen it.

For companies focused on AI-driven products, customer insights and automation, these barriers slow progress and introduce risks.

Applications Of Synthetic Data

Synthetic data is a strategic tool that should complement, not replace, real data. Instead of waiting months for real-world data collection, training datasets can be generated on demand, reducing time-to-market and speeding up AI development.

Synthetic datasets provide realistic training data without exposing sensitive information, which helps enhance privacy and compliance. To expand market reach and AI performance, companies can generate synthetic data to fill gaps in new geographies, demographics or edge cases.

AI-driven applications in customer analytics, computer vision and predictive maintenance require large labeled datasets. Synthetic data can deliver datasets large enough to be useful. Note that synthetic data is not about replacing real data but augmenting it to remove bottlenecks and drive AI innovation.

Key Considerations For Synthetic Data Adoption

Now we understand that Synthetic data is powerful but must be used wisely. Key risks of using synthetic data include:

• Balancing Synthetic And Real Data: Poorly generated synthetic data can cause AI models to fail in real-world scenarios. Over-reliance on synthetic data can introduce new biases if not carefully managed.

• Evaluating Data Quality: Synthetic data must retain statistical similarity, preserve relationships, and meet privacy standards. Companies use statistical benchmarking and human validation to ensure synthetic data aligns with real-world patterns.

• Limitations In Complex Use Cases: Synthetic data may not work well for tasks requiring deep contextual understanding, such as medical diagnostics or financial modeling. Additionally, advanced methods, such as GANs, require substantial computing power, adding to computational costs.

• Challenges In Rare Data Scenarios: Synthetic data generation needs real data as a starting point. For rare cases (e.g., a disease occurring in one in 100,000 cases), synthetic data helps by augmenting samples, using simulations or extrapolating from related datasets. However, if no real-world samples exist, meaningful synthetic data generation becomes nearly impossible.

• Regulatory Landscape: Governments and regulators are beginning to establish synthetic data governance frameworks, especially in industries like healthcare and finance. Enterprises should monitor evolving regulations to ensure compliance in their synthetic data use cases.

The Future

For AI leaders, synthetic data has the potential to be a business enabler. Companies that use synthetic data can work to deploy AI faster, expand into new markets and improve compliance.

However, the best AI strategies can not rely only on synthetic data. A hybrid approach—combining synthetic and real data—can create robust, generalizable and ethical AI models. As AI governance regulations evolve, enterprises that strategically and responsibly adopt synthetic data can lead the next wave of AI-driven transformation.

Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?