AI Development Issues Synthetic Data Can Help You Overcome

Forbes

07-07-2025

As AI systems become more sophisticated, the challenges of training them effectively—and responsibly—continue to grow. The use of real-world data often comes with concerns and roadblocks—privacy risks, inconsistent formats, gaps in edge cases and regulatory hurdles can all slow development or skew outcomes. Synthetic data offers a promising alternative, delivering clean, scalable and customizable datasets that can augment—or even replace—traditional data in key use cases.
Below, members of Forbes Technology Council share real-world challenges that come with training AI systems and how synthetic data can help address them. Their insights highlight how developers can overcome data-related barriers while building smarter, safer AI models.
1. Lack Of Edge Case Data
Synthetic data can help address the challenge of edge cases in your real-world data, which, by definition, doesn't have enough examples to create a training set. The real-world data can be used to identify an edge case your AI may encounter, but you leverage synthetic data to create variations of that edge case for machine learning. This hybrid approach is often most effective in terms of cost, time and so on. - Radha Basu, iMerit
2. Inconsistency And Lack Of Control
One of the major challenges is inconsistency and lack of control. Real-world data is messy, biased and often incomplete, making it hard to scale or use reliably in training high-performance models. Synthetic data solves this by offering precision, balance and control at scale. Synthetic data gives AI developers the ability to test, stress and scale models in ways real-world data simply can't match. - Alexandre de Vigan, Nfinite
Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?
3. Unpredictable Training Environments
Remember the debate about synthetic versus petroleum-based oil? Similar to how older engines were run with nonsynthetic oil, legacy businesses use messy, sensitive and unpredictable real-world data, resulting in poor AI. For smart, modern businesses, synthetic data, like synthetic oil, allows developers to train models in predictable and controllable environments, ensuring strong performance in the real world. - Robert Clark, Cloverleaf Analytics
4. Privacy And Scale Limitations
Synthetic data avoids sensitive personal information, which reduces privacy concerns, and can be generated efficiently at scale, making it ideal for training large models. Real-world data—like patient records—is often messy and unpredictable. However, training only on synthetic data can limit a model's ability to perform well in complex, real-world scenarios. - Tim O'Connell, emtelligent
5. Incomplete And Biased Datasets
One challenge with real-world data is that it can be biased or incomplete—like teaching someone to drive only on city streets but not highways or country roads. Synthetic data fills these gaps, adding detail and diversity that real-world data might lack. However, synthetic data only leads to smarter, fairer, more reliable AI if it's high-quality and generated with standards to minimize bias. - China Widener, Deloitte
6. Sensitive Industry Restrictions
AI developers face the significant challenge of addressing issues related to data privacy. Obtaining relevant information within sensitive industries presents unique difficulties, especially when dealing with regulated elements. Synthetic data helps alleviate this burden. - Michael Gargiulo, VPN.com
7. 'Class Imbalance'
Real data has bias due to 'class imbalance.' Imagine a scenario where using AI for résumé screening fails since the model has been unintentionally trained on a dominant class (favoring male over female or giving more weight to a certain demographic), as that's what was available as historical data. Synthetic data can overcome this, as long as proper context is given for data generation. - Arjun Srinivasan, Wesco
8. Scarcity Of Rare Scenarios
I believe that AI developers often struggle with obtaining large, diverse datasets that include rare edge cases—such as unusual driving scenarios for autonomous vehicles—which are difficult and costly to capture in the real world. Synthetic data can generate these rare but critical conditions at scale, improving model robustness without compromising user privacy. - Mark Vena, SmartTech Research
9. Privacy And Distribution Barriers
Real-world data often comes with privacy constraints and regulatory friction. Synthetic data solves distribution gaps in training sets by generating edge cases for mission-critical systems. It allows AI developers to simulate realistic, diverse datasets without exposing sensitive information. - Andrey Kalyuzhnyy, 8allocate
10. Rare Event Modeling Needs
For AI models, the rule is often 'the more data, the better the model.' However, if you are trying to model events that rarely happen, such as communications involving insider trading, bribery, harassment and other such events, the only way to get enough data is to create it. By using synthetic data that is then reviewed by subject matter experts, you can have enough examples to create great models. - Vall Herard, Saifr
11. Voice Diversity Challenges
One key challenge with real-world voice data is obtaining sufficient diversity and volume, especially for rare accents, speaking styles or noisy environments. Synthetic voice data overcomes this by generating limitless, tailored examples, including difficult-to-capture scenarios, without privacy concerns. This enables training more robust AI models. - Harshal Shah
12. High Data Acquisition Costs
The simple acquisition of real-world data can be difficult and costly. Synthetic data can help, but it needs to be evaluated carefully for quality and assessed for its potential impact on the training of a model. - Leonard Lee, neXt Curve
13. Privacy-Conscious Experimentation
Real-world data often limits innovation due to privacy and regulatory barriers. Synthetic data helps AI developers simulate edge cases and future scenarios that don't yet exist. This enables safer experimentation, faster iteration and smarter models without compromising sensitive information. - Rishi Kumar, MatchingFit
14. Ignored Edge Users In CX Data
Real-world customer experience data often ignores edge users—the 'silent majority' who never complain; they just leave. Synthetic data enables you to simulate and operationalize a retention strategy before it's too late. It's not just a data problem; it's a CX risk. - April Ho-Nishimura, Infineon Technologies AG
15. Corrupted Or Low-Quality Real Data
The use of corrupted real-world datasets for training can silently compromise AI models and cause unreliable results. Synthetic data eliminates this risk by providing clean, controlled datasets when real-world data quality issues are affecting model performance. - Chongwei Chen, DataNumen, Inc.
16. Simulating Future Scenarios
Real-world data is stuck in yesterday's world—permissioned, fragmented and slow. Synthetic data isn't just a privacy workaround; it's a simulation engine. Developers can now model edge-case chaos, future scenarios or AI-on-AI interactions at scale, long before reality catches up. That's not a patch. That's evolution. - Akhilesh Sharma, A3Logics Inc.
17. Cross-Silo Collaboration Barriers
AI developers attempting to collaborate across silos (such as government agencies) where data sharing is challenging or explicitly forbidden are able to exchange synthetic datasets. This improves model stability and time to release by allowing multiple parties to share in the model evaluation process and reproduce bugs to broaden the troubleshooting audience. - Matthew Peters, CAI
18. Reactive Versus Proactive Modeling
A major challenge with real-world data is its stagnancy. It reflects what has been, not what could be. Synthetic data allows AI developers to generate rich, forward-looking scenarios that model emerging trends, unseen behaviors or disruptive events. It shifts AI from reactive to proactive, enabling systems to anticipate and adapt in a world that evolves faster than yesterday's data. - Sandipan Biswas
19. Inconsistent Labeling
Real-world data is messy. Labels are often inconsistent, even among experts, and that noise quietly limits how far your models can go. Synthetic data gives us clean, perfectly labeled ground truth. We use it to identify annotation errors and train models that handle uncertainty more effectively and surpass accuracy ceilings—without incurring the costs of relabeling. - Gavita Regunath, Advancing Analytics