Artificial intelligence (AI) is entering a pivotal era. Its capabilities are expanding rapidly, reshaping a litany of industries from healthcare to media, the public sector, and beyond. Yet a significant challenge looms on the horizon — the availability of high-quality data to train the systems of tomorrow. Without action, the AI sector could face a slowdown in innovation, undermining the progress it promises.
The Exhaustion of High-Quality Data
AI systems, from large language models (LLMs) to multimodal models (LMMs), rely on vast amounts of data to learn, adapt and improve. Researchers estimate that by 2026, we will exhaust the supply of high-quality text data needed to train advanced AI models. This issue is compounded by increasing restrictions on data scraping and the rightful pushback from data rights holders. Lawsuits and regulatory crackdowns have made it clear — the days of unregulated data collection are likely over.
As a result, AI developers are turning their attention to alternative sources, including synthetic data. However, while synthetic data may fill short-term gaps, it presents serious limitations that make it unsuitable as a long-term solution. Created through algorithmic processes, this low-quality data may have niche applications, but its limitations make it unreliable for training complex, high-stakes AI systems.
Models trained on synthetic data are more prone to hallucinations, a phenomenon where AI generates outputs that appear credible but are entirely fabricated. These hallucinations undermine trust and, in critical domains like healthcare or finance, can lead to dangerous or catastrophic outcomes and legal repercussions. Moreover, synthetic data fails to capture the rich nuances of real-world information and function. It cannot replicate the cultural, historical, or contextual complexities embedded in authentic human experiences. Without access to real data, AI systems lose their ability to understand and respond to the intricacies of the world they are meant to serve.
Enterprise Data: An Untapped Goldmine
The solution to the data crisis lies not in generating artificial substitutes but in unlocking the value of existing enterprise data. Organizations across industries possess vast reservoirs of proprietary, unstructured data — audio recordings, emails, documents, videos, both short and long form, and customer interactions — that can fuel the next wave of AI innovation.
However, transforming raw data into AI-ready assets is no simple task. This process demands robust technological infrastructure, strict compliance measures, and deep expertise. Without these capabilities, organizations risk mismanaging sensitive information, violating privacy regulation,s or failing to realize the full potential of their data.
Refining Data for the AI Era
Veritone’s aiWARE platform addresses these challenges by serving as an AI operating system and data refinery that transforms unstructured data into structured, AI-ready assets. Acting as an orchestration layer, aiWARE integrates seamlessly with enterprise data systems and third-party models, enabling organizations to manage their data securely and efficiently.
But refining data isn’t just about internal use — it’s also about responsible monetization. As the demand for high-quality training data grows, enterprises can ethically and transparently license their proprietary datasets to AI developers. By doing so, they not only generate new revenue streams but also contribute to the advancement of next-generation AI technologies.
Ensuring Ethical and Secure AI
Data security and ethical use must remain at the forefront of this transformation. Veritone’s longstanding commitment to protecting intellectual property and adhering to global privacy regulations, such as GDPR and SOC2, ensures that data rights holders retain control over how their assets are used. Granular permissions and access controls further guarantee that data is handled responsibly.
Why Real Data Matters
AI’s ability to learn, adapt, and solve problems depends on the quality of the data it consumes. Synthetic data, while useful in limited contexts, cannot replace the richness and reliability of real-world data. As we navigate the data scarcity crisis, enterprises have an unprecedented opportunity to lead the charge by refining, managing, and responsibly sharing their proprietary data.
At Veritone, we’ve spent over a decade at the forefront of AI innovation, helping some of the world’s most recognizable brands transform their data into actionable intelligence. Our mission is to ensure that AI continues to serve humanity in meaningful and trustworthy ways. By prioritizing real data and ethical practices, we can unlock AI’s full potential and pave the way for a future where technology truly enhances the human experience.
