How Synthetic Data Generation Solves AI's Training Problem

The data problem in AI isn't what most people think it is. It's not just about volume—though that matters—it's about structure, privacy, and the fundamental gap between how information exists in the world and how models need to consume it.

Legare Kerrison, speaking for IBM Technology, walks through synthetic data generation as a solution to this gap. The example he uses is straightforward: you find a scientific paper on quantum batteries and want to build a chatbot that can answer questions about it. Simple enough in concept. But the paper is unstructured text—paragraphs, tables, equations—and machine learning models cannot learn directly from that format.

This is where synthetic data generation enters, not as a workaround but as an increasingly essential part of the enterprise AI pipeline. The question is whether it's being deployed thoughtfully or whether it's creating new problems while solving old ones.

The Pipeline: Structure, Seed, Scale

Kerrison outlines a three-stage process. First, you structure the unstructured data. Tools like Dockling use OCR and parsing to convert PDFs into something a model can digest—not a blob of text but a table of concepts and definitions. This step is foundational and largely uncontroversial. Computers need structure.

Second, you create seed data: manually written question-and-answer pairs that teach the model not just what information exists but how to respond to queries about it. "You want to teach your model how to respond, not just the information that's in your research paper," Kerrison explains. This is where human judgment shapes the model's behavior.

Third—and this is where synthetic data generation matters—you scale those seed examples. A model analyzes the distribution of your real Q&A pairs, identifies patterns, and generates new examples that follow the same structure. For chatbots, this means expanding a handful of manually crafted pairs into hundreds or thousands of realistic variations.

The technical mechanism is straightforward enough. What's worth examining is what happens when this process runs at scale across enterprise environments.

Privacy by Synthesis

One claim Kerrison makes deserves scrutiny: "You can preserve privacy by generating synthetic data that's statistically similar to your source but contains no real identifiers." This is the promise of synthetic data in regulated industries—healthcare, finance, anywhere personally identifiable information creates liability.

The theory is sound. If you can generate data that preserves statistical properties without containing actual individual records, you can train models without exposing sensitive information. But the practice is more complicated. Recent research has shown that synthetic data can sometimes be reverse-engineered to reveal information about the source dataset, particularly when the synthetic data is highly accurate.

The tension here is real: make synthetic data too similar to the source and you risk privacy violations; make it too different and it loses utility for training. Projects like SDGHub, which Kerrison mentions, attempt to thread this needle by validating synthetic data for "faithfulness to the source, relevance to the source and diversity of data."

What's notable is that IBM's approach keeps the process local. "You could run these flows locally so that your source data never has to leave your environment," Kerrison notes. This matters in enterprise contexts where data governance policies prohibit external API calls with proprietary information. The synthetic data generator connects to "any open AAI compatible endpoint or claude uh Olama VLM even your hosted model" to create and validate examples without sending source material to third-party services.

This addresses one privacy concern while surfacing another: if the model generating your synthetic data was itself trained on broad internet data, what biases or patterns is it importing into your domain-specific training set?

The Reproducibility Requirement

Kerrison emphasizes that synthetic data generation pipelines must be reproducible, calling this "essential for enterprise AI workflows." This is not a minor technical detail. In regulated industries, you need to be able to demonstrate exactly how training data was generated, validated, and used.

Reproducibility also matters for debugging. When a model fails or produces unexpected outputs, engineers need to trace the problem back through the training pipeline. If synthetic data generation involves non-deterministic processes or changes based on the state of the generating model, this becomes significantly harder.

But reproducibility creates its own constraints. Lock down the data generation process too tightly and you lose the benefits of diversity that synthetic data is supposed to provide. Run the same generation process multiple times and you might get useful variation—or you might get artifacts and errors that compound across training runs.

What This Actually Solves

Synthetic data generation addresses three concrete problems:

Data scarcity in specialized domains. If you're building a model for a narrow technical field, you simply may not have enough real examples. Synthetic generation can bridge that gap, though with the caveat that it can only recombine and extrapolate from patterns in your seed data—it cannot introduce genuinely novel information.
Class imbalance. In datasets where certain categories are rare, synthetic data can "balance rare classes," as Kerrison puts it. This is particularly valuable in scenarios like fraud detection or rare disease diagnosis, where the events you care most about are, by definition, uncommon.
Testing and validation before deployment. You can "test your pipelines before deployment" using synthetic data that mimics production data without the risk of exposing real user information or real business data.

What it doesn't solve is the fundamental question of whether the model architecture and training approach are appropriate for the task. Synthetic data generation is a data augmentation technique, not a substitute for sound model design.

The Open Questions

The move toward synthetic data generation at enterprise scale raises questions that IBM's presentation doesn't address:

How do you validate that synthetic data hasn't introduced subtle biases or correlations that don't exist in the real world? The validation checks Kerrison describes—faithfulness, relevance, diversity—are necessary but may not be sufficient.

What happens when multiple organizations generate synthetic training data from similar but not identical source material? Do models trained on synthetic data from different generators behave systematically differently, even when trained for the same task?

And perhaps most importantly: as synthetic data becomes a larger component of training datasets, what happens when models trained on synthetic data are themselves used to generate the next generation of synthetic data? This isn't a hypothetical concern—it's an emerging pattern in AI development.

Kerrison frames synthetic data generation as "worth checking out to reach that scale." That's a reasonable technical assessment. Whether it's worth relying on for production systems depends on how thoroughly organizations think through these questions before committing to the approach.

—Samira Okonkwo-Barnes, Tech Policy & Regulation Correspondent