The future of AI depends on better data, not bigger models

Model scale is not the source of AI's advantage

The reason is straightforward. Poor data quality produces unreliable AI, regardless of how sophisticated the underlying model may be. This pattern appears repeatedly in systems that perform well in testing but struggle with real-world scenarios. These failures often trace back to data issues, including incomplete datasets, inconsistent labeling or training data that doesn't reflect actual use cases.

This matters particularly in high-stakes applications. In healthcare, financial services and other regulated industries, AI errors aren't just inconvenient; they carry material consequences. As AI moves from experimental projects to core business processes, tolerance for unreliable systems continues to shrink. Regulators are scrutinizing not only AI outputs but also the data and processes behind them. Organizations that treat data quality as an afterthought are increasing their exposure to regulatory and operational risk.

The limitations of model-centric thinking are becoming clearer. Large language models generate impressive text, but also produce hallucinations and perpetuate biases present in their training data. Recommendation systems drive engagement yet may amplify problematic content when the underlying data is poorly curated. As AI becomes embedded in the decision-making process, demand for explainability and accountability increases -- and that requires rigorous oversight of data.

Enterprise risk and competitive implications

From a strategic standpoint, data-centric AI offers a more sustainable path forward. Building ever-larger models requires enormous computational resources that few organizations can afford. Improving data quality, by contrast, is achievable for organizations of any size. It requires discipline and investment, but not access to massive compute infrastructure.

Organizations embracing this approach are seeing tangible results. Better data leads to more accurate models, faster development cycles and AI systems that deliver value in production. It also creates a defensible competitive advantage as proprietary, high-quality datasets are difficult for competitors to replicate.

Operationalizing a data-centric strategy

The path forward requires rethinking priorities. Organizations should begin by auditing existing data. Where are the gaps? What biases exist? How well does training data reflect real-world scenarios? These questions often reveal uncomfortable truths, but they're essential starting points.

Data infrastructure investment warrants the same attention traditionally shown to model development. This includes tools for data labeling and validation, development of data products, formal processes to maintain data quality over time and teams with both technical and domain-specific knowledge of the data.

Most importantly, it requires cultural change. Data work needs to be treated as a strategic function rather than a preprocessing task. Organizations that recognize data as a core asset and invest accordingly will be positioned to build AI systems that are more capable, trustworthy and sustainable over the long term.

Stephen Catanzano is a senior analyst at Omdia where he covers data management and analytics.

Omdia is a division of Informa TechTarget. Its analysts have business relationships with technology vendors.