Report highlights emerging web data infrastructure layer as bottleneck for enterprise AI systems

The report describes a growing gap between AI model capabilities and the data pipelines required to ground them in real-world, up-to-date information. Early AI advances relied on static, large-scale training datasets, but organizations now need continuous feeds of fresh, verifiable data to support dynamic decision-making. This shift places new demands on compute, networking, retrieval, and data engineering—capabilities that many enterprises are not yet equipped to handle at scale.

According to the report, 56% of AI practitioners surveyed said businesses need access to real-time web data to improve trust in AI outputs. The emphasis on real-time data reflects a broader recognition that static snapshots are insufficient for applications such as pricing engines, sentiment tracking, and threat detection, where conditions change rapidly.

Citing Gartner, the report states that 60% of AI projects not supported by AI-ready data—defined as accurate, structured, organized, and contextualized—will be abandoned by the end of the year. This suggests that data readiness is becoming a decisive factor in the success or failure of enterprise AI initiatives.

The infrastructure layer described would need to retrieve data across hundreds of millions of existing domains and billions of new URLs created weekly, while handling geographic, linguistic, and access-rule variability. It must also operate with low latency and emulate human-like browsing behavior to bypass anti-bot measures and access JavaScript-heavy sites.

The report highlights technical and governance challenges, including compliance with privacy frameworks such as GDPR and CCPA, and the need to avoid paywalled or private data. It notes that building such capabilities in-house diverts engineering resources from core AI development, pushing many organizations toward specialized platforms for data retrieval and orchestration.

The piece frames this infrastructure as critical to reducing hallucinations and improving contextual relevance, with one executive quoted saying that pairing powerful models with hollow knowledge layers yields systems that are ‘useless in practice.’