Why AI‑Native Big Data Platforms Are About to Explode

Frontier: AI Core Transformation Is Interaction Mode

Since the surge of large‑model technology, breakthroughs have been limited to customer service and process automation, while decision‑support applications have seen little progress. The author cites a report on enterprise large‑model challenges, noting that current AI accuracy, explainability, reproducibility, stability, and rapid BadCase resolution are insufficient for robust decision support.

Industry experts (referenced from a conference summary) highlight several core insights: large‑model intent recognition, small‑model output quality, and scenario‑specific models. They argue that explainability, stability, and BadCase analysis are domains where big data excels, and that big data has long been a core module for commercial decision‑making across industries.

AI + Big Data: Concept Explosion

In the current year, major internet firms such as Tencent and Alibaba have announced “AI‑Ready Intelligent Data Platforms,” while organizations like the World Bank promote an “AI4Data + Data4AI” flywheel effect.

Cloud‑native databases Snowflake and Databricks have each outlined clear AI transformation plans, including acquisitions and the formation of AI teams.

New Opportunities in AI Industrial Cloud

The author references a discussion on AI’s advantages for Digital Twin applications. With protocols such as MCP, A2A, ACP, and ANP maturing, AI‑Native hardware and software systems like Agora and LMOS are becoming ready.

Rebuilding AI‑Ready Data Infrastructure

The need to reconstruct the entire data flow from an AI‑Ready perspective is emphasized, requiring all data storage processes and service interfaces to undergo AI‑Ready transformation.

This transformed data platform is also called Data 4.0, illustrated by the accompanying diagram.

Within the Data 4.0 scope, Databricks has introduced a Data Intelligence Platform, and at the Data and AI Summit 2025, it highlighted the integration of AI and analytics.

While the analytical layer can be relatively straightforward to upgrade, the real challenge lies in automating data acquisition and creating AI‑driven data indexes.

Service Code‑Generation for Data Platforms

Large models empower data platforms by enabling services to be expressed as code generated by AI. Consequently, every data‑platform service can be represented as specific code, which large models can produce on demand.

This creates a logical flywheel where AI‑for‑Data continuously generates code‑based solutions.

Conclusion

Gartner predicts that current AI inputs and agents have achieved significant breakthroughs in descriptive and diagnostic analytics. Future empowerment at predictive, prescriptive, and insight levels will depend on AI‑native big data platforms.