From data assets to AI productivity, AI industrialization is pushing data infrastructure to a more important position.

AI Industrialization Enters a New Stage, Data Infrastructure Is Being Rebuilt

In 2026, as the large-scale application of AI accelerates, the importance of the data side has risen further. The Ministry of Industry and Information Technology and the National Data Administration jointly launched the "Model-Data Resonance" initiative, bringing high-quality datasets, distinctive intelligent agents, and "Model-Data Resonance" spaces to the forefront. One signal is becoming increasingly clear: when large models truly enter factories, mines, hospitals, and urban governance, what determines whether AI can enter production systems is no longer computing power and model capabilities, but whether enterprises can continuously supply high-quality data, support models in understanding industries, and connect intelligent capabilities with real business operations.

In recent years, the parameter scale and reasoning capabilities of general-purpose large models have improved rapidly. However, when facing real industrial scenarios such as abnormal warning for refining units, optimization of automobile processes, and hospital-assisted diagnosis, they often struggle to play a greater role due to insufficient understanding of industry semantics, business rules, and scenario logic. On the other hand, the massive PB-level data accumulated in energy, manufacturing, healthcare, and other fields remains scattered across different systems, with complex formats and inconsistent standards, making it difficult for algorithms to directly understand and utilize.

What "Model-Data Resonance" points to is precisely promoting the transition of model capabilities and industrial data from relative separation to deep collaboration. KeenData has built a trinity technical architecture around "data foundation + AI platform + agent entry point," transforming the business experience, industry knowledge, and rules accumulated by enterprises over the long term into capabilities that models can understand and invoke, allowing data and models to form closer collaboration in real business scenarios.

From Data Foundation to Intelligent Foundation: Connecting Data, Models, and Agents

KeenData's core product, the KeenData Agentic Lakehouse Platform intelligent foundation, takes the "AI-in-Lakehouse" technical architecture as its core, natively integrating the lakehouse engine, multimodal computing engine, and training-inference acceleration engine. It uniformly carries the access, governance, annotation, and operation of structured and unstructured data, enabling data not only to be stored and managed, but also to continuously enter model training, inference, and agent applications.

In terms of technical accumulation and market practice, KeenData possesses more than 200 technology invention patents and ranks first in market share for advanced technology lakehouse data platforms. The platform is deeply optimized for GPUs, continuously improving computing efficiency and processing capability, and natively supports large model deployment and application, building systematic AI development and application capabilities for organizations.

From Organizational Intelligence to Industrial Intelligence: Bringing AI Data Infrastructure into Real-World Scenarios

The effectiveness of AI industrialization ultimately needs to be verified in real environments. Compared with internet scenarios, the data environments of energy, manufacturing, and government affairs are more complex: industrial production involves equipment, processes, and real-time operational data; manufacturing scenarios require connecting production processes and supply chain information; and urban governance requires connecting government, public, and industrial data. Although different scenarios appear vastly different, they point to the same question—how to make long-accumulated data truly enter AI and further transform it into AI productivity for production, operations, and governance.

KeenData continues to place AI data infrastructure into real industrial environments for verification and evolution. Centering on the intelligent needs of large organizations and regional industries, the company not only focuses on AI data foundation construction itself, but also on how data can further enter models and intelligent applications, and ultimately connect with business scenarios. In practices such as the Beijing Artificial Intelligence Pilot Zone data foundation construction, the Suzhou high-quality dataset platform, and the Hangzhou urban trusted data space, KeenData has continued to advance around urban-level data foundations, high-quality data supply, and trusted data circulation, bringing AI data infrastructure further into regional and industrial development systems.

What these practices point to is not more isolated AI projects, but a change in the underlying logic of industrial intelligence: when artificial intelligence begins to enter production, operations, and governance systems, industries need a new type of infrastructure that can continuously carry data production, trusted circulation, and intelligent applications. AI data infrastructure has thus evolved from the data foundation that previously supported digital construction into an intelligent foundation that connects data, models, and business—allowing industrial data to enter models, models to understand business, and intelligence to truly enter processes.

At present, KeenData has served more than 300 large organizations and continues to carry out industrial practices in government affairs, energy, finance, manufacturing, and other fields. From enterprise data foundation construction to regional data infrastructure and industrial intelligent applications, and then to the layout of new infrastructure for the AI era, KeenData is continuously expanding the industrial depth of AI data infrastructure.

Artificial intelligence industrialization is not a single-point technological breakthrough, but a systematic evolution around data, models, business, and industrial systems. As AI moves from "being able to answer" to "being able to produce," data infrastructure is also moving from back-end technical support to the key foundation of industrial intelligence. For KeenData, this is precisely the long-term proposition facing an "AI data infrastructure leader"—continuously transforming data into intelligence and making intelligence a true industrial productivity.