Today, the world is undergoing profound transformation which is jointly driven by geopolitical restructuring and the revolution of artificial intelligence technologies. Globalization is evolving toward regionalization, supply chains are accelerating localization, and artificial intelligence is rapidly advancing from a frontier technology to a core engine of productivity. Research from McKinsey indicates that generative AI is expected to contribute approximately 7 trillion US dollars to the global economy, with China projected to contribute about 2 trillion US dollars, which approaches one-third of the global total.

IDC’s forecasts further confirm this trajectory, projecting that by 2028, total global IT investment in the AI sector will reach 815.9 billion US dollars, while China’s total AI investment will exceed 100 billion US dollars, with a five-year compound annual growth rate of 35.2 percent. This trend demonstrates that the AI revolution is not merely a technological upgrade but a profound structural shift which reshapes the global economic growth model.

In this wave of transformation, organizations of all types, whether enterprises or government bodies, are facing both major challenges and significant opportunities. Research by Accenture shows that Chinese enterprises are undergoing a “compressed transformation”, with shorter transformation windows, greater pressure and more numerous challenges. Traditional and isolated data systems, such as data warehouses or fragmented data platforms, can no longer meet the requirements of AI applications for real-time performance, multimodal data processing and highly elastic computing power. Enterprises urgently need to break down data silos and address pain points such as insufficient high quality data supply, the disconnect between models and business scenarios, and the growing burden of data security and compliance. These challenges are now forcing data infrastructure to undergo a paradigm shift, and they are pushing it to evolve from single purpose data storage and analytics tools into an intelligent foundation which supports end to end business processes. For data platform vendors, the key to seizing these opportunities and standing out in the next round of competition is to build a new generation of data infrastructure whose core capabilities lie in the following aspects:

• First, it must treat data production, data governance and business side AI model applications as a single dynamic and continuous production process, which meets business requirements for real time responsiveness and operational continuity.
• Second, it must move beyond single scenario agent models and avoid repeatedly creating new “data stovepipes”. Gartner forecasts that by 2028 at least 15 percent of day to day decisions will be made autonomously by agentic AI, which requires data infrastructure that supports cross scenario and large scale deployment of intelligent agents.
• Third, it must establish an integrated data infrastructure which combines centralized governance with decentralized enablement, so that data assets are governed in a unified, secure and efficient way, while business units across the organization are empowered in a distributed way to flexibly access data and innovate.

At the technical level, the core pathway for next-generation data infrastructure lies in the deep integration of the lakehouse architecture and AI-native principles. The lakehouse model combines the openness and flexibility of data lakes with the structured management and ACID transactional capabilities of data warehouses, providing a unified data foundation. AI-native design enables tight coupling between data, model training, deployment and governance, opening the full chain from data to intelligence. This is not merely an architectural integration but a conceptual shift which drives the true convergence of Data and AI.

The integration of Data and AI will elevate the realization of data value from points (efficiency improvement), to lines (organizational innovation), to surfaces (industry-level collaboration) and ultimately to systems (industry-wide transformation), becoming a national strategic engine for an intelligent society.

To explore the latest trends in Data and AI integration, the Jazzyear Institute conducted this research to systematically examine new trends, new definitions, new paradigms and new application practices in China’s Data and AI infrastructure landscape. The goal is to provide forward-looking insights and practical guidance for large organizations deploying Data and AI infrastructure.

Core Insights of the Report

• Data applications and artificial intelligence, once independent, are steadily converging toward full integration.
• The convergence of data applications and AI is driving stepwise evolution in architectures and capabilities.
• Data infrastructure is becoming the “core productive tool” that sustains data as a production factor and supports continuous intelligent transformation.
• The value chain of Data and AI infrastructure follows a progressive pattern from point → line → surface → system.
• The five key capabilities of Data and AI infrastructure are integrated development, platform architecture, resource scheduling, intelligent nativity and secure operations.

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