On August 27, during the 2026 China International Big Data Industry Expo, the "Digital-Intelligence Public Governance and Public Resource Trading Big Data Exchange" was held at the Guiyang International Eco-Conference Center. Guo Zhenqiang, Vice President of KeenData, was invited to deliver a keynote speech at the afternoon session. Under the title "Data Reconstruction: The New Infrastructure for the Age of AI Industrialization — From Model Hype to Industrial Intelligence, From Data Resources to Intelligent Production Capacity," he systematically articulated the pathways and methodologies for "data reconstruction" in the era of AI industrialization. The session drew a full house.

Guo Zhenqiang noted that the first half of AI has been about model breakthroughs, while the second half is about systemic industrial transformation. The current global competition in AI is increasingly defined by one question: who can turn AI into industrial productivity.

He further emphasized that model capabilities are rapidly becoming commoditized, but industrial value does not materialize automatically — data, knowledge, processes, and actions have yet to form a closed loop. This is precisely the "industrialization gap" standing between models and industry. What truly determines whether AI value is realized is whether organizations can complete data reconstruction and forge the new infrastructure needed to support AI industrialization.

Guo Zhenqiang proposed a systematic methodology for "data reconstruction": transforming data from passive "business records" into active "intelligent production assets." In the past, data documented the world; today, data trains models; in the future, data will drive industrial action.

First Layer: From Data Availability to AI-Ready Data Supply. Having data does not mean being ready for AI. Raw data suffers from unstable quality, inconsistent metrics, unstructured data governance challenges, and a lack of feedback loops from real-world scenarios — all of which stand as practical barriers before model training can even begin. Through KeenData Lakehouse and its high-quality dataset capabilities, KeenData builds "AI-ready data capabilities," addressing the fundamental question of how data becomes fuel for models.

Second Layer: From Data Assets to Industrial Cognition. The key to industrial intelligence is not simply feeding data to models, but enabling models to understand industries. Guo Zhenqiang illustrated this with examples from medical diagnosis pathways, energy equipment operating conditions, manufacturing process supply chains, government regulatory logic, and even the specific semantic rules of tender evaluation. He proposed a three-level evolution — data cognition, business cognition, and action cognition — and through semantic governance, knowledge engineering, enterprise cognitive models, and industry knowledge bases, builds the "cognitive operating system" for each industry.

Third Layer: From Intelligent Q&A to Agent-Driven Industrial Action. The end goal of AI industrialization is not Q&A, but a closed action loop. Through KeenClaw, agents move beyond the chat window and into core business processes, connecting enterprise business systems, data systems, knowledge bases, and approval workflows, while also linking data providers, model providers, scenario owners, and operators across the industrial chain. Only by forming a complete "data-model-agent-process-feedback" closed loop can AI evolve from a capability showcase into genuine industrial productivity.

Notably, throughout the Expo, the KeenData booth maintained strong foot traffic, drawing numerous clients for on-site consultations and partnership discussions — making it one of the busiest booths at the event. Clients and partners from public resource trading, government, energy, finance, manufacturing, and other sectors engaged in in-depth conversations with the KeenData team on topics including AI data infrastructure, high-quality datasets, and agent deployment. Many visitors expressed interest in further collaboration on the spot.

KeenData: Moving in Step with the Times, Growing with Industry. In the age of AI industrialization, KeenData is leveraging AI data infrastructure as a lever for reconstruction, helping more industries transform data into intelligence and intelligence into productivity.