China's MIIT Releases Industrial AI Navigation Map Covering 20 Subsectors and Over 1,000 Scenarios
nashnova research
China's MIIT on Oct 15 published an industrial-AI roadmap spanning 20 sectors and over 1,000 standardized scenarios, backed by a ¥60 billion dedicated fund; this marks the shift from policy encouragement to a concrete, sector-by-sector deployment playbook.
What does this "roadmap" actually map?
The roadmap was jointly compiled by leading research institutes and industrial firms, grounded in real production data. It covers petrochemicals, steel, automotive, consumer electronics, and 20 sectors in total, cataloguing over 1,000 standardized AI use cases ready for deployment.
This means → companies no longer need to figure out "where can AI help?" from scratch — they get a ready-made checklist of scenarios to match against their own operations.
In plain terms = the government has moved from "you should use AI" to "here's exactly which process step, and how."
Front end, mid end, back end — what does AI handle at each stage?
Front-end R&D: intelligent parameter simulation, automated structural optimization, and process pre-emulation — letting AI run the experiment before committing physical resources. Best suited to high-end equipment, automotive, and aerospace where R&D costs are high and test cycles are long.
Mid-end manufacturing: smart scheduling, closed-loop process control, and flexible line dispatch — enabling production lines to switch between product types in small, customized batches.
Back-end operations: machine-vision quality inspection, predictive maintenance — catching equipment faults before they happen — plus energy optimization, safety-risk detection, and intelligent materials matching across the supply chain.
This means → the roadmap is not a single-point tool; it covers the entire chain from R&D to finished goods.
How large is the supporting infrastructure?
MIIT has matched thousands of manufacturing firms with AI technology providers, designated 18 national AI innovation pilot zones, and implemented nearly 200 industrial-AI standards.
On the funding side, a ¥60 billion national AI-industry fund is in place, creating a four-pillar support system: policy, capital, pilot zones, and standards.
In plain terms = this is not just a published document — the money, the standards, the pilot sites, and the matchmaking are all already operational.
Adoption is already significant — so where's the bottleneck?
As of H1 2026, AI adoption among large-scale manufacturers tops 30%; among "lighthouse" factories, AI penetration exceeds 70%. Over 56,000 basic-level smart factories have been built nationwide.
Yet industry experts assess that most firms remain at stage two — deploying AI in isolated steps like logistics or quality inspection. Very few have reached stage three, where AI reshapes the entire production workflow end to end.
This reflects a critical gap: point-solution AI is easy; threading AI across an entire production line is hard. By 2027, as "model-data resonance" and "AI + manufacturing" action plans roll out, more companies are expected to attempt the jump from single-point to full-process intelligence.
How do institutions frame the investment opportunity?
Morgan Stanley projects a capex super-cycle for Chinese manufacturing from 2026 to 2035, with Industry 5.0 transformation driving roughly $12 trillion in incremental investment — split into new industrial infrastructure (~$0.5 trillion), smart retrofitting of existing factories (~$5.5 trillion), and strategic emerging capacity (~$6.0 trillion).
Huatai Securities identifies three investment threads: ① mid-to-high-end manufacturing going overseas (track comparative advantage and market share); ② supply-chain reinforcement and import substitution (track domestic-sourcing ratios); ③ forward positioning in emerging and future industries (track penetration rates). Sectors flagged include electronics, machinery, software, automotive, servers, medical devices, chemicals, and rare earths.
This means → capital is not making a blanket "bet on AI" — it is entering in layers along a infrastructure → retrofit → new capacity structure.
Which Hong Kong-listed names tie directly to this roadmap?
ZTE Corp (00763): provides full-stack industrial intelligence capabilities built on 5G-A, AI, and industrial-internet integration.
AInnovation (02121): its "AInnoGC" industrial large model has over 15 billion parameters and delivers purpose-built large-model services for vertical industries.
Luxshare Precision (02475): operates extensive digitized smart factories spanning consumer electronics, automotive electronics, AI servers, and robotics components, with end-to-end smart manufacturing line-building capability.
In plain terms = whether the roadmap can push industrial AI from point-solution penetration to full-process transformation is the key validation checkpoint for these investment theses.
市场有风险，内容仅供研究参考，不构成投资建议。