Shanghai AI Manufacturing Action 2026

Policy signal: AI is moving from demonstration projects to production system capacity

The Shanghai City Economics and Information Technology Commission (SECI) issued measures in July 2026 to further promote “AI+ manufacturing” with a focus on industrial niche models, industrial intelligence, physical AI, industrial software, and industrial Internet. The National Initiative on “Advanced Intelligence+ Manufacturing” also explicitly promotes the use of the principles of field-drivenness, safety and industrial intelligence in the development, testing, production, quality control, transport, supply chain and management of operations.

This means that the manufacturing firm's AI project cannot be targeted solely at “finishing a question and answer assistant.” A more valuable way to build is to embed AI in the production business closed loop, so that it can read credible data, understand business constraints, make recommendations, trigger controlled actions, and continuously optimize through feedback on results.

• Research and development links: knowledge retrieval, design aids, document and regulatory inspection
• Production chain: Emission support, process analysis, abnormal diagnosis and quality prediction
• Equipment chain: smart inspection, fault identification, predictive maintenance, synergy with worksheets
• Business chain: order analysis, inventory warning, supply chain synergy and business forecasting

Not from the model capacity, from the production bottlenecks.

The high value of manufacturing sites is often hidden in delivery cycles, good rates, downtime, inventory, energy consumption, and manual experience. Enterprises should first identify bottlenecks that affect their business outcomes, and then judge whether AI can improve this bottleneck by identifying, predicting, retrieving, generating or organizing their tasks.

The scenarios suitable for the first pilots usually have four conditions: high frequency of operations, availability of historical data, manual judgement rules that can be described and results quantified. For scenes where data are scarce, high risk of liability or processes are still subject to frequent changes, digital remedial courses or complementary decision-making models should be used.

• Change the words “building industrial intelligence” to a clear operational indicator
• Record current baselines, such as average stoppage time, mass check time and abnormal closed cycle
• Distinguishing the recommended, approved and self-implementing scenarios
• Priority for entry points within 90 days to complete the closed loop verification

The base of industrial intelligence is data, knowledge and IT/OT connections.

The information is often spread among PLCs, SCADA, MES, ERPs, QMS, WMS, documentation systems and individuals’ experience. Without a uniform data semantics and interfaces, the intelligence can only stay in isolation.

knowledge base needs to maintain document versions, applicable production lines and approval status, while real-time data takes into account time series, delay, missing and abnormal values.

• Harmonization of key codes for equipment, materials, worksheets, process and quality issues
• Designing different access methods for real-time data, business data and documentation knowledge
• Inheriting authority from the original system to AI application and avoiding overstepping roles
• Establish data bloodlines, update times and response basis demonstration mechanisms

Designing intelligence as a controlled collaborator, not automating the black box.

The smarts can analyze alarms, search protocols, generate disposal recommendations or create worksheets, but when it comes to modifying process parameters, stopping equipment, adjusting discharges and releasing quality results, there is a need for clear authorization levels and artificial confirmation points.

It is recommended that capacity be classified into read-only queries, supporting recommendations, controlled execution and high-risk prohibition levels four. Each tool call is recorded by the originator, input, basis, action, outcome and manual confirmation, and can be reversed to manual processes if failure occurs.

Pilot validation of value with small closed loops, copying to the line and plant

The pilot should not be a function demonstration, but rather a validation using real shifts, real worksheets and real anomalies.

When a scenario reaches the predefined threshold, the sediment data access, permission, assessment, monitoring and release template is extended to adjacent equipment, products or plants. Platformization should take place after replicable models are validated, rather than before the first scene.

• Week 1-2: On-site interviews, process observations and baseline measurements
• Weeks 3-6: data access, prototype development and offline assessment
• Weeks 7-10: small-scale online, human synergies and risk observation
• Weeks 11-12: Business Breager, Receiving and Inspection and Reproduction Decision-Making

Project acceptance and acceptance is based on operations, technology, safety and operations at the same time

The acceptance and acceptance of the industrial AI project cannot be limited to a list of pages and functions. The operational side needs to confirm whether the indicators have improved, the technical side needs to identify performance, stability and interface quality, the security side needs to identify the authority, audit and emergency mechanisms, and the operational side needs to identify those responsible for updating knowledge, modelling and questions.

For externally executed or FDE collaborative projects, scenarios, data dictionary, interface files, assessment collections, test reports, competency matrices, deployment manuals, monitoring rules, training materials and iterative lists should also be delivered to avoid system failure to maintain them.