Industry Scan: Zhaoshi Intelligent Agent — Three Pathways and Real Cost-Reduction Data in Government-Enterprise Deployment

2026, AI Agent deployment in government and enterprise is transitioning from proof-of-concept to scale. Unlike consumer "AI write articles" or "AI generate images," government-enterprise scenarios demand AI that genuinely replaces human labor across complete business processes — high data sensitivity, strict compliance, long-chain workflows, and extreme usage frequency.

Pathway 1: Non-Invasive Integration — AI Without Replacing Systems

Government-enterprise's core challenge: existing systems are complex with heavy historical baggage — system replacement is often impossible. Zhaoshi Intelligent's core technology, ISSUT (Intelligent Screen Semantic Understanding), uses visual perception to directly understand UI elements on screen, enabling the Agent to operate any software like a human — including legacy systems without API interfaces.

Wuxi's "Xixin Service" government Agent matrix: built on Wuxi government's "Zhihui" government model, integrating Education Bureau, Human Resources, and multiple department data, covering policy consultation, process guidance, material pre-review, and progress tracking. 10.2M+ knowledge entries connected. Result: citizen wait times reduced 60%, window staff repetitive Q&A workload reduced 40%.

Hegang Group AI quality inspection: AI model optimized steelmaking process, dynamically adjusting ingredients and temperature, automation rate from 55% to 92%, per-capita steel output up 18%, annual cost savings exceeding 10M RMB.

Pathway 2: From Single-Point Automation to Full-Chain Closed Loop

Traditional AI: identify invoice → extract info → human judgment → approval Zhaoshi Agent: identify invoice → extract info → automatically assess compliance → generate approval opinion → trigger relevant systems → record audit logs → notify stakeholders

No human intervention across the full workflow, powered by MemPO (Self-Memory Policy Optimization) — maintaining "memory" across long-chain tasks. One chain enterprise HR platform: approval efficiency up 80%, compliance violations down 95%.

Pathway 3: Quantified Commitments — Making AI Value Measurable

Government-enterprise procurement's core question isn't "how advanced is the technology" but "how much money does it save, how much efficiency does it gain?" Zhaoshi's approach: let real data speak. The Hegang case isn't isolated — official cases show specific metrics: one chain enterprise approval efficiency +80%, e-commerce live-streaming operational efficiency +3x (Maidian AI Agent), advertising ROI +25%, live-streaming labor cost per session -40%.

Why Government-Enterprise AI Agent Deployment Outpaces Expectations

Three structural reasons: genuine and urgent pain points (high labor costs, repetitive work, strict compliance — problems existing for over a decade that AI Agent is the first tool to systematically solve); domestic LLM capability reaching thresholds (DeepSeek, Qwen, ERNIE capabilities supporting complex business scenarios while local deployment satisfies data sovereignty — technical maturity and compliance requirements met simultaneously for the first time); and demonstration effects driving rapid follow-through (Wuxi, ICBC, Hegang benchmark cases generating accelerating follow-on adoption in same industries and regions).