应用时代结束了 央国企都在养数字员工

This article shares the current state of AI-driven digital transformation among China's central and state-owned enterprises (CSOEs), outlines an ongoing industry paradigm shift from building one-off AI applications to nurturing "digital employees", and lays out clear, actionable implementation directions.

1. Current transformation status: By 2026, 56% of CSOEs have entered the AI pilot phase, with use cases shifting from general office productivity to core business operations. However, 78% of these firms still prioritize cost reduction and efficiency improvement as their primary goal for AI adoption. Most projects only stop at the problem identification stage and fail to form a complete closed loop for problem-solving. The overall success rate of current AI projects stands at around 70%.

2. Key implementation takeaways: Nurturing digital employees does not require top-down large-scale organizational change. Instead, companies should start with small, knowledge-intensive, highly repetitive specific tasks, gradually accumulate experience from frontline employees, validate a single use case, then expand horizontally. Budgeting logic should also shift from one-off asset purchases to ongoing operational investment, with value measured by actual output.

This article summarizes the pain points and emerging transformation paradigms for large enterprise AI implementation, offering valuable reference for brand owners advancing their own digital transformation and delivering tangible business value from AI.

1. Common pain points in current AI transformation: Most companies still limit AI adoption to cost cutting and efficiency improvement, only reaching the problem identification stage without building a business closed loop for problem-solving. Core issues include poor data quality, a shortage of cross-functional talent with both business and AI expertise, and AI architectures that are not integrated into business execution processes, failing to meet modern value creation assessment requirements.

2. Recommended transformation direction: Brands can adopt the digital employee transformation path: starting with specific, high-repeat, knowledge-intensive frontline tasks, accumulating frontline employees' business experience, replacing traditional top-down organizational change with a bottom-up rollout, and adjusting budgeting to prioritize ongoing operational investment. This approach ultimately delivers business value beyond basic cost reduction and efficiency gains, aligning with assessment criteria for technological innovation and strategic value.

This article reveals the enormous market opportunity of CSOE AI digital transformation, clarifies current industry demand characteristics, risks, and opportunities, and provides clear guidance for sellers serving the enterprise market.

1. Market opportunity: CSOE AI adoption has moved from the wait-and-see phase to large-scale pilot deployment, creating a very sizable market. According to SASAC (State-owned Assets Supervision and Administration Commission) data, total digital transformation investment by central government-owned enterprises during China's 15th Five-Year Plan period is expected to exceed 2 trillion yuan, with AI budgets accounting for 20% to 30% of total digital investment. A conservative estimate puts annual AI investment by central enterprises alone at 150 billion yuan, and demand is shifting from general office use cases to core production operations.

2. Risk and direction guidance: The core pain point for current clients is that existing AI solutions only stop at problem identification and cannot deliver a closed-loop execution implementation. Sellers need to adjust their product strategy to adapt to the new digital employee paradigm, abandon the old approach of building one-off applications, focus on clients' specific business scenarios, support ongoing operations, and align with clients' updated budget and assessment frameworks.

This article addresses the pain points of AI implementation on production factory frontlines and provides specific implementation plans, offering strong guidance and insight for factories advancing digital and AI transformation.

1. Core pain points of AI implementation in traditional production scenarios: The legacy general modeling approach has two intractable problems: first, many core process links lack sufficient data to build a modeling foundation; second, core frontline operating expertise is mostly tacit knowledge held by senior foremen, with significant outcome variation across different operators' work styles, making it difficult to standardize and accumulate experience.

2. Transformation and implementation insights: Factories can adopt a gradual ontology-based digital employee approach. Instead of building an enterprise-wide large platform from the start, organizations can start with specific production tasks, systematically accumulate each frontline worker's operating data and experience, generate multi-version solutions for decision-makers when problems arise, validate one single point scenario, then expand replication across the business. In terms of organizational rollout, no top-down forced change is required; nurturing digital employees starting from specific bottom-level tasks is sufficient.

This article clarifies the current development trends of the CSOE AI service market, core client pain points, and new solution directions, providing valuable reference for all types of digital and AI service providers adjusting their business strategies.

1. Industry development trends: CSOE AI adoption has completed the shift from wait-and-see to active implementation. By 2026, 56% of CSOEs have entered the pilot phase, with use cases shifting from general office work to core business operations. The entire industry is undergoing a paradigm shift from building one-off applications to nurturing digital employees, and the market is enormous: annual AI investment from central enterprises alone is around 150 billion yuan.

2. Core client pain points: Common problems with current AI projects include insufficient data quality and a shortage of cross-functional talent. Most projects only cover the problem identification stage and lack an action layer for business execution, failing to meet SASAC's requirements for value creation assessment.

3. Solution direction: Service providers can build their products around the digital employee paradigm, focus on specific frontline tasks, support gradual capability accumulation, align with clients' ongoing operation budgeting logic, and help clients deliver a closed-loop AI value implementation.

This article summarizes the core demands of enterprise clients for AI digital services and identifies pain points of traditional platform models, offering guidance for platform operators to adjust their operational strategies and capture market opportunities.

1. Core client demands: The traditional application-building model, which requires building an enterprise-wide large platform before identifying use cases, no longer fits new client needs. Clients now need digital employee services that align with specific frontline business scenarios, accumulate frontline tacit knowledge, support ongoing operations, and deliver closed-loop business execution, to meet SASAC's value creation assessment requirements.

2. Recommended platform optimization: Platforms can shift from the legacy approach of prioritizing platform building over scenario implementation, open up their capabilities to support small-bore frontline scenario implementation, and adapt to the分工 model where group headquarters build industry-level corpora and frontline business units build scenario-specific knowledge. Billing models should shift from one-off project fees to ongoing usage-based billing by token, aligning with clients' updated budget and governance frameworks. Platforms that highlight their ability to deliver tangible value creation will attract more client partnerships.

This paper proposes a new paradigm for AI transformation among CSOEs, summarizes the latest industry trends and core problems, and provides important empirical data and directional reference for research on AI-enabled industrial digital transformation.

1. New industry trends: CSOE AI adoption is progressing rapidly. By 2026, 56% of CSOEs have entered the pilot phase, with application scenarios shifting from general office work to core production operations. A paradigm shift is underway across the industry: moving from the legacy project-based "build application" model where delivery marks the end of work, to a capability-based "nurture digital employee" model centered on ongoing operations. The two models differ completely in terms of organizational capability requirements and budgeting logic.

2. New industry problems: Currently, 78% of CSOEs still rank cost reduction and efficiency improvement as their primary AI goal. Most projects stop at the problem identification stage and lack an execution action layer. Core pain points include insufficient data quality, a shortage of cross-functional talent, misalignment between traditional top-down organizational change paths and the new paradigm, and legacy budgeting logic that does not support ongoing operations.

3. New research directions: This paper proposes a bottom-up four-layer organizational model progressing from task to role to organization, as well as a new budgeting logic that shifts from asset purchase to ongoing operational investment, providing new directions for future related research.