Building Platforms, Selecting Scenarios, and Pooling Capabilities: Schneider Electric's Xiong Yi Interprets the Three Principles for Enterprises to Break Through AI Challenges
2026-05-13

The 2026 Government Work Report clearly proposed creating a new form of intelligent economy, officially making artificial intelligence the core engine driving the high-end and intelligent upgrading of industries. Currently, the scale of my country's core AI industry has exceeded 1.2 trillion yuan, and it is expected that by the end of the 15th Five-Year Plan, the scale of related industries will reach the 10 trillion yuan level. AI's comprehensive penetration into all industries has become an inevitable trend. However, amidst this fervor, many enterprises are deeply mired in transformation confusion, with over half of central and state-owned enterprises having unclear strategic positioning, and less than 30% of AI investment truly translating into business growth. Faced with the industry dilemmas of difficult implementation, weak value, and rapid iteration, Xiong Yi, Senior Vice President of Schneider Electric, proposed the three principles of building platforms, selecting scenarios, and pooling capabilities, pointing out a feasible, replicable, and sustainable path to break through challenges in enterprise AI transformation.

I. The Real Dilemma Under the AI Wave: Misalignment Between Technological Speed and Value Implementation

Artificial intelligence technology is iterating rapidly, but enterprises generally face a double dilemma in its implementation. On the one hand, there is a mismatch between technological iteration and project pace. Traditional enterprise digitalization projects often have cycles measured in years, while AI technology updates rapidly on a weekly or monthly basis. Outdated construction models cannot keep up with the speed of technological evolution, resulting in an awkward situation where technology is ahead of its time but implementation lags behind. On the other hand, there is an imbalance between micro-level efficiency gains and macro-level fluctuations. Efficiency improvements achieved by enterprises through lean production and AI applications are easily offset by external factors such as market fluctuations and supply chain instability. Uncertain returns on investment and unpredictable transformation risks lead many enterprises to adopt a wait-and-see attitude towards AI deployment. AI is no longer a simple technology selection, but a systemic test concerning corporate strategic resolve and long-term development.

II. Core Logic of AI Transformation: Three-Dimensional Principles to Overcome Implementation Bottlenecks

Faced with widespread transformation anxiety, Xiong Yi offers a clear solution: for enterprises to truly master AI and transform technology into productivity, they must adhere to three core principles: building platforms, selecting scenarios, and pooling capabilities. First, build platforms to construct a unified AI technology foundation for the enterprise. Moving beyond fragmented, isolated pilot projects, a unified and scalable data and AI technology platform should be built to achieve data integration, model consolidation, and capability reuse, laying the foundation for subsequent large-scale replication and avoiding redundant construction and resource waste. Second, select scenarios and focus on precise entry points with quantifiable value. Avoid technology for technology's sake; prioritize core business pain points such as production control, quality optimization, energy management, and supply chain scheduling. Select scenarios with controllable investment, quantifiable results, and replicable models for pilot projects, using these pilots to expand the reach and make the value of AI visible, quantifiable, and easily scalable. Third, aggregate capabilities to create a long-term, sustainable organizational ecosystem. AI transformation is not a one-off project but a long-term capability building process. Enterprises must ensure data governance and the assetization of experience, while cultivating AI literacy among all employees and collaborating with industry chain partners to build an innovation ecosystem, forming an internal driving force for self-iteration and continuous upgrading.

III. Platform First: Building a Solid Foundation for Intelligentization with a Unified Architecture

As a global leader in energy technology, Schneider Electric has pioneered the integration of the three principles into its digital practices. Leveraging the mature EcoStruxure global architecture, Schneider Electric has built the DataCube unified data platform, connecting the equipment adaptation layer, control and operation layer, and management optimization layer. This achieves unified data across the entire domain and system interoperability, truly realizing the integration of energy and industrial intelligence. Based on this platform system, Schneider Electric has implemented hundreds of AI engine projects in its supply chain, achieving intelligent optimization of the entire process of planning, scheduling, and execution. Its Shanghai Putuo factory, relying on AI, has completed prototype development, flexible production, and intelligent operation and maintenance upgrades, increasing per capita production efficiency by 82% and shortening the R&D cycle by 63%. It was successfully recognized as a World Economic Forum end-to-end lighthouse factory, setting a benchmark for the platform-based construction of AI in industrial enterprises.

IV. Deep Scenario Cultivation: Anchoring Real Business to Achieve Quantifiable Value

AI divorced from business scenarios is meaningless. Schneider Electric insists on deeply embedding algorithms and hardware/software solutions into real industry scenarios, covering core areas such as industrial manufacturing, building infrastructure, water treatment, and data centers. Implementation projects generally achieve 10%-20% energy efficiency improvements and cost optimizations. In the food and beverage industry, AI has been used to optimize filtration processes for leading beer companies, replacing traditional manual experience in controlling diatomaceous earth dosage. This has improved filtration efficiency by approximately 20% and reduced material loss by 15%. This mature solution has been rapidly replicated and promoted in multiple factories. In the data center sector, AI algorithms are used to precisely control power distribution and cooling loads, and intelligent computing infrastructure supports the development of the AI industry itself, achieving both green energy saving and stable operation.

V. Capability Accumulation: Talent and Ecosystem Drive Long-Term Growth

The ultimate competition in AI transformation is the competition of organizational capabilities and ecosystem strength. Schneider Electric focuses on both talent development and industrial ecosystem to build a sustainable innovation system. The Wuhan factory, serving as a talent "lighthouse factory," has established a full-cycle employee growth system, cultivating a large number of well-rounded talents who understand both business and AI through real-world AI project practice. Simultaneously, relying on the "Innovation and Success Plan," the company has created the AI+ Exploration Camp, attracting over 1,300 innovative companies to participate over six years, with over 90 science and technology innovation companies joining the camp for co-creation, resulting in over 40 joint innovation achievements. This open ecosystem promotes the deep integration of AI technology and the real economy. There are three key indicators of truly effective AI implementation: moving from single-point pilot projects to platform-based replication; establishing a clear and quantifiable ROI system; and shifting from top-down implementation to spontaneous employee adoption. These are also important benchmarks for the maturity of enterprise intelligence.

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Building Platforms, Selecting Scenarios, and Pooling Capabilities: Schneider Electric's Xiong Yi Interprets the Three Principles for Enterprises to Break Through AI Challenges2026-05-13

Building Platforms, Selecting Scenarios, and Pooling Capabilities: Schneider Electric's Xiong Yi Interprets the Three Principles for Enterprises to Break Through AI Challenges
2026-05-13