Huawei’s Strategic Path to Advance Industrial Intelligence and Maximize AI Potential: Three Key Steps

October 3, 2025

Huawei Outlines Three-Step ‘ACT‘ Pathway for Prosperous AI Integration

Leo Chen, Huawei’s Senior Vice President and president of Enterprise Sales.

Shanghai – The question is no longer whether organizations should adopt Artificial Intelligence, but how to do so effectively, according to Leo Chen, Senior Vice President and President of Enterprise Sales at Huawei. Chen articulated this point during his keynote address, titled “Breaking New Ground to Industrial Intelligence,” at HUAWEI CONNECT 2025, emphasizing the transformative power of ai in bolstering industrial efficiency.

He identified three key challenges confronting businesses as they explore Ai implementation: demonstrating a return on investment, utilizing proprietary data for competitive advantages, and broadening Ai applications beyond initial pilot programs.According to a recent Gartner report, over 40% of Ai projects fail to reach the production stage due to scalability issues.

Huawei’s ‘ACT’ Framework for ai Transformation

to address this, Huawei introduced its new three-step “ACT” pathway – Assess, Calibrate, Transform – designed to guide organizations through a complete Ai-driven transformation. The framework focuses on pinpointing high-impact Ai applications, refining models with sector-specific data, and ultimately, implementing scalable Ai solutions. Alongside the framework, Huawei unveiled nine new industrial intelligence solutions developed in collaboration with its partner network.

“Real progress stems from practical application,” Chen stated, before detailing successful Ai implementations across multiple industries. These case studies illustrate the potential of Ai when thoughtfully integrated into existing workflows.

case Study: Clever Power Line Inspections

China Southern Power Grid, responsible for a vast power transmission network, sought a solution to enhance defect detection in its infrastructure. Collaborating with Huawei, they developed “MegaWatt,” an Ai model meticulously tailored for the energy sector. Built on Huawei’s Ascend computing platform and MindSpore Ai framework, MegaWatt combines computer vision with natural language processing to automate the identification of defects.

The project emphasized rigorous data management,encompassing data cleansing,processing,labeling,and optimization. Huawei’s optimized operators accelerated model training and significantly improved accuracy. Utilizing Ascend’s MoE Expert Parallelism Cluster, throughput per card increased by 3.3x when compared to conventional methods, while also reducing inference latency. Post-implementation, MegaWatt dramatically increased defect recognition efficiency-a fivefold betterment-and achieved over 90% accuracy, fully automating the process from image analysis to report generation.

Case study: Streamlining Medical Recordkeeping with Ai

West China Hospital recognized the importance of streamlined and accurate medical recordkeeping for superior patient care. Partnering with Runda, Huawei introduced an Ai-powered system built upon the ascend platform, integrating both open-source models and specialized models designed for clinical language understanding.

This system automatically transcribes and summarizes doctor-patient conversations, accurately interprets diagnoses, and generates comprehensive hospital records. Integrated with pre-diagnosis and quality control Ai agents, the system ensures both efficiency and accuracy. Doctors can now finalize records with minimal edits-no more than four-and submit them to the hospital information system in seconds, drastically reducing documentation time.

Five Key Findings for Industrial Intelligence Success

Based on these and other deployments,Huawei identified five pivotal elements for successful Ai transformation:

1. Prioritize the selection of impactful implementation scenarios.
2. Leverage high-quality, industry-specific data for effective modeling.
3. Prepare for a rapidly growing need for large-scale inference capabilities.
4. Embrace human-Ai collaboration as a new operational paradigm.
5. Implement robust governance and risk management protocols.

Huawei is actively supporting enterprises with its ACT pathway, aiding them in determining valuable Ai opportunities. The company’s comprehensive toolchain and security measures facilitate the creation of specialized models. Its Versatile platform simplifies agent deployment and workflow automation, while continuous investment in AI-optimized ICT infrastructure ensures support throughout the entire Ai lifecycle.

Furthermore, Huawei is fostering a thriving partner ecosystem, currently comprising over 6,300 Kunpeng partners and 2,700 Ascend partners. The nine new solutions debuted at the Shanghai event address diverse sectors-including city management, healthcare, finance, manufacturing, logistics, energy, and steel production-driving the next wave of industrial intelligence.

Chen concluded, “Our collective approach to the changes brought about by Ai will define its ultimate ability to deliver genuine value and translate technological advances into real-world benefits.”

For more information,visit https://e.huawei.com/en/

Key Framework Component	Description
Assess	Identify high-value Ai application areas within the institution.
Calibrate	refine Ai models using specialized industry data for improved accuracy.
transform	Implement scalable Ai agents to reshape operational processes.

Did You Know? The global Ai market is projected to reach $1.84 trillion by 2030, growing at a compound annual growth rate (CAGR) of 38.1% from 2023 to 2030.

Pro Tip: Before investing in Ai, conduct a thorough assessment of your existing data infrastructure to ensure it can support the demands of Ai models.

What challenges does your organization face in adopting Ai? How can businesses best prepare for the future of work in an Ai-driven world?

Understanding the Long-Term Implications of ai

The successful integration of Ai requires a shift in organizational mindset, from viewing Ai as a technological add-on to embracing it as a core component of business strategy. Ongoing investment in data governance, talent development, and ethical considerations will be crucial for maximizing the long-term benefits of Ai.

Frequently Asked Questions About Ai Implementation

• What is the biggest hurdle to Ai adoption? the primary barrier is often a lack of high-quality, labelled data and the expertise to effectively manage it.
• How can Ai improve business efficiency? Ai can automate repetitive tasks, improve decision-making, and personalize customer experiences.
• What is Huawei’s role in the Ai landscape? Huawei provides the infrastructure, tools, and expertise to support organizations through their Ai journey.
• What is the ‘ACT’ pathway? The “ACT” pathway (Assess, Calibrate, Transform) is Huawei’s framework for guiding enterprises through a successful Ai transformation.
• Is ai a threat to jobs? While Ai may automate certain roles, it is also expected to create new opportunities that require uniquely human skills.
• How important is data governance in Ai projects? Data governance is absolutely critical to ensure ai models are accurate, reliable, and ethically sound.
• What are the benefits of human-Ai collaboration? Combining human intelligence with Ai’s analytical capabilities leads to better decision-making and innovative solutions.

Share your thoughts on the future of Ai in the comments below!

What are the key components of Huawei’s full-stack AI approach?

Building the Foundation: Full-Stack AI Capabilities

Huawei’s ambition isn’t simply to use Artificial Intelligence (AI); it’s to become a leading provider of the entire AI stack – from the chips that power it, to the platforms it runs on, and the applications it enables. This full-stack approach is the cornerstone of their industrial intelligence strategy. It’s a purposeful move to control the value chain and offer end-to-end solutions, particularly crucial in the context of increasing geopolitical complexities surrounding technology supply chains.

* Hardware Innovation: Huawei’s Kirin chips, despite facing challenges, demonstrate a commitment to in-house silicon design optimized for AI workloads. The development of dedicated Neural Processing Units (NPUs) within these chips substantially accelerates AI processing,reducing latency and power consumption. This is vital for edge computing applications in industrial settings.

* Software Platforms – MindSpore: Huawei’s open-source AI framework, MindSpore, is a critical component. Unlike some competitors, MindSpore is designed with a focus on simplicity and efficiency, aiming to lower the barrier to entry for developers. It supports various programming paradigms, including imperative and declarative, offering versatility for different AI tasks. MindSpore’s all-scenario capabilities – cloud, edge, and device – are particularly relevant for industrial deployments.

* Cloud Infrastructure – modelarts: Huawei Cloud’s ModelArts platform provides a comprehensive suite of tools for the entire AI lifecycle – data preprocessing, model training, deployment, and management. This integrated habitat streamlines the development and deployment of AI applications,accelerating time-to-market for industrial solutions.

Step One: Deepening AI Integration in Core Industries

huawei isn’t pursuing AI for AI’s sake. Their strategy centers on applying AI to solve specific challenges within key industries. This targeted approach maximizes ROI and demonstrates tangible value to potential customers. Focus areas include:

* Manufacturing: Predictive maintenance using AI-powered anomaly detection,quality inspection with computer vision,and robotic process automation (RPA) are key applications. Huawei has partnered with manufacturers to implement these solutions, improving efficiency and reducing downtime.

* Energy: AI is being used to optimize energy consumption, predict equipment failures in power plants, and improve grid management. Smart grids leveraging AI are becoming increasingly prevalent.

* Healthcare: AI-assisted diagnostics, drug discovery, and personalized medicine are areas of active development. Huawei is collaborating with hospitals and research institutions to explore these applications.

* Transportation: Autonomous driving, intelligent traffic management, and logistics optimization are all benefiting from AI advancements.Huawei’s work in 5G technology is crucial for enabling these applications.

Real-World Example: In 2023,Huawei collaborated with a major automotive manufacturer to implement an AI-powered quality control system on the production line. This system,utilizing high-resolution cameras and deep learning algorithms,reduced defect rates by 30% and significantly improved production efficiency.

step Two: Cultivating a Robust AI Ecosystem

Recognizing that no single company can dominate the AI landscape, Huawei is actively building a thriving ecosystem of partners. This involves:

* Developer Programs: The HUAWEI Developers program (developer.huawei.com/consumer/fr/) provides developers with access to tools, resources, and support to build AI-powered applications on Huawei’s platforms. This fosters innovation and expands the range of available solutions.

* Strategic Partnerships: Collaborations with universities, research institutions, and other technology companies are crucial for driving AI research and development. Huawei is investing heavily in joint research projects and open-source initiatives.

* investment in Startups: Huawei is actively investing in AI startups, providing them with funding, mentorship, and access to its resources. This helps to accelerate the development of innovative AI technologies.

* Open-Source Contributions: Contributing to open-source projects like MindSpore demonstrates Huawei’s commitment to collaboration and fosters a wider community of developers.

Step Three: Pioneering AI-Driven innovation – Edge AI and Beyond

huawei is pushing the boundaries of AI by focusing on emerging technologies like Edge AI and exploring new applications of AI in areas like robotics and digital twins.

* Edge AI: Processing AI workloads closer to the data source – on devices or at the edge of the network – reduces latency, improves security, and enables real-time decision-making.Huawei’s NPUs and MindSpore’s edge capabilities are key enablers of Edge AI.This is particularly crucial for industrial applications where low latency is critical.

* Digital Twins: creating virtual replicas of physical assets – factories, power plants, cities – allows for simulation, optimization, and predictive maintenance.AI plays a crucial role in analyzing data from these digital twins and providing actionable insights.

* AI-Powered Robotics: Integrating AI with robotics enables robots to perform more complex tasks, adapt to changing environments, and collaborate with humans. Huawei is developing AI algorithms for robot vision, navigation, and manipulation.

* Generative AI: While a relatively newer focus, Huawei is actively exploring the potential of generative AI models for industrial applications, such as design optimization and content creation.

Benefits of Huawei’s Approach:

* Reduced Costs: AI-driven automation and optimization can significantly reduce operational costs.

* **Increased Efficiency