> **来源：[研报客](https://pc.yanbaoke.cn)** # KPMG Global Tech Report 2026 Summary: Industrial Manufacturing ## Core Content The KPMG Global Tech Report 2026 highlights the transformative role of technology, particularly AI, in the industrial manufacturing sector. As the sector undergoes restructuring and adaptation to global dynamics, it is leveraging digital capabilities to enhance competitiveness and operational efficiency. The report emphasizes that industrial manufacturing is one of the most mature sectors in terms of network and cloud infrastructure, and many leaders are actively deploying AI to generate business value. ## Main Points - **Technology as a Key Enabler**: Industrial manufacturing is leading in embedding digital capabilities, with a strong focus on AI, cybersecurity, and data analytics. - **AI Deployment**: Nearly half of the executives (49%) report active deployment of AI use cases that are delivering business value, significantly higher than the cross-sector average of 28%. - **Value Creation**: 87% of industrial manufacturing executives believe that advanced technology will drive future competitive advantage, and 49% report significant financial gains from AI investments. - **Operational Efficiency and Risk Management**: AI and intelligent technologies are expected to drive operational efficiency, but cybersecurity remains a top concern. Enhanced cybersecurity is cited as one of the biggest benefits of technology investment (34%). - **Data Importance**: Reliable data is essential for AI success, with 83% of executives believing their data foundations are strong, but 76% also identify insufficiently reliable data as a top AI risk. - **Challenges and Strategies**: Data silos, cost pressures, and technical debt are key challenges. Organizations are focusing on upskilling the workforce and developing AI governance models. - **Government Incentives**: Initiatives like India's Budget 2026 tax incentives for datacenter and digital infrastructure are seen as catalysts for digital transformation. - **Emerging Technologies**: Digital twins, edge computing, and software-driven automation are being integrated with AI to enhance operational performance and resilience. - **Human-AI Collaboration**: The report underscores the need for cross-functional collaboration and the development of AI Champions and Ninjas to support change management. - **Strategic Partnerships**: Industrial manufacturers are forming strategic ecosystem partnerships to enhance innovation and interoperability. ## Key Findings - **AI Adoption**: 87% of industrial manufacturing executives expect to deploy AI at scale in the next 12 months. - **Centralized Governance**: 70% of organizations are taking a centralized approach to AI implementation, with IT leading the effort. - **Cross-Functional Collaboration**: 87% of leaders emphasize the importance of collaboration across IT, security, and risk teams. - **Data Governance**: 59% of respondents agree that traditional KPIs are not sufficient for tracking AI performance, highlighting the need for better data management practices. - **Cybersecurity Investment**: 48% of executives plan to increase cybersecurity budgets significantly in the next 12 months. - **Data Ontology**: This emerging concept allows businesses to create a network diagram of their data landscape, improving data accessibility and usage. - **Supply Chain Resilience**: 48% of executives identify improving data flows as the most important measure to respond to macroeconomic and geopolitical volatility. ## Implications - **AI and Data Integration**: The success of AI in industrial manufacturing hinges on the quality and reliability of data, indicating a need for robust data governance. - **Operational Resilience**: Cybersecurity is a critical enabler of operational resilience, especially in an era of heightened cyber threats. - **Strategic Alignment**: Organizations that align technology investments with clear strategic and business value are more likely to succeed. - **Workforce Adaptation**: Upskilling and engaging the workforce are essential for AI adoption and to overcome resistance to change. - **Platform-Based Scaling**: Moving from isolated AI pilots to shared platforms enables broader AI deployment and scalability across the enterprise. ## Actions for Success 1. **Focus on Data Foundations**: Standardize, connect, and govern OT/IT data to support AI initiatives and ensure data quality. 2. **Design for Human-AI Collaboration**: Upskill employees and redesign roles to enable effective collaboration between humans and AI systems. 3. **Apply AI to High-Value Use Cases**: Prioritize proven applications such as predictive maintenance, quality optimization, and supply chain resilience. 4. **Scale AI via Platforms**: Transition from isolated pilots to shared platforms like Databricks or Azure Fabric for broader deployment. 5. **Put Security at the Heart**: Integrate security and ethics into AI development and ensure rigorous testing before deployment. 6. **Drive Strategic Ecosystem Partnerships**: Build partnerships that foster innovation, flexibility, and better customer outcomes. 7. **Upskill the Workforce**: Ensure employees are equipped with the skills to work effectively with AI, including the creation of AI Champions and Ninjas. ## Conclusion Industrial manufacturing is at the forefront of technological transformation, with a clear focus on AI, data governance, and cybersecurity. The sector is actively investing in these areas to drive operational efficiency, competitive advantage, and resilience in a volatile global environment. As the industry moves toward the Intelligence Age, the ability to integrate AI with other emerging technologies and to manage the associated risks will be crucial for long-term success.