技术趋势2026：AI从概念验证迈向价值创造-德勤

Core Insights - The core focus of the report is the transition of AI development from experimental phases to scalable value creation, emphasizing the need for businesses to restructure processes and strategies for competitive differentiation [1][14][25]. Group 1: Innovation and Value Creation - The report highlights a compound effect of innovation, where advancements in technology, data, investment, and infrastructure create a self-reinforcing cycle, accelerating growth and necessitating a shift from mere automation to comprehensive business process redesign [15][25]. - Generative AI has seen rapid adoption, reaching 100 million users in just two months, showcasing the exponential growth potential of AI technologies [15][25]. Group 2: Physical AI and Robotics - Physical AI is transforming robotics, enabling machines to operate autonomously in complex environments, with applications in warehousing, manufacturing, and autonomous driving [16][30]. - By 2035, it is projected that 2 million humanoid robots will be deployed in workplaces, although challenges such as training gaps and cybersecurity risks remain [16][30]. Group 3: Digital Workforce and AI Agents - There is a significant gap in the application of digital employees (AI agents), with only 11% of companies implementing them in production due to challenges like legacy system integration and data architecture limitations [17][32]. - Leading companies are restructuring processes around AI agents, focusing on multi-agent collaboration and viewing AI as a core component of workforce management [17][32]. Group 4: AI Infrastructure Strategy - Despite a dramatic decrease in inference costs (down 280 times over two years), overall AI spending remains high due to increased usage, prompting companies to shift from a "cloud-first" strategy to a hybrid architecture combining cloud, on-premises, and edge computing [18][33]. - Companies are investing in AI-specific data centers and "AI factories" to support this hybrid approach, while also addressing challenges related to employee skill transformation and sustainable computing innovations [18][33]. Group 5: Cybersecurity and AI Risks - AI introduces a paradox in cybersecurity, where the same technologies that drive innovation also create new vulnerabilities, necessitating robust risk management across data, models, applications, and infrastructure [21][35]. - Companies are advised to enhance security measures through access controls and model isolation, while leveraging AI for automated threat detection and red team testing [21][35]. Group 6: Emerging Technology Signals - The report identifies eight key technology signals to monitor, including the potential plateau of foundational AI models, the application and risks of synthetic data, and the rise of neuromorphic computing and edge AI [3][22]. - The ability to rapidly perceive, assess, and respond to technological changes will be crucial for companies to maintain competitiveness in the AI era [3][22].