Editor profile
Editor, Enterprise AI & Implementation
Julian Chen investigates the practical implementation of AI in enterprise workflows and automation. He reports on ROI analysis and the digital transformation of traditional industries through AI.
julian.chen@aiindustryreview.orgIn-depth analysis of the regulatory challenges brought about by the rapid development of AI technology, exploring the theoretical foundations, main risk points, and implications for global enterprises and policymakers in building an international coordinated governance framework.
In-depth analysis of the scale, driving factors, key technological advancements, infrastructure competitive landscape, and the impact of regulatory policies on enterprise adoption and investment in the global artificial intelligence market from 2026 to 2035.
In-depth analysis of the differentiated pathways of major global jurisdictions in AI regulation, from the EU AI Act to the comparison between China and the US policies, revealing the challenges and opportunities for enterprises facing cross-regional compliance.
In-depth analysis of how Agentic AI can push generative AI from content creation towards real-time decision-making and complex workflow automation, exploring its practical implementation paths and business value in enterprise applications such as software development, RPA, and customer support.
This article deeply analyzes the complete lifecycle of end-to-end AI model training and fine-tuning, covering data preparation, model training, human feedback alignment (RLHF/DPO), and practical paths for multimodal AI, providing industry implementation perspectives for enterprises and investors.
In-depth analysis of the scale forecast, driving factors, key application scenarios, and challenges facing the US AI data center market. Analyze how this market will achieve exponential expansion through a compound annual growth rate of 27.4% from 2026 to 2032.
In-depth analysis of the structural impact of the emergence of China's new open-source large model GLM-5.2 on the global AI ecosystem, open-source vs. closed-source competition, and the implementation of enterprise AI applications, exploring new dimensions of Sino-US AI competition.
In-depth analysis of the engineering phase of large language model (LLM) development. This paper elaborates on three core bottlenecks—data quality, model evaluation, and alignment safety—arguing that the focus of future AI competition will shift from merely pursuing scale to building reliable and trustworthy AI systems.
Algomatic Dynamics, a new company under the DMM Group, is starting with $32.5 million in first-round funding and plans to launch an AI multi-fingered robotic hand platform in Japan by the end of 2026, with its business spanning motion data collection, robot learning, and bipedal stability control.
The EU AI Act, China's Generative AI Measures, and U.S. state-level legislation—global AI regulation is rapidly fragmenting. How can companies get ahead in multi-jurisdictional compliance? Drawing on the AI regulatory tracker report published by White & Case, this article reviews the trends and offers recommendations for response.
According to the latest report from SNS Insider, the vertical-domain large language model market is expected to grow from $4.56 billion in 2025 to $88.45 billion in 2035, with a CAGR of 34.55%, reflecting the evolution of enterprise-level AI toward deep industry customization.
Meta releases a new AI model and reiterates its open-source commitment, with Zuckerberg's declaration drawing industry attention. This article analyzes the profound impact of open-source AI on the enterprise market, competitive landscape, and future trends.
Based on an in-depth analysis of CybertizeWeb's "Global LLM Ecosystem Report," this provides a data-driven reference for industry decision-makers by interpreting the global large model market size, competition between open-source and closed-source, enterprise adoption rates, pricing trends, and multimodal evolution.
Based on White & Case's latest AI regulatory tracking report, this analysis examines the fragmented landscape of parallel US federal and state-level AI legislation, and its differentiated impact on tech giants, startups, and investment institutions. How can companies turn regulatory pressure into compliance competitiveness?
Deloitte releases its annual "2026 Technology Trends" report, interpreting enterprise AI applications, infrastructure investment, and competitive landscape changes from the perspective of the AI industry, and analyzing their implications for decision-makers and investors.
White Jade Law Firm published the "AI Watch: Tracking US Regulation" report, reviewing federal and state-level AI legislative developments and analyzing the profound impact of fragmented US AI regulation on enterprise-level AI deployment.
This article is based on the AWS official blog, analyzing the effectiveness of Amazon's application of advanced fine-tuning technology in three major scenarios—healthcare, engineering, and e-commerce—and exploring the competitive landscape and future trends of enterprise AI in the era of multi-agent orchestration.
This article analyzes the fragmented state of US AI regulation and explores the compliance challenges and response strategies faced by enterprises.
As the United States imposes export controls on Anthropic's models, AI sovereignty has become an unavoidable strategic issue for enterprises. This article analyzes full-stack control, emerging market response strategies, and the trend of global regulatory divergence, providing decision-making references for businesses.
Nature published a perspective article, proposing that understanding large language models requires distinguishing between human projection and machine cognition, and that the framework of machine empiricism may change the logic of R&D, investment, and evaluation in the AI industry.