Core Insights - The article discusses the disparity between the hype surrounding AI and its actual implementation in businesses, highlighting that many companies are stuck in a "+AI" mindset rather than fully integrating AI into their core operations [1][4][5] - A new AI Adoption Maturity Model (AIM²) has been developed to help companies transition from a superficial use of AI to a more integrated approach, termed "AI+" [2][9] Group 1: Current State of AI in Business - Many companies are investing in AI due to a sense of insecurity, with 50% of surveyed firms citing this as their primary reason for adoption [1] - A significant 95% of organizations involved in Generative AI (GenAI) projects reported no returns, indicating a failure to translate investments into productivity gains [4][5] - The lack of a systematic framework and reliance on intuition rather than data-driven decisions have led to widespread failures in AI implementation [5][11] Group 2: The AIM² Model - The AIM² model provides a structured approach for businesses to assess their AI maturity across five levels and six dimensions, guiding them from "+AI" to "AI+" [9][11] - The model emphasizes the importance of integrating AI into business strategy, operations, and technology, rather than treating it as an add-on [4][9] - AIM² aims to reduce marginal costs by promoting platformization, standardization, and reuse of AI applications [11] Group 3: Industry Examples and Applications - China Pacific Insurance has successfully integrated AI into its operations by merging various data sources, positioning itself strategically in the "big health" ecosystem [7][12] - Ant Group's AI health manager demonstrates the potential of AI to create a closed-loop ecosystem in healthcare, enhancing decision-making and operational efficiency [8][12] - In the retail sector, L'Oréal has localized AI technologies to enhance its market competitiveness, showcasing the benefits of embedding AI within existing ecosystems rather than building new platforms [13] Group 4: Future Directions - The transition to "AI+" requires a fundamental shift in how businesses approach AI, making it a core driver of innovation rather than a supplementary tool [7][14] - Companies that successfully adopt the AIM² model will not only improve their operational efficiency but also enhance their competitive positioning in the industry [15] - The future of AI in business is expected to reshape industry ecosystems, guiding companies to make informed decisions and optimize their AI strategies [14][15]

Core Viewpoint - The article emphasizes the need for companies to transition from a "+AI" mindset, which merely adds AI tools to existing processes, to an "AI+" approach that fundamentally integrates AI into business strategies and operations [3][6][12]. Group 1: Current State of AI Adoption - Many companies are investing in AI but are struggling to realize its full potential, with 95% of organizations seeing no return on their GenAI investments [8]. - The primary reason for this stagnation is the lack of a systematic framework for AI application, leading to a focus on technology rather than practical integration into business [9][12]. - A significant portion of companies (50%) adopt AI out of "insecurity," lacking a clear understanding of how to leverage AI effectively [3][4]. Group 2: AIM² Model Introduction - The AIM² model, developed by Shanghai Jiao Tong University and various industry partners, aims to provide a structured approach for companies to assess and enhance their AI maturity [4][15]. - This model consists of a "five-level six-dimensional" framework that guides companies from using AI as a tool to making it a core component of their business strategy [15][17]. - The six dimensions include strategy, organization, data, technology, application, and business, promoting a holistic view of AI integration [17]. Group 3: Transitioning to "AI+" - The transition to "AI+" requires companies to embed AI deeply into their operations, transforming it from a tool into a driving engine for innovation [12][13]. - Successful examples include China Pacific Insurance, which integrates AI into its healthcare and insurance services, and Ant Group's AI health manager that connects various resources in the medical ecosystem [12][13]. - Companies must focus on creating a complete solution that clearly defines the measurable value AI brings to their operations [9][12]. Group 4: Industry-Specific Applications - In the financial sector, companies like Shanghai Bank are using AI for risk management, optimizing credit assessments through machine learning [18]. - The healthcare industry is leveraging AI to enhance service delivery, as seen with Meinian Health's upgrades to its health management systems [18]. - Retail companies like L'Oréal are adapting AI technologies to local markets, enhancing their competitive edge through localized data platforms [19]. Group 5: Future Outlook - The future of AI in business lies in its ability to evolve into an intrinsic capability that drives long-term competitive advantage through ecosystem development rather than short-term technological gains [13][22]. - Companies that successfully implement the AIM² model will not only improve their current operations but also position themselves strategically for future industry shifts [22].

Core Insights - The evolution of AI large models is marked by a shift from general models to specialized vertical models, enhancing AI's ability to solve specific problems in various industries while changing commercialization methods and potential [1][2] - By 2024, vertical models are expected to achieve commercial applications in manufacturing, finance, and healthcare, becoming crucial for companies to enhance efficiency and reduce costs in their digital transformation efforts [1][2] - The current application of vertical models is limited to specific operational segments, which restricts their overall impact on business processes, highlighting the need for a transition from "AI+" to "+AI" to unlock greater value from AI applications [1][2][3] Industry Trends - The use of AI in enterprises has remained around 50% from 2018 to 2023, with a projected increase to 75% in 2024, indicating a significant growth in AI adoption across business functions [2] - The three essential elements of business—production, management, and sales—are undergoing transformation due to AI, leading to substantial cost reductions in production and potential improvements in management efficiency through digital tools [2][3] Application and Development - Current AI applications in manufacturing focus on three main areas: process management to enhance execution efficiency, intelligent Q&A for knowledge acquisition, and decision support through complex data analysis [3][4] - The development of a digital operating space that facilitates collaboration between humans and AI agents is essential for advancing AI applications beyond single-task execution to more integrated solutions [4][5] Ethical Considerations - The establishment of a reusable, interconnected, and trustworthy AI ecosystem is critical, requiring collaboration on standardization and ethical practices within the industry [4][5][6] - Ensuring that AI operates within ethical boundaries is vital for maintaining system stability and preventing issues such as "AI hallucinations," which can undermine trust in AI-generated content [5][6]