Understanding how AI empowers enterprise management from over 700 cases

Recently, Microsoft’s analysis shows that over 700 real business scenarios worldwide have successfully applied AI technology. These cases span numerous industries, including manufacturing, retail, healthcare, and finance, covering large multinational corporations as well as small and medium-sized enterprises, including several Fortune 500 companies. Research shows that for every 1 dollar invested in generative AI, enterprises can expect an average return of 3.7 dollars. Such an enticing ROI is undoubtedly accelerating the embrace of AI across various industries.

This article will classify and analyze the industry distribution, application types, and technological trends of AI based on 700 real cases, with a focus on how AI practices in enterprise management (such as ERP, finance, supply chain, etc.) inspire and serve Chinese enterprises.

1.Industry distribution: AI applications are flourishing everywhere, with finance and manufacturing leading the way.

From the collection of over 700 AI cases, AI empowerment has already penetrated almost all major industries. The financial and manufacturing industries are the most active domains for AI applications, accounting for about 21% and 19% of all cases, respectively. Following closely are industries such as technology/communication and services. This distribution of industries indicates that AI is no longer the “patent” of one or two sectors, but is becoming a universal enabling technology across industries. If we compare AI to a new infrastructure—just like electricity was to the Industrial Revolution—now AI is becoming the “new public resource” for enterprise digital transformation.

2.General Type: From Customer Service to forecast, AI plays a diverse role.

By sorting through 700 cases, it can be found that enterprises apply AI mainly in several typical scenarios, each corresponding to a different emphasis on enhancing business value.

(1) Customer Service and Marketing Automation: A large number of enterprises use AI chatbots and virtual assistants to optimize customer interaction. For example, Indonesia’s BRI bank integrates AI technology into its Customer Service Chatbot, supporting multiple local languages, significantly enhancing Customer Satisfaction. The retail e-commerce platform 17Life utilizes AI to automatically generate and categorize product tags, accurately understanding consumer search intent, and has successfully done so to significantly improve the efficiency of personalized recommendations. In the marketing field, Unilever has developed a marketing assistant that can automatically aggregate market data and generate creative proposals, significantly speeding up the planning and execution of advertising campaigns. These AI applications provide 24/7 quick responses and personalized content, which not only reduces the burden on human customer service and marketing teams but also significantly enhances customer experience and conversion rates.

(2) Improvement of Employee Efficiency and Collaboration: This is one of the most widely used types of AI applications, with many organizations equipping employees with AI “partners” to help alleviate daily repetitive work, allowing employees to focus on higher-value tasks. For example, after the Queensland Bank in Australia piloted the AI Copilot, 70% of employees saved 2.5 to 5 hours each week; the Somerset County Government in the UK deployed the Copilot, allowing employees to save 10 hours each month, and 87% of users provided feedback that their work was easier. The globally renowned accounting firm KPMG has developed an onboarding AI assistant that automatically provides templates and data references for new employees, reducing the preparation time for training materials by 20%. For example, after HP embedded GitHub Copilot into the development flow, programmers significantly increased their speed in coding and solving errors, and collaboration efficiency also improved noticeably. It can be said that the AI Assistant is becoming an invaluable <Partner> for <Employees>, capable of everything from writing <Documents>, summarizing meeting minutes, to gathering information.

(2) Business Process Flow Optimization and Automation: Many businesses are embedding AI into their core business process flows, driving a qualitative leap in operations efficiency. The most typical aspects in this regard are enterprise management and operations processes, covering finance, supply chain, production, internal support, and so on. For example, Animal Supply Company (a pet supply distribution company) has completely transformed the manual processing of invoices using an AI document intelligence platform, achieving an annual savings of $500,000 and freeing up 50% of the invoice clerk’s time for handling and building supplier relationships. In the manufacturing sector, Bridgestone is introducing AI into factory maintenance, combining sensor data and LLM to achieve predictive maintenance and natural language queries, enhancing production continuity and reducing downtime. For example, the Indian ICICI Lombard insurance company has created a Copilot <Assistant> for claims adjusters, which automatically <extracts> the <Main Point> from a vast amount of claims documents, reducing the time to process a claim by more than half. It can be seen that AI is penetrating deep into the business process flow, through automating repetitive tasks, intelligently analyzing data, and optimizing decision-making processes, making enterprise operations more efficient, accurate, and agile.

(3) Intelligent Forecast and Decision Support: The company is making more informed business decisions by leveraging the powerful analysis and prediction capacity of AI. In supply chain management, AI can perform demand forecasting and inventory optimization based on historical data and real-time information. For example, the European retailer SPAR developed an AI-driven demand forecasting system, with an inventory forecast accuracy of up to 90%. ACWA Power, a company in the energy <Domain>, combines AI and IoT to achieve real-time monitoring and predictive maintenance of power generation equipment, which not only reduces maintenance <Cost> but also enhances <Run> safety. Financial investment institutions are also introducing AI to assist in risk control decision-making. For example, UBS, a Swiss bank, is using AI to create a legal AI assistant that helps employees find compliance information more quickly, improving the efficiency and accuracy of risk assessment. In summary, using AI for <Intelligent Forecast> enables enterprises to proactively <Optimization> their supply chains, enhance equipment reliability, gain insights into market trends, and mitigate <Risk>, thereby seizing opportunities in a fiercely competitive environment.

(4) Innovative R&D and Product Service Upgrade: AI is becoming an innovation for enterprises, accelerating  development and new business incubation. Some pharmaceutical and research institutions have significantly shortened their research and development <Cycle> using generative AI. For example, Daiichi Sankyo in Japan built an <Internal> large model DS-GAI in just one month and promoted it throughout the company, improving the efficiency and accuracy of their researchers. The games and media industry is also using AI to release creativity: Square Enix developed a Slack chatbot to provide real-time technical Q&A support for game developers, accelerating game production; a UK media company, Four Agency, introduced AI to spark creative ideas, speed up data analysis, and report generation, allowing employees to dedicate more time to market expansion. It is foreseeable that AI will continue to drive innovation in business <Mode> and <Product> <Form>: From design prototypes, code development, to content creation and personalization services, everything has become quicker and more efficient because of AI. This provides the enterprise with an unprecedented acceleration of innovation.

3.Technology Trends: Large models lead the way, integrating diverse AI technologies.

Through case analysis, it is not difficult to find that the technical direction of today’s enterprise-level AI applications shows distinct trend features. Many cases reflect the trend of multi-technology integration. Companies often combine LLMs with RPA (Robotic Process Automation), knowledge graphs, computer vision (CV), and Speech Recognition to create comprehensive intelligent solutions. For example, the onsite sales assistant developed by HEINEKEN integrates speech recognition and computer vision: Sales representatives use AI voice services for multilingual voice input, with AI automatically recording the branch situation and triggering backend processes, while also utilizing CV technology for intelligent document parsing, which improves data collection efficiency for frontline sales.

4.Case insights: A new paradigm of enterprise management empowered by AI.

It is worth noting that among the more than 700 cases, a large number indicate that AI is fully penetrating management operations</x1> such as finance, supply chain, and human resources, bringing revolutionary changes to the operational models of traditional enterprises.

Meanwhile, in China, which is thousands of miles away, AI is also sparking a “digital intelligence revolution” in the field of enterprise management, especially in the central stages of financial management, supply chain, and other areas of enterprise management.

In intelligent finance, AI has great potential. The Finance Department has long been responsible for a large amount of tedious and repetitive work, such as accounting review, invoice processing, and report preparation. The introduction of AI is changing this situation. For example, Columbia BDO has reduced the Units of Production for payroll and financial processes by half through a virtual assistant, which is a typical representation of financial AI applications. In the domain of invoices and billing, Animal Supply in the United States saves $500,000 in costs each year by utilizing AI, enhancing the level of automation and accuracy in the Service Center. The insights from these practices are: The financial work can completely achieve a qualitative change through the “AI Financial Assistant.”

AI can quickly read instruments and vouchers, check for errors, and even provide financial insights based on historical data. For example, Kingdee’s intelligent financial solution aims to provide each employee with an AI financial assistant, which, through the perception, memory, and decision-making capabilities of the LLM, offers assistance ranging from travel agents, intelligent order review, financial data insights, analysis, to report writing. For financial personnel, Kingdee’s AI assistant can be embedded in financial process nodes to provide real-time guidance, through</x1> a sidebar tool to help with the extraction, comparison, and review suggestions of attachment information, achieving process reengineering. For management, the AI assistant can also provide a real-time summary of key financial indicators and conduct analysis and interpretation, helping decision-makers gain a comprehensive understanding of the business situation. It is conceivable that in the near future, “AI Accounting” and “AI Audit” will become standard, allowing financial personnel to be freed from a large amount of mechanical work and to focus their energy on strategic financial planning and business collaboration. This will undoubtedly greatly enhance the strategic value of finance management.

In the field of smart supply chain and ERP optimization, AI also has great potential. Supply chain management involves the purchase, inventory, production planning, logistics, and other stages. AI can optimize these complex processes through prediction and automation. For example, the “TAMI” system developed by manufacturing giant Textron Aviation allows frontline engineers to quickly search for maintenance instructions from 60,000 pages of technical documentation through conversation, reducing troubleshooting time from 20 minutes to less than 2 minutes—this is essentially a model of combining ERP/knowledge bases with LLM. Icertis provides AI-driven contract review, risk assessment, and hidden clause extraction for 30% of Fortune 100 companies. These solutions cover the entire contract lifecycle management (CLM), agreement data insights, legal due diligence, internal financial compliance reviews, and semantic search of union agreements, among various scenarios.

For Chinese manufacturing and supply chain companies, this means that by introducing AI, they can achieve a comprehensive upgrade from demand forecasting, procurement contracts, production scheduling to inventory management, reducing supply chain management costs and improving responsiveness to market changes. For example, Kingdee Cloud is based on intelligent large model technology, helping enterprises achieve intelligent contract review, automatic supplier recommendations, smart sourcing, and intelligent replenishment, among others.

It can be seen that the application of AI in the field of enterprise management is triggering a shift in management paradigms—from “human governance” to “human-machine collaboration.” For Chinese enterprises, this is both a challenge and a tremendous opportunity. As a top enterprise management AI company in China, Kingdee has provided numerous local companies with AI empowerment schemes that are suitable for the national context. For example, Kingdee Cloud provides intelligent assistants for finance, human resources, procurement, and other areas through its built-in Cangqiong Agent platform, deeply embedding AI into the business process flow. The five AI agents are designed to address the most common business needs: Golden Key Financial Report (for financial report analysis), ChatBI (a business data assistant), Recruitment Agent, Travel Agent, and Enterprise Knowledge Agent. They are ready to use and easy to deploy, bringing the power of AI to managers, specialists, and frontline employees alike.

Over 700 real cases worldwide vividly demonstrate that AI is no longer a cutting-edge technology experiment, but has become a practical business productivity tool. From enhancing employee efficiency, optimizing customer experience, to reshaping business processes and fostering innovative models, the value that AI brings to various industries and functions is evident. For enterprises in China, these global practices also provide valuable insights and confidence.