On the enterprise side, enterprises in various industries are accelerating their embrace of generative AI and promoting the wave of intelligent upgrading. In the fields of transportation, energy, manufacturing, and chemicals, high-tech companies are working closely with traditional industries and investing significant resources to jointly develop industry-specific generative AI models to explore how to use this cutting-edge technology to drive innovation in the real economy. According to a global survey of business executives in 16 countries at the end of 2023, 79% of respondents believe generative AI will transform businesses and industries within three years, with nearly a third expecting the change to be happening already (14%) or within a year (17%).
Application effect: Various fields are vying to carry out intelligent transformation to reduce costs and increase efficiency
With the development of technology and the change of market demand, more and more enterprises have begun to realize the importance of using intelligent technology to optimize business processes and improve work efficiency.
For example, in the conversational user interface space, generative AI plays an important role. Many companies apply it to intelligent customer service systems, such as e-commerce platforms, and receive a large number of customer inquiries every day, covering various issues such as product information, order status, and after-sales service. Traditional human customer service is difficult to cope with such a large number of inquiries in a short period of time, resulting in long waiting times and poor customer experience. With generative AI-driven intelligent agents, you can respond to user inquiries in real time, understand user intent through natural language conversations, and provide answers quickly and accurately. Not only can it provide 24×7 hours of uninterrupted service, but also provide personalized services based on users' historical consultation records and behavioral data, significantly improving customer satisfaction and reducing manual customer service costs. According to statistics, after an e-commerce platform introduced intelligent customer service, customer service labor costs were reduced by 30%, and customer satisfaction increased to more than 85%.
In industrial manufacturing, generative AI is transforming businesses across the board. In the R&D and design process, through simulation and data analysis, we help enterprises quickly design more optimized product solutions.
In manufacturing, generative AI enables real-time monitoring and failure prediction of production equipment. By analyzing the operation data of the equipment, the potential faults can be found in advance, and maintenance can be arranged in time to avoid production interruptions and improve production efficiency and product quality.
The agriculture sector also benefits from generative AI. Taking precision agriculture as an example, generative AI can provide farmers with accurate planting recommendations through the analysis of multi-source data such as soil moisture, fertility, meteorological data, and crop growth conditions, including sowing time, variety selection, irrigation and fertilization plans, and more.
Figure: Generative AI is making its mark in many areas of the enterprise
According to the report, the early application of generative AI models is mainly focused on improving the efficiency of practical operations. As the technology matures and enterprises deepen their understanding of its potential, the application scenarios will gradually expand, which will not only affect the decision-making and strategic layout of enterprises, but also may reshape the industry structure and operation model.
In the short term, the application of large models focuses on high-frequency human-computer interaction and repetitive tasks. There is an urgent need for automation and efficiency improvement in such scenarios, and the accuracy and effectiveness of the model are easy to verify. For example, in customer service, document management, data processing, and other fields, large models can significantly speed up workflows, reduce labor costs, and provide a more personalized user experience through continuous optimization. This strategy takes into account the maturity of the technology, the acceptance of the business, and the likelihood of delivering value in the short term.
In the long run, the role of large models will go beyond the operational level and gradually extend to decision support and strategic management, and be deeply integrated into the core business of enterprises, such as market forecasting, risk assessment, strategic planning, etc. This puts forward higher requirements for the understanding and analysis ability of the model. With continuous optimization and self-learning, large models will be able to provide more accurate decision-making suggestions, and even make autonomous decisions in specific situations. This transformation will not only reshape the way of working, but also drive innovation in the organizational structure and operating model of enterprises, and promote the iterative upgrading of products and services, and ultimately the operation system of the entire industry.
The application of generative AI in enterprises has shown great potential and is gradually penetrating into all aspects of enterprise operations, bringing many advantages such as efficiency improvement, cost reduction, and innovation enhancement to enterprises, and becoming an important force to promote enterprise intelligent upgrading and industry transformation. With the continuous development and improvement of technology, the application of generative AI in enterprises will be broader, and it is expected to create more value for thousands of industries.
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