Artificial intelligence (AI) technology is reshaping the way businesses make decisions. Through powerful data analysis capabilities and automation technologies, AI helps businesses make faster and more effective decisions in complex business environments. This article will start from the principle of AI to improve decision-making efficiency, and gradually discuss its specific practice in two key application areas: customer experience and marketing strategy optimization, and operation and supply chain management.
1. Data-driven decision optimization: The principle of AI to improve decision-making efficiency
The core value of AI in enterprise decision-making lies in its powerful data analysis and prediction capabilities. While traditional decision-making methods often rely on limited data and human analysis, they cannot respond quickly to dynamic market changes, while AI technology can process massive amounts of data and generate deep insights to support companies to make more accurate and efficient decisions.
By integrating historical and real-time data, AI systems are able to predict market trends, identify potential risks, and provide solutions. For example, in the financial sector, AI systems can quickly identify abnormal behaviors by analyzing transaction data in real time, helping enterprises effectively prevent fraud risks. In the retail industry, AI analyzes historical sales data and customer behavior to help companies predict high-demand items and optimize purchasing and inventory management.
AI technology improves the overall decision-making efficiency of enterprises in a data-driven way, and its value is particularly significant in the areas of customer experience and marketing strategy optimization, and operations and supply chain management. Next, this article will explore in detail the practical examples of AI technology in both areas.
2. Improvement of customer experience and marketing strategy
Behind customer experience and marketing strategies are a number of complex decision-making steps, such as customer segmentation, personalized recommendations, and marketing budget allocation. By improving the efficiency of these key decision-making links, AI technology can help companies quickly respond to customer needs and formulate accurate marketing plans, thereby significantly improving decision-making efficiency.
The potential and practice of generative AI
In the field of marketing, generative AI technology has shown significant application value, not only to capture user needs in real time, but also to quickly generate high-quality, personalized content. For example, Taobao Askang, the first generative AI application in the e-commerce industry, has attracted more than 5 million experiences since its closed beta launch in September 2023. During last year’s 11.11, users asked more than 8 questions per day through the AI shopping guide system, and the maximum number of questions asked by a single user reached 4,000. Based on the questions raised by users, AI can analyze needs in real time and generate highly matched product recommendations, significantly improving users’ shopping conversion rates and satisfaction.
The potential of this generative AI is also reflected in the application of tools for merchants. For example, the AI tools launched by Taotian Group and Alimama’s Wanxiangtai Unbounded Edition were called more than 1.5 billion times during last year’s Double 11, helping merchants quickly generate marketing copy, optimize product display, and accurately reach target consumers. Through these tools, merchants not only improve the efficiency of content production, but also effectively increase product exposure and sales conversion rate.
Whether it’s an AI shopping guide tool that serves consumers directly or a content generation tool for merchants, generative AI technology has demonstrated its core value of improving decision-making efficiency – by understanding consumer needs and optimizing marketing content and strategies, companies can significantly increase customer satisfaction and brand loyalty, while winning more business returns against the competition.
3. Intelligent operation and supply chain management
Operations and supply chain management are full of complex decision-making tasks, such as inventory management, logistics scheduling, and quality control, and the decision-making efficiency of these directly affects the operating costs and overall competitiveness of enterprises. AI technology optimizes these critical decision-making processes through real-time data analysis and automated tools, helping enterprises achieve efficient allocation and precise management of resources.
Optimization of inventory management
In terms of inventory management, AI technology can quickly predict future inventory demand by analyzing historical sales data and market trends, helping business decision-makers adjust procurement plans in time to avoid excess inventory or shortages. Taking Skechers as an example, the company has achieved rolling forecast tracking through Guanyuan BI, which has effectively improved the efficiency of inventory management. Lee, head of its data platform, mentioned that traditional inventory reports usually take 15-20 days to complete, while BI-based systems can automatically generate them on the 6th or 7th every month, improving the forecast timelines by 10-15 days. This fast, efficient forecasting capability significantly optimizes inventory planning, reduces inventory costs, and improves operational efficiency.
Intelligence in logistics scheduling
In the field of logistics, AI technology can analyze traffic and weather data in real time, optimize distribution routes, and help enterprises make dynamic scheduling decisions. For example, a county-level logistics company in Hubei Province fine-tuned an open-source model based on LLaMA-2-7B and injected enterprise-specific data, including historical order data, rural road data, and market periodic tables. Trained on the Google Colab Pro+ platform, the company developed dynamic path planning and demand forecasting. After implementation, its average daily mileage was reduced from 380 km to 308 km, a reduction of 19%; The vehicle loading rate increased from 67% to 82%, saving an average of 540,000 yuan in fuel costs per year. This case fully illustrates that the customized fine-tuning open-source model can effectively solve the pain points of rural logistics distribution, optimize scheduling decisions, and greatly reduce operating costs.
Intelligent quality control
In addition, AI technology also plays an important role in decision-making in the quality control process. Through machine vision and deep learning algorithms, AI systems are able to monitor production lines in real-time, quickly identify potential quality issues and suggest adjustments. This automated quality management method not only reduces production costs, but also improves customer satisfaction with products.
4. Challenges and Suggestions
Challenge analysis
- Data quality and privacy concerns: Small and medium-sized enterprises (SMEs) may lack high-quality data and need to pay special attention to data privacy and compliance when using AI technology.
- Technology cost and resource constraints: Many SMEs have limited budgets and lack a dedicated team to support the implementation and maintenance of AI.
- Lack of employee skills: The use of AI requires teams that are familiar with technology systems, and traditional enterprise employees may face the challenge of upskilling them.
Recommendations for SMEs
1. Prioritize low-cost, high-yield scenarios: Prioritize AI applications in areas such as customer service, marketing automation, or inventory management, where the ROI ratio is high and it is easy to achieve quick results.
2. Leverage open-source models and cloud platforms: Enable custom AI applications at a lower cost by fine-tuning open-source models (e.g., LLaMA-2, ChatGLM) and using cloud platforms (e.g., Google Colab Pro+).
3. Focus on employee training and partner selection: Quickly ramp up your in-house AI application capabilities by training existing employees or partnering with AI service providers.
With scientific planning and effective implementation, SMEs can also use AI technology to significantly improve decision-making efficiency and gain a competitive advantage in a rapidly changing business environment.
References：
[1] Prasanth A, Densy J V, Surendran P, et al. Role of artificial intelligence and business decision making[J]. International Journal of Advanced Computer Science and Applications, 2023, 14(6).
The work described in this article was supported by InnoHK initiative, The Government of the HKSAR, and Laboratory for AI-Powered Financial Technologies (AIFT).
(AIFT strives but cannot guarantee the accuracy and reliability of the content, and will not be responsible for any loss or damage caused by any inaccuracy or omission.)
