AI is rewriting the rules. How can traditional industries turn around?

With the development of science and technology, AI technology occupies most of the market. In this case, we have to think about how traditional industries should deal with themselves? The following article is compiled and shared by the author on how traditional industries can adjust to the impact of the AI wave.

In the era of digitalization, AI technology is sweeping across like a huge wave. Both emerging industries and traditional industries are facing unprecedented challenges and opportunities. For traditional industries, how to find their footing in this wave and embrace change has become a question that every entrepreneur and decision-maker must think about.

1. Dilemmas and opportunities of traditional industries

Traditional industries, whether manufacturing, agriculture, retail or service industries, have profound historical accumulation and unique cultural traditions. These industries have formed an inherent set of operating models, business processes and market strategies over a long period of development. However, with the rapid advancement of science and technology and the advancement of globalization, these industries are also facing unprecedented challenges.

Dilemma:

• Bottleneck of production efficiency: For a long time, many traditional industries have relied on manpower and experience for production and management. However, with the expansion of market size and the diversification of consumer needs, this reliance has been unable to meet the high efficiency requirements of modern production.
• Rapid changes in market demand: In the context of digitalization and globalization, consumer needs and tastes are changing at an unprecedented speed. This is undoubtedly a huge challenge for traditional companies that have relied on fixed market strategies for a long time.
• Intensifying competition: With the advancement of technology and the influx of multinational companies, competitors in traditional industries are no longer just local counterparts, but giants from around the world. This makes the market competition more intense, and those companies that cannot adjust their strategies in time face the risk of being eliminated.

Opportunities:

• Technology-driven innovation: Although traditional industries are facing various difficulties, there are also huge opportunities. AI technology, as one of the most transformative technologies today, provides unprecedented innovation space for traditional industries. By introducing AI technology, companies can not only improve production efficiency, but also dig deeper into market demand and provide consumers with more personalized products and services.
• Market opportunities of globalization: With the advancement of globalization, traditional industries also have the opportunity to go abroad and enter the international market. Through AI technology, companies can more accurately analyze global market needs and trends, thereby formulating more reasonable market strategies.
• Possibility of cross-border cooperation: In the AI wave, the boundaries between different industries are gradually blurring. This provides traditional industries with the possibility of cross-border cooperation with other industries to achieve complementary advantages and jointly create greater market value.

Before discussing how AI technology can bring changes to traditional industries, we first need to deeply understand the core value of AI technology. AI, or artificial intelligence, is not just a technology, but a new way of thinking that is profoundly changing our understanding of data, decision-making, and innovation.

The power of data：

In the traditional business model, data is often regarded as an auxiliary tool for recording and reporting the operation of the business. But in the AI era, data has become a company's most valuable asset. Through deep learning and machine learning technologies, AI can extract valuable information from large amounts of data and provide enterprises with unprecedented insights. For example, the manufacturing industry can analyze production data and monitor the operating status of equipment in real time to achieve predictive maintenance, greatly reducing downtime and repair costs.

The revolution of decision-making:

With the help of AI, decision-making no longer relies solely on human experience and intuition. AI technology can provide decision-makers with more accurate and comprehensive data support, making the decision-making process more scientific and reasonable. For example, the retail industry can accurately predict future sales trends by analyzing consumer shopping data, thereby providing strong data support for inventory management, promotion strategies, etc.

The driving force of innovation：

AI technology can not only help enterprises optimize existing business processes, but also open up new business areas for enterprises. Through AI technology, companies can gain a deeper understanding of market demand, explore potential business opportunities, and achieve product and service innovation. For example, agriculture can use drones and image recognition technology to achieve precise monitoring of farmland and provide farmers with more scientific planting suggestions.

3. Three strategies for embracing change

In the face of the AI wave, entrepreneurs and decision-makers in traditional industries may feel confused and uneasy. But in fact, as long as the right strategies are adopted, traditional industries are fully capable of finding their place in this technological revolution. The following are three core strategies designed to help traditional industries better embrace the changes brought about by AI:

In-depth cooperation and co-creation：

• Integration of technology and industry: The application of AI technology is not isolated, it needs to be combined with specific industry scenarios. Therefore, traditional industries should take the initiative to establish in-depth cooperative relationships with AI technology companies and jointly explore how to integrate AI technology with their own businesses. For example, the textile industry can cooperate with AI companies to use machine learning technology to conduct quality inspections on textile materials, thereby improving product quality and production efficiency.
• Open Innovation Platform: Enterprises can consider establishing an open innovation platform and invite external technical teams, research institutions and startups to participate to jointly develop AI solutions suitable for their own industries. This can not only accelerate the technology research and development process, but also bring more innovative ideas and opportunities to enterprises.

Training and Education：

• Internal training: The introduction of AI technology often requires corporate employees to have certain technical knowledge and abilities. Therefore, companies should increase AI technology training for employees to ensure that they can master and apply relevant technologies proficiently. This can not only improve the company's technology application capabilities, but also enhance employees' awareness of innovation and enthusiasm.
• Cooperation with academia: Enterprises can establish cooperative relationships with universities and research institutions to jointly carry out research and training projects on AI technology. This can not only provide enterprises with a large number of technical talents, but also strengthen exchanges and cooperation between enterprises and academia.

Innovative Thinking and Culture：

• Encourage trial and error: In the application process of AI technology, failures and setbacks are inevitable. Enterprises should encourage employees to dare to try and innovate without fear of failure. Only by establishing a culture that encourages trial and error can companies continue to explore and advance in the AI wave.
• Cross-border thinking: The application of AI technology often requires cross-border thinking and perspectives. Companies should encourage employees to go beyond their own areas of expertise and communicate and cooperate with experts in other industries and fields to gain more innovation inspiration and opportunities. 4. Specific case analysis: changes in the retail industry. As the industry that has the most direct contact with consumers, the changes in the retail industry are particularly eye-catching. Driven by AI technology, the retail industry is experiencing an unprecedented technological revolution. From offline to online, from tradition to modernity, every link is full of innovation and opportunities. The intelligence of offline retail:
• Smart shelves: By installing sensors and cameras, smart shelves can monitor the quantity and status of goods on the shelves in real time. When goods are out of stock or improperly placed, the system will automatically send reminders, thereby improving shelf management. efficiency and accuracy.
• Unmanned Supermarket: By using RFID, computer vision and deep learning technology, consumers can freely choose products in unmanned supermarkets. There is no need to queue up at checkout, and the system will automatically deduct money for consumption. Provide users with a more convenient shopping experience.
• Personalization of online retail：
• Personalized Recommendation: By analyzing consumers’ shopping records, browsing history and social networks, the AI system can provide consumers with personalized product recommendations, thereby improving conversion rates and customer satisfaction.
• Virtual Fitting: Through AR technology, consumers can try on products in the online mall and see the true effect of the products on themselves, thereby greatly improving the efficiency of purchasing decisions.
• Supply chain optimization：
• Intelligent inventory management: By analyzing sales data, market trends and supply chain information, the AI system can provide retailers with more accurate inventory forecasts, thereby reducing inventory costs and improving capital usage efficiency.
• Dynamic Pricing: By analyzing market demand, competitor prices and inventory conditions in real time, the AI system can provide dynamic pricing suggestions for retailers, thereby optimizing prices and increasing sales profits.

This article was originally published by @yancheng on Everyone is a Product Manager. Reprinting without permission is prohibited

Title picture comes from Unsplash, based on CC0 protocol

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