Artificial intelligence is a widely used technology that, with the help of machines, reshapes the process of human beings integrating information, analyzing data and obtaining insights, helping humans improve efficiency and optimize decision-making. After years of development and practice, artificial intelligence has become an increasingly widely adopted technology and developed into a new infrastructure that empowers all walks of life and reshapes the industry landscape.

The penetration rate of artificial intelligence in the overall economy has been growing, but to a large extent it still faces many development bottlenecks, including insufficient data, relatively high application costs, system security and governance issues, and deployment challenges. However, in recent years, the market and society as a whole have gradually recognized the transformative role of artificial intelligence. In particular, the following advances in technological and social factors have accelerated the commercial application of artificial intelligence.

Today's world has achieved extensive digitization and interconnection, which has also led to a rapid increase in the amount of data. In 2021, 83ZB of data was created, acquired, copied and consumed globally, which has increased nearly 30 times in the past decade, and is expected to further grow to 208ZB in 2026. The important information contained in the huge amount of data has created a lot of opportunities for every organization. However, the surge in data volume has also brought unprecedented challenges to data analysis. It has become increasingly difficult and costly to handle data analysis tasks manually. Against this background, the accumulation of data has promoted the application of artificial intelligence. At the same time, artificial intelligence learns, trains and develops from abundant data, becoming smarter and able to solve real-world problems in a more effective way.

Rapid increase in computing power: Computing power is closely related to the iteration of chip development. Compared with previous generations of chips, most chip companies' artificial intelligence chip products have significantly improved their computing power. The computing power of the new generation of artificial intelligence chips from major chip manufacturers can be increased by up to ten times compared with the previous generation of the same series of products.

Reduced time and cost of model training: The emergence of new algorithms and frameworks has improved the efficiency of artificial intelligence training and deployment in industries. For example, with the help of transfer learning technology, the insights gained from an artificial intelligence model can be migrated and copied to new areas; with the help of automatic machine learning (AutoML) technology, all developers and business personnel can develop and optimize machine learning models, reducing Reliance on machine learning experts.

Artificial intelligence continues to help transform all walks of life around the world, and decision-makers in various organizations have also noticed this and invested in artificial intelligence. In 2021, global artificial intelligence spending will reach US$163.8 billion, compared with US$46.2 billion in 2017, with an average annual compound growth rate of 37.2%, and is expected to increase to US$463.9 billion in 2026, with an average annual compound growth rate of 23.1%. It is expected that by 2030, artificial intelligence will drive nearly 15% of global GDP.

Among the pioneers in the application of artificial intelligence, the domestic market is highly active and is now leading the development of the global artificial intelligence industry with artificial intelligence innovations that continue to cross existing boundaries and emerge rapidly. On the demand side, AI is considered an easily accessible and easy-to-use tool that enables entities of all sizes to achieve operational efficiency improvements and business success in today’s digital era. The country's huge economic scale and considerable social activity level have brought about a variety of application scenarios that can be integrated with artificial intelligence. There is a huge demand for artificial intelligence solutions in the Chinese market, and they need to be customized for diverse and dynamic real-life scenarios, which also encourages innovation in technology and business models in the artificial intelligence industry. On the supply side, domestic AI providers benefit from the large and growing amount of data generated by the scale of the economy and social activity levels, a strong talent pool, leading research capabilities and dynamic AI field players. In addition, the government's supporting policies and regulations to promote artificial intelligence technology, artificial intelligence talent education and the application of artificial intelligence solutions are expected to further promote the rapid development of China's artificial intelligence industry.

In 2021, China's artificial intelligence expenditure reached 198.7 billion yuan, and is expected to grow to 846.6 billion yuan in 2026, with an average annual compound growth rate of 33.6%. According to the "2022 Artificial Intelligence Index Report", China is a major artificial intelligence market with increasing global influence, which is also confirmed by the following facts:

China's artificial intelligence industry can be divided into four major categories according to application fields: decision-making artificial intelligence, visual artificial intelligence, speech and semantic artificial intelligence, and artificial intelligence robots. The following sets out the definitions and typical application scenarios of each of the four categories:

Among the above categories, decision-making artificial intelligence is expected to be the fastest growing category. In 2021, the expenditure scale of China's decision-making artificial intelligence market reached RMB 47.1 billion, and is expected to grow to RMB 284.1 billion in 2026, with an average annual compound growth rate of 43.3%.

To fully exploit the value contained in data, many organizations have adopted a data-driven approach to support decision-making in daily operations. In the face of ever-increasing amounts of data, data analysis by machines rather than humans has different effects.

Before the rise and eventual commercial application of artificial intelligence, even if there is enough data, companies still need to rely on artificial perception, experience, judgment, and sometimes even intuition to make decisions. The world we live in today is changing rapidly. It is difficult to predict the risks involved in making key decisions by relying only on common sense and accumulated experience, nor can we bear the consequences of making wrong decisions. At the same time, in the digital age, manually processing and analyzing massive amounts of data has become increasingly difficult, costly, and impractical.

Artificial intelligence-driven decision-making models overcome the limitations of human limited rationality and cognitive biases, and are now gradually liberating and enhancing human capabilities. In some cases, they can even replace human labor in traditional workflow models to improve the reliability of decision-making. and efficiency. As artificial intelligence is applied to more and more diverse scenarios, decision-making artificial intelligence can optimize almost all components of enterprise operations from top to bottom, including but not limited to expanding business scale, improving marketing effects, and improving operational efficiency. For example, in the context of smart marketing, AI-driven solutions can drive revenue growth for e-commerce companies by improving the accuracy of marketing delivery. Artificial intelligence-driven decision-making is also changing the risk management system in the financial industry. For example, using AI credit risk models can significantly reduce default rates. More and more industries are happy to see the huge impact brought by advanced decision-making artificial intelligence technology.

Different from other AI solution categories that mainly focus on the perception and recognition of data patterns, decision-making AI provides predictive analysis and recommendations to support and guide business actions. It is used in various real-life scenarios such as precision marketing, risk management, and daily operation optimization. According to information from Chishi Consulting, it is expected that China’s decision-making artificial intelligence expenditure will increase significantly in the next five years and account for a higher proportion of overall artificial intelligence expenditure.

Although China’s market environment is conducive to promoting the development of decision-making artificial intelligence, various organizations still often face several major challenges that make it difficult to develop and adopt artificial intelligence applications on their own: Shortage of artificial intelligence experts: Lack of experienced artificial intelligence experts and Data scientists have always been a key obstacle for companies to establish internal artificial intelligence teams and develop and operate artificial intelligence infrastructure by themselves. For many companies, talent shortages are a major obstacle to successful internal AI development.

High total cost of ownership and uncertain ROI: Building a proprietary AI system or integrating multiple point AI software application solutions is prohibitively expensive for most businesses. For example, according to Chishi Consulting's estimates, a company generally needs an upfront investment of approximately RMB 500 million to develop a complete set of enterprise-level artificial intelligence systems internally, and will subsequently incur ongoing maintenance costs of approximately RMB 50 million per year. As a result, the total cost of ownership is much higher than the company's annual expenditure through external procurement of an artificial intelligence system of the same standard (approximately RMB 50 million to RMB 100 million). Moreover, due to the lack of artificial intelligence expertise and model training, the internally developed artificial intelligence system may require more investment and take longer to evaluate, and the improvement in the system's effectiveness and efficiency may not meet expectations, resulting in a high return on investment. uncertain.

Difficulties in implementation: Due to their technology- and capital-intensive nature, enterprises may need a lot of resources to deploy AI solutions on a large scale to develop their customized applications and make internal processes (such as decision-making) intelligent. In most cases, enterprises need to develop and deploy self-developed artificial intelligence applications or outsourced point solutions in a "trial and error" manner to find the best application combination that best suits their business. This process takes a long time. long. According to estimates from Chishi Consulting, with the existing artificial intelligence team configuration, it will take an average of about three years for enterprises to complete the internal construction of large-scale artificial intelligence infrastructure and artificial intelligence capabilities.

Data and system incompatibility: The use of externally procured single-point artificial intelligence application solutions creates the risk of incompatibility between single-point solution applications and/or between single-point solution applications and internally developed artificial intelligence applications. Additionally, increased awareness of data security, data privacy, and centralized data and systems management will complicate the deployment of disparate point solutions and the aggregation of multiple data sources.

With the rapid rise of platform-centric AI solutions in China, the above challenges can be identified and appropriately addressed. Unlike point solutions, in addition to artificial intelligence applications and basic computing infrastructure, platform-centric decision-making artificial intelligence solutions also provide end users with an artificial intelligence development platform. This artificial intelligence development platform provides end users with unified development standards, high compatibility, and the ability to flexibly expand applications according to actual needs. Its plug-and-play features and infrastructure that can be used to further develop and operate solutions for specific application scenarios make the decision-making artificial intelligence platform more flexible, scalable, compatible, and easier to manage.

In the decision-making artificial intelligence market, China’s platform-centered decision-making artificial intelligence market segment is constantly expanding. In 2021, the size of the platform-centered decision-making artificial intelligence market reached RMB 9.4 billion in terms of artificial intelligence spending, and is estimated to grow to RMB 84.5 billion in 2026 at a compound annual growth rate of 55.0%, beyond decision-making The overall growth rate of the artificial intelligence industry. The key difference between non-platform-centric and platform-centric decision-making AI solutions is the potential for AI development and deployment to meet changing needs, i.e. more AI applications in other Scalability in application scenarios and compatibility of such artificial intelligence applications. Most of the decision-making AI applications that are not platform-centric are fixed and one-time deliverables. It is difficult to meet the additional demand of users to expand the original solutions, mainly because of the artificial intelligence designed for different workflows. Applications, their basic frameworks, related data governance infrastructure or processing rules may also be different, resulting in incompatibilities in artificial intelligence systems. In contrast, a platform-centric decision-making AI solution based on a decision-making AI platform provides an operating environment and tools that allow flexible AI development, with modules and modules that can be migrated and replicated to similar or adjacent scenarios. application. It also unifies the development environment and rules on which all applications are built. This unified approach eliminates the switching costs of integrating independently developed non-platform-centric decision-making AI applications and makes AI systems fully compatible with each other. In this regard, platform-centric decision-making artificial intelligence solutions can give enterprises more room for smooth and complete artificial intelligence development and deployment in the future.

AI solution providers typically seek to start with selected customers, especially industry leaders, as a starting point for industry development. Providers will first establish mutual trust and cooperative relationships with "benchmark" customers in various industries or fields, help "benchmark users" identify key problems, provide solutions, and help customers improve their business value. AI solution providers will scale their deployments by capturing incremental customer needs. These cases of artificial intelligence "benchmark users" will continue to attract potential customers in corresponding industries and fields. Therefore, being able to create value and help customers achieve continued success is critical for decision-making AI companies to become industry pioneers.

After helping "benchmark users" achieve increased business value, decision-making AI providers can use their industry knowledge and practical experience to attract and serve other customers in the target industry. First-mover advantage is an important factor for success in the artificial intelligence industry. Benefiting from rich application scenarios, in-depth relationships with "benchmark users" and rapid accumulation of industry knowledge, pioneers in the industry can achieve faster business expansion and establish a virtuous closed loop. As a result, they are able to iterate on smarter AI and quickly adopt effective AI solutions to meet changing user needs.

Technical capabilities are critical for AI solution providers to thrive. Given the rapid changes in the artificial intelligence industry, the ability to continuously adopt advanced technologies and launch innovative solution services is key to maintaining competitive advantage.

The demand for artificial intelligence talents has grown rapidly over the past few years. As the industry changes rapidly and AI solutions are widely deployed, companies with varying AI capabilities are aware of technology gaps and working to fill them. The technology-intensive nature of artificial intelligence business also requires experienced and technically proficient talents. Therefore, the ability to continuously attract and retain talents has become a major factor in corporate success.