▲The Artificial Intelligence Foundation (AIF) and Taizhiyun AI Supercomputing Accelerator will jointly release the "Taiwan's AI Startup Map 2024" on November 25th. This map primarily lists startups established within the last eight years and categorizes them based on the infrastructure and vertical industry applications required for AI implementation. Companies can find partners based on their specific needs.
Since 2023, generative AI has rapidly swept the media, with technological iterations and application tools emerging like mushrooms after rain. But have these truly accelerated the pace of industrial AI transformation? This is an issue that AIF has been focusing on over the past year, and it is also a technological trend that many startups are closely tracking.
Ten years of infrastructure development have yielded results that have demonstrated practical value in multiple fields.
The global AI ecosystem is experiencing unprecedented growth. In his report, "The 2024 MAD (ML, AI & Data) Landscape," renowned investor Matt Turck points out that the number of companies in the Machine Learning, Artificial Intelligence, and Data (MAD) field has expanded to 2,011 in 2024, an increase of 578 from previous years. This contrasts sharply with the initial version in 2012, which only had 139 companies, demonstrating the rapid expansion of the industry. The generative AI that has gained significant traction in recent years is primarily driven by continuous investment in data infrastructure over the past decade, with its results already demonstrating substantial application value in various fields such as media, research, finance, and law.
International research firms like Gartner also believe that AI has been developing for several years, and this year is the time for organizations to deliver and implement AI. In other words, AI applications are no longer just experiments or small projects; they must become real products or services so that investments made over the past few years can begin to recoup, while also being able to address future risks and potential changes. No matter how eye-catching AI technology is, or how many unprecedented valuations it generates for startups, the ultimate issue is monetization—how to create business value and profit from it; this is the core of any business's development.
However, observing the current situation in Taiwan, according to the 2023 Taiwan Industrial AI Transformation Survey, only 10.21 TP3T companies were able to maturely utilize AI in different projects. A survey jointly released by AIF and Google Cloud in September 2024 further indicated that the average AI readiness index for Taiwanese companies was 54.08. Although most companies have recognized the importance of artificial intelligence and computing resources, their actual implementation efforts and methods still need improvement.
Whether it's data, technology, or vertical applications, how can startups add value to businesses?
Considering that the adoption rate of AI among Taiwanese enterprises is about 30%, and that AI adoption involves multi-faceted integration and transformation of organizations, this map is specially designed based on the needs of enterprises and combines five aspects of AI readiness: data capability, technical capability, computing power, governance capability and innovation capability. It is categorized according to the characteristics of startup services and application areas to build a more effective cooperation bridge between enterprises and startups.
Firstly, in the infrastructure sector, the company primarily comprises enterprises focused on data, technology, and computing services. Data resources include companies specializing in industry data collection and integration services, as well as data platform service providers offering cloud-based data processing and analysis capabilities. A company's "data power" refers not only to its ability to collect and manage data, but more importantly, to its ability to apply data. Data is the most important foundation for enterprises to drive AI, and strong data power is a key element for successful AI implementation. However, many companies experience significant gaps in data collection, management, and application, resulting in poor AI implementation outcomes.
Especially in the manufacturing sector, such as automobile and equipment manufacturing, companies that previously excelled in precision technology have been eager to catch up with the digital transformation and AI wave in recent years. However, they still face significant challenges in combining data with physical products and creating new value. Many companies quickly discover a lack of data when they begin implementing AI: some haven't collected any data at all, some have collected data but haven't organized it, and some have collected and organized data, but in the wrong direction… How to solve the data problem? Many startups are collecting data specific to their scenarios and providing corresponding analysis and solutions.
On the other hand, the retail service industry is also highly dependent on customer data. Especially in recent years, with the rise of various social media, how to integrate information from multiple sources to form effective consumer insights is not only a need for enterprises, but also a need for many startups to combine AI technology to provide corresponding solutions.
The "2024 Taiwan AI Startup Map" launch event will be held on November 25th, and partners from industry, academia, and research are invited to participate.
▚ Source:Know the situation