The scarcity of cross-field leaders and the challenges of industrial application of AI large models

In today's era of rapid technological development, the potential of artificial intelligence (AI) large models is widely optimistic. However, surprisingly, despite the continuous advancement of AI technology, we rarely see the emergence of large models for certain industrial applications. . This article will delve into the reasons behind this phenomenon, especially from the perspective of managers, to analyze why leaders with the triple capabilities of business, programming and AI technology are scarce, and how this scarcity affects the use of large AI models in the industry. application.

The importance of business capabilities

Business capabilities refer to a deep understanding of the pain points and needs of a specific industry. This ability comes from the accumulation of long-term experience in working in a specific industry, and requires a keen insight into industry dynamics, customer needs, market trends, etc. Business experts can identify which problems can be solved by AI technology and which problems require other methods. However, business experts often lack knowledge of programming and AI technologies, which limits them from translating business needs into concrete technical solutions.

The necessity of programming skills

Programming ability is the key to converting business requirements into technical implementation. Programming is not just about writing code, but also includes the understanding of development frameworks, mastery of software design processes, and the application of logical thinking. An excellent programmer can decompose complex business requirements into executable code and build a stable, efficient, and scalable software system. However, programming experts often focus on technical implementation and may not have a deep understanding of business needs, which also limits the application of AI technology in industry.

Complexity of AI Technology

AI technology, especially the development of large models, involves many aspects such as data science, neural network architecture design, training optimization, and inference algorithms. AI experts need to have a strong mathematical foundation, algorithm design capabilities and sensitivity to data. They are able to design powerful AI models, but without a deep understanding of business needs, these models may not solve real problems. In addition, the rapid development of AI technology also requires experts to continue learning to keep up with the latest research progress.

The scarcity of leaders with triple abilities

An ideal leader should have both business, programming and AI technology capabilities. Such leaders can deeply understand business needs, design technical solutions that meet the needs, and use AI technology to implement these solutions. However, due to the high degree of specialization and complexity of each of these three fields, it is very rare for someone to be proficient in all three. In most cases, team members each specialize in one area, and leaders need to have the ability to coordinate and integrate across areas.

Challenges of cross-disciplinary collaboration

Even with triple-capable leaders, cross-disciplinary collaboration is a huge challenge in itself. Business, programming, and AI technology each have their own unique workflows, terminology, and ways of thinking. Leaders need to be able to understand and integrate these different perspectives to ensure team members can communicate and collaborate effectively. This requires leaders to have excellent communication, team management and strategic planning skills.

Insufficient education and training

At present, education and training systems tend to focus on in-depth learning in a single field and lack an integrated education model across fields. This results in most professionals having deep expertise in one area but relatively weak expertise in others. To develop leaders with triple competencies, education systems need to be reformed to provide more interdisciplinary learning opportunities.

Complexity of industrial applications

The application of AI large models in industry is not only a technical issue, but also involves many aspects such as business models, laws and regulations, ethics and morals. These factors increase the complexity of AI large model applications, requiring leaders to have not only a technical perspective, but also a business and social perspective. This ability to think in multiple dimensions is a huge challenge for most leaders.

Conclusion

The application of large AI models in industry requires leaders with the triple capabilities of business, programming and AI technology. However, such leaders are scarce due to deficiencies in the education system, challenges in cross-domain collaboration, and the complexity of industrial applications. In order to promote the application of large AI models in industry, we need to reform the education system to provide more interdisciplinary learning opportunities, while encouraging and supporting cross-field collaboration and innovation. Only in this way can we see large AI models unleash their huge potential in various industries.

Through in-depth analysis, we can conclude that the application of large AI models in industry is not a technical problem, but a human problem. Only when we cultivate more leaders with triple capabilities and establish effective cross-domain collaboration mechanisms can AI large models truly play their due role in the industry. It will take time, resources and sustained effort, but ultimately, it will bring about far-reaching impact and change.