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2025 Enterprise AI Application Outlook

Authors
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    Name
    LI Tian
    Twitter

As a software engineer dedicated to the AI field, I have accumulated extensive practical experience over the past two years. Combining the technological breakthroughs of companies like OpenAI and Anthropic in 2024, along with the forward-looking speeches by Andrew NG and Sam Altman and others, I would like to share some insights and predictions regarding enterprise-level AI applications.

Observations on Technological Trends

  • Foundation Models as a Service: While chatbots like ChatGPT have made significant progress, they still fall short in addressing real-world problems. The demands of the real world are complex and diverse, requiring more detailed solutions.
  • Competition in Foundation Models: This is a race within the data science field. Despite the varied focus of media reports, my attention is on how to efficiently utilize these APIs to complete tasks.

Challenges Faced by Enterprises

  1. High Token Costs: As AI applications deepen, token consumption surges, leading to rising costs. However, I believe that with increasing market competition, these costs will gradually decrease.
  2. Challenges in Private Deployment: To protect data security, many companies prefer to deploy large models privately. However, this often comes with expensive hardware costs. The "distillation" technology proposed by DeepSeek offers a new solution to this dilemma, enabling small models to possess the capabilities of large models, thereby significantly reducing hardware requirements.
  3. Industry Knowledge Barriers and Lack of Mature Development Models: Basic large models struggle to meet the requirements of enterprise processes and precision because they lack industry-specific knowledge bases and procedural support. Additionally, current software development tools and frameworks are not yet mature and do not provide a set of convenient templates or patterns for developers to use. Looking back at the history of software development, whenever new technologies give rise to effective development models (such as design patterns), it significantly promotes advancements in the relevant fields. For AI applications, developing a mature AI App development model will be a crucial step. Only then can companies more easily build efficient, customized AI solutions and encourage non-IT enterprises to experiment with and innovate in enterprise-level AI applications.

Exploring Solutions

In 2025, we can address these challenges through the following approaches:

  • Deepen Research on Distillation Technology: Learn and apply DeepSeek's open-source distillation methods to develop small, efficient models tailored to enterprise needs.
  • Optimize Private Deployment Strategies: Explore different deployment schemes, including enterprise data centers and cloud services, to find the most cost-effective solutions.
  • Promote the Development of Agent Frameworks: Combine existing frameworks to develop Agent solutions that are more suitable for enterprise application scenarios, lowering the usage threshold and increasing adoption rates.

Looking Ahead

My expectation for 2025 is to promote the development of enterprise-level AI applications through the aforementioned methods, achieving great capabilities with small models, and finding practical enterprise models in Agent development. This will not only enhance the competitiveness of enterprises but also lay a solid foundation for the widespread application of AI technology.