In 2026, the application of large models in the industry is undergoing a critical turning point from "universal dialogue" to "vertical deep cultivation". After more than a year of technological iteration and scenario exploration, the landing path of AI big models in various industries is becoming increasingly clear.
In the financial field, big models have evolved from simple intelligent customer service to deep business assistants. A financial big model fine tuned based on industry knowledge base, capable of real-time analysis of financial report data, assisting risk assessment, and generating compliance reports. After a leading securities firm introduced AI investment research assistants, the efficiency of analyst information processing increased fourfold and the research report output cycle was shortened by 60%.
The field of healthcare has also seen breakthroughs. The medical big model demonstrates professional level capabilities in auxiliary diagnosis, drug development, and medical record structuring through the training of massive cases and medical literature. Several tertiary hospitals have piloted AI assisted diagnostic systems, and the accuracy of image recognition has approached the level of senior experts.
In the field of education, we are exploring a collaborative model of "AI teachers+real teachers". The large model can automatically generate personalized learning paths based on students' answer data, intelligently identify weak points in knowledge, and provide targeted exercises. After piloting an AI math tutoring system in a middle school in Beijing, students' average grades improved by 15%.
However, vertical field implementation also faces challenges: difficulty in obtaining industry data, high annotation costs, and strict compliance requirements. The solution lies in "taking small steps and running fast" - selecting high-value and low-risk scenarios for verification first, gradually expanding the application boundaries. At the same time, the maturity of RAG (Retrieval Enhanced Generative) technology enables large models to access enterprise private knowledge bases without retraining, significantly reducing the customization threshold.
This article is a comprehensive compilation of AI application practice reports publicly released by various industries.