DeepSeek-R1 has captured the attention of the AI community with its reasoning capabilities and experimental use, but consensus on its optimal applications is lacking, as shown by a survey of over 500 AI professionals. Despite 57% of respondents experimenting with the model, only 3% have deployed it in production, highlighting challenges such as high operational costs, integration difficulties, and performance variability. Its potential is noted in specialized use cases like medical data analysis and legal document summarization, yet uncertainty about its performance compared to other models remains, with 47% of practitioners unsure of its advantages. There is a strong desire among AI teams for more customization options, with 46% expressing interest in fine-tuning, indicating a need for flexibility to tailor the model to specific needs. The current mixed sentiments suggest that while DeepSeek-R1 shows promise, its practical adoption requires further innovation and adaptation by AI teams willing to experiment and fine-tune the model.