How Do We Use Generative AI? Automation or Collaboration? Insights from Claude’s Data

Artificial intelligence is increasingly integrating into work processes, but the key question remains the same – are we replacing human labor with AI, or enhancing our work through collaboration? The company Anthropic, which develops Claude.ai, conducted a study analyzing over a million user interactions to determine whether people fully delegate tasks to AI or work alongside it while maintaining control over the process.

According to the analysis, in 57 percent of cases, users actively collaborate with AI, seeking suggestions, guidance, or corrections, while in 43 percent of cases, they fully delegate tasks to it with minimal involvement.

Among collaborative interactions, the most common use case is task refinement, accounting for 31 percent of instances. In these cases, AI helps users refine ideas, edit texts, and improve content. For example, AI can enhance a marketing text by making it more persuasive or suggesting additional data points.

The second most frequent use case is the learning process, covering 23 percent of interactions. Users turn to AI to understand complex topics, get explanations, and acquire new knowledge. A student may ask AI for a detailed discussion of a historical event, a programmer may request an explanation of how an algorithm works, and a business analyst may seek a brief economic trend analysis.

The third type of interaction is validation, accounting for 3 percent of cases. Here, users ask AI to verify work they have already done, whether it’s checking code, statistical analysis, or logical consistency in a written piece. For instance, a data analyst may ask AI to review an SQL query and identify potential errors.

On the automation side, where users assign tasks to AI with minimal involvement, the most frequent use case is technical task execution, which comprises 27 percent of cases. Users rely on AI for specific tasks such as converting text into Markdown format, generating template-based reports, or drafting standardized emails.

The second major use case is error correction, making up 15 percent of interactions. Programmers and data analysts frequently use AI to debug code or troubleshoot technical issues. For instance, a user may send a code snippet and ask AI to identify the cause of an error and suggest potential fixes.

These findings indicate that AI serves more as a collaborative partner than a complete replacement for human work. While automation plays a significant role, collaboration remains dominant. The fact that users actively engage with AI rather than merely delegating tasks suggests that AI is perceived more as an intelligent assistant that enhances productivity rather than a tool that fully replaces human effort.

It will be interesting to observe whether AI maintains this role in the future or if, with further advancements, it shifts more towards complete automation. For now, the data suggests that humans still hold the primary decision-making power, while AI acts as an intelligent aid that makes work more efficient and flexible.