‘Did you use AI?’ is the wrong question. Ask ‘What did you use AI for?’

As the publishing world grapples with the implications of artificial intelligence in creative works, a new perspective has emerged: instead of asking "Did you use AI?", businesses should focus on "What did you use AI for?". This shift in questioning acknowledges the tool's potential and encourages a more nuanced approach to its application.

In a business context, three key aspects of work should be assessed: quality, capability, and development. These aspects are no longer directly tied together by AI's involvement, which can produce excellent results without enhancing an individual's skills. Traditional metrics of quality no longer serve as reliable indicators of an individual's ability, as AI can generate high-quality output independently.

Instead of relying on AI's provenance to judge an employee's performance, leaders should set clear criteria for what constitutes "good work" and evaluate the output based on these standards. It is essential to hold employees accountable for their work, regardless of the tools they employ. The AI's role should not exempt individuals from responsibility for errors or subpar performance.

However, AI can provide valuable insights into an individual's capabilities, particularly in job interviews and promotion discussions. Rather than merely asking whether AI was used, leaders should engage employees in discussions about the work, encouraging them to explain their choices and justifications. This approach helps assess an individual's problem-solving skills, critical thinking, and creativity, even when AI is involved in the process.

It is crucial to recognize the limitations of AI detection methods. Current tools, like Anthropic's watermark, can only determine if AI was involved at some point, not the extent of its contribution. Leaders should avoid turning every output into a "purity test" and instead focus on testing employees directly to evaluate their skills and potential.

While AI can streamline certain tasks, efficiency should not be the sole priority in development. Learning by doing remains vital, and efficiency should be sacrificed to foster growth and skill development. Businesses must navigate the delicate balance between embracing AI's potential and ensuring that employees continue to learn and grow through hands-on experience.