What Did You Use AI For? The Question Leaders Should Ask

A debut crime novelist lost a $2 million publishing contract in July after rumors surfaced that artificial intelligence helped write the manuscript. The author denies the allegations, and fourteen publishing houses had already bid on the work based on its quality. Yet concerns about AI involvement alone killed the deal.

This incident marks at least the third major AI-related scandal in publishing this year, according to Fast Company. While readers' concerns about authenticity in creative work are legitimate, the backlash is fueling a broader workplace anxiety: employees now fear being caught using a tool they're simultaneously told they must master.

When Anthropic announced that Claude would embed invisible watermarks in AI-processed text, the response split sharply. Some users applauded the transparency measure. Others canceled subscriptions, worried about being branded as AI users regardless of how they employed the tool.

The workplace double bind

Employees face contradictory pressures. Avoiding AI tools entirely risks falling behind productivity expectations and drawing criticism from pro-AI leadership. Using them invites potential stigma from colleagues and managers who view any AI assistance as cheating.

This tension stems from asking the wrong question. "Did you use AI?" treats the technology as a binary moral choice rather than a versatile tool with multiple applications.

Why it matters

Business leaders who fail to distinguish between appropriate and inappropriate AI use will either stifle productivity gains or inadvertently encourage misuse. The path forward requires moving past blanket judgments about AI involvement to evaluating what specific tasks the technology performed and whether that use aligned with the work's purpose.

Reframing the conversation

Fast Company reports that organizations should focus on a more nuanced question: "What did you use AI for?"

This shift acknowledges three distinct dimensions leaders need to evaluate in any business output:

Quality and accuracy: Does the work meet standards regardless of production method? The publishing houses that bid millions for the crime novel judged the manuscript's quality before AI concerns emerged. The words themselves hadn't changed.

Process alignment: Does the AI use match the work's intended purpose? Using AI to generate first drafts of routine communications differs fundamentally from using it to produce work meant to showcase original human thinking.

Stakeholder expectations: Does the output fulfill what clients, colleagues, or customers expect? A reader purchasing a novel expects human creativity. A manager requesting a data summary expects accurate information, not necessarily manual compilation.

The current moment demands that leaders establish clear guidelines matching AI use to work objectives rather than imposing blanket bans or uncritical adoption. Organizations that thoughtfully define appropriate AI applications for different tasks will capture productivity benefits while maintaining quality and trust.

These details were first reported by Fast Company.