Inquirer's manual article-tagging process was inconsistent and inefficient. With over 300 different election-related tags in their database—many of them duplicates or slight variations—it was difficult to track coverage and analyze content performance accurately. It took the team 32 minutes to recategorize 100 articles, creating a significant workflow bottleneck, particularly for important and extensive election coverage.

Using Gemini, the Inquirer team analyzed their list of duplicated and wrong tags, and created a standardized "holy list" of just 19 categories for election coverage. They then developed an AI-powered tool that could automatically categorize 1,500 articles in just 30 minutes—a task that would have taken over 12 hours to complete manually. The AI tagger achieved 90% accuracy, far surpassing the consistency of the previous human-led process. This tool promises to make both the data and editorial teams more efficient, allowing for a more robust and streamlined analysis of all content.