AI's most practical value for professionals may lie not in replacing creative work, but in automating the organizational chaos that consumes hours before the actual writing begins. Technology journalist Joanna Stern spent a year systematically testing AI tools across her professional life and discovered that the real productivity gains came from using AI to organize research, track deadlines, and retrieve information from her own materials, rather than from AI writing or brainstorming assistance.

What Did Joanna Stern Actually Use for Her Book Project?

Stern relied on ChatGPT and Claude, specifically their "Projects" features, to manage the overwhelming volume of materials that accumulate during a major writing project. She uploaded research notes, academic papers, interview transcripts, editor feedback, and deadline reminders into these tools, creating what she calls "BookBots." These custom AI assistants then helped her stay organized throughout the book-writing process.

The distinction matters because these tools weren't used to write the book itself. Stern explicitly stated that she wrote the entire book herself. Instead, the AI handled the unglamorous but essential task of keeping track of what she had researched, when deadlines were approaching, and which experts or sources were worth pursuing based on her accumulated notes.

How Can Professionals Use AI Projects for Complex Work?

• Research Organization: Upload academic papers, interview transcripts, and background materials, then ask the AI to summarize findings or identify patterns across multiple sources without manually re-reading everything.
• Deadline Tracking: Feed the tool your project timeline, editor notes, and task lists, then ask it to remind you what you should be working on next or flag upcoming deadlines automatically.
• Expert Identification: Store research notes and company information, then ask the AI to recommend which sources or experts are worth pursuing based on your accumulated research.
• Information Retrieval: When you need to find a specific detail buried in months of notes, ask your custom AI assistant to locate it rather than manually searching through files.

"I used the tools to create my 'BookBots.' I uploaded research notes, academic papers, transcripts, deadlines, editor notes and more. Those BookBots kept me on track and made it easy to find things when I was writing. If I had questions about deadlines, what I should be working on, or which companies or experts my research suggested were worth talking to, I asked the BookBots," explained Joanna Stern.

Joanna Stern, Technology Journalist and Author

Stern's approach reveals a crucial insight about AI productivity tools: they work best when solving the organizational problem rather than the creative problem. The time-consuming aspects of complex projects often aren't the actual writing or thinking, but the administrative overhead of remembering which expert said what, tracking which sources support which claims, and keeping deadlines visible across dozens of documents.

Why Is This Different From Earlier AI Productivity Tools?

Earlier AI productivity tools focused on writing assistance, brainstorming, or general task management. The Projects feature in ChatGPT and Claude takes a different approach by letting professionals create a custom AI trained on their specific documents and project context. The tool doesn't attempt to write your book, article, or report; instead, it helps you navigate the research and organizational chaos that precedes the actual writing.

After a year of experimenting with various AI tools, Stern found that certain applications stuck with her workflow. She noted that she talks to ChatGPT regularly via voice mode while driving, and uses Meta AI through her Meta Ray-Bans for quick questions. However, the Projects feature proved most valuable for sustained, complex work where she needed to reference her own accumulated materials.

The broader implication is that AI's most practical near-term value may lie in automating the organizational and retrieval tasks that consume time without requiring creative judgment. For journalists, researchers, academics, and other professionals managing complex projects, this represents a shift toward AI tools designed around how people actually work, rather than tools that attempt to do the work itself.