AI to the Rescue: Months of Podcast Repairs Done in 42 Minutes

This is a real-world account of how artificial intelligence audited a 234-episode podcast catalog, repaired dozens of titles and summaries, and preserved a legacy project without removing human judgment from the process. AI helped uncover and repair a much larger podcast problem than expected.

The Project Began With a Family Legacy

This project began with a collection of sermons recorded by Reverend Jack Hamilton during the 1980s and 1990s. The messages had been preserved on cassette tapes and later converted into digital audio. The goal was simple but meaningful: turn the recordings into an organized podcast archive that could continue reaching listeners for years to come.

The work required much more than uploading audio. Each episode needed to be reviewed, introduced, combined with supporting material, described accurately, and scheduled for distribution. Completed manually, the workflow moved slowly.

The Original Workflow

1. Listen to each sermon and identify its central message
2. Write and record a suitable introduction
3. Combine the introduction and sermon audio
4. Create a clear episode title and description
5. Upload the finished episode and schedule its release

During the first year, only about 10 to 15 episodes were completed. With hundreds of recordings available, the pace made it difficult to imagine finishing the archive.

AI Changed the Scale of the Project

The workflow was redesigned so AI could handle much of the repetitive production work. It helped transcribe sermons, draft and voice introductions, assemble audio, create episode titles and descriptions, upload files to Simplecast, and schedule releases.

The result was a dramatic increase in output. Approximately 250 episodes were prepared and scheduled through 2028, with about 800 sermons available for future work. What had once seemed like a project that could take years became manageable.

Then a quality problem surfaced. I believed one title had accidentally been repeated about ten times. I expected a small cleanup. Instead, the AI audit showed that the issue was much larger.

The Audit Uncovered the Real Problem

AI examined all 234 completed and scheduled episodes. It compared episode titles, descriptions, dates, audio durations, production records, and original source material. The audit uncovered 23 repeated-title groups affecting 93 episodes.

The repeated titles did not necessarily mean the audio was duplicated. In many cases, completely different sermons had been given the same title. That distinction mattered: the repair required understanding the content of each sermon rather than simply renaming files.

How AI Repaired the Catalog

The repair process required AI to:

1. Locate the original production folders and source files
2. Inventory the completed and scheduled podcast episodes
3. Compare titles, descriptions, dates, and audio durations
4. Separate true duplicate episodes from different sermons sharing one title
5. Return to the original sermon transcripts
6. Identify the unique message within each sermon
7. Write accurate replacement titles and summaries
8. Update the production records and matching Simplecast episodes
9. Preserve the correct audio files and scheduled publication dates
10. Refresh the catalog and verify that each correction had saved

One failed save was caught during verification and repaired. That final check was important. Automation is useful only when the results are reviewed and confirmed.

The Measurable Result

AI replaced and verified 54 episode titles and summaries in 42 minutes and 6 seconds of active work. A separate 29 minutes and 59 seconds of waiting time, while I took a walk, was excluded from the active repair time.

• Episodes audited: 234
• Repeated-title groups initially found: 23
• Episodes affected by the initial problem: 93
• Titles and summaries repaired and verified: 54
• Active repair time: 42:06
• Episodes now carrying unique titles: 202

A final catalog review confirmed that 202 of the 234 episode titles are now unique. Sixteen repeated-title pairs remain for a separate repair phase that was intentionally not completed during this round.

Human Oversight Remained Essential

The AI did not complete this work without direction. I corrected it when it selected the wrong drive, identified a timing mistake, told it to continue after it paused for a question, and verified that the saved changes actually remained in place.

That is the practical lesson from this project. AI can manage repetitive, detailed work at a scale that would overwhelm one person, but it still needs a human to define the goal, recognize mistakes, make judgment calls, and approve the result.