AI transcription has genuinely gotten very good. For clear audio with one speaker and no background noise, automated tools now get most of the words right on the first pass. So why not just use the raw output?
AI is excellent at the easy 80%
Clear, single-speaker, well-recorded audio in a common accent is close to a solved problem. If that's all a transcript needed, a human reviewer would be redundant.
And still unreliable on the hard 20%
The remaining fifth of most real-world recordings is where automated transcription breaks down: overlapping speakers, technical or industry-specific jargon, unfamiliar names, accents underrepresented in training data, and background noise. AI doesn't know when it's wrong — it just confidently outputs the wrong word, formatted identically to the words it got right. There's no visual cue telling you which parts to double-check.
The hard 20% is usually the part that matters
A misheard filler word barely matters. A misheard number in a financial interview, a misattributed quote in a legal recording, or a garbled technical term in a research transcript can genuinely change what the document means. The errors don't distribute evenly across importance — they cluster exactly where getting it wrong is costliest.
What a human reviewer actually catches
- Misheard homophones and near-homophones ("there" vs. "they're," a company name vs. a common word)
- Speaker mislabeling during cross-talk or quick exchanges
- Domain-specific terms an AI model has never encountered
- Sentences that are technically transcribed correctly but don't make sense in context — a strong signal something was misheard
This is why every file that leaves Apex Transcript gets an AI first pass for speed, followed by a human pass against the actual audio for accuracy. Neither step alone is enough. Together, they're what separates a usable transcript from a rough guess.