AI transcription for business: common questions on accuracy, privacy and risk

How accurate is AI transcription?

It depends on audio quality, accents, vocabulary, and the model. Published figures put strong speech models at roughly 2 to 4 wrong words per 100 on clean benchmarks. Hard clips with noise or overlapping speakers do worse, and accuracy figures from a benchmark rarely transfer exactly to your recordings.

Why do transcripts sometimes include words nobody said?

Generative models predict likely text. When audio is unclear or silent, they can fill the gap with a plausible sentence. This is why fluent output still needs checking.

Which errors matter most?

Names, numbers, dates, drug names, and direct quotes. A wrong word in casual conversation is minor. A wrong figure in a contract or clinical note is not.

Can I trust it for regulated work?

Only with controls. In sectors such as healthcare and banking, keep the source recording, require human review of high-risk items, and keep an audit trail of who approved what.

Is it safe to upload confidential recordings?

Check where audio is stored, whether it is used to train models, how long it is retained, and who can access it. Ask vendors for these answers in writing, and involve your privacy and security teams.

What should I ask a vendor?

• Does the tool mark uncertain words?
• Can I jump from any line to the source audio?
• Does it transcribe exactly, or tidy the text?
• What are the data retention and privacy terms?

Do I still need a human reviewer?

For anything consequential, yes. Automated checks help, as described in validation layers for enterprise AI, but they do not replace judgment.

Where can I learn more about responsible use?

Our Responsible AI-by-Design Framework outlines how we approach oversight, privacy, and fairness.