Subtitles for Business Video: Where AI Falls Short

Automatic captions are now a button on every platform, and the output is good enough that most businesses publish it unread. The trouble is that the words a transcription model gets wrong are not randomly distributed. They cluster on names, products, prices and jargon, which is a precise description of the content a business video exists to deliver.

Where the errors concentrate

Plausibility is the real problem. Transcription errors arrive polished rather than garbled. A model confronted with a product name it has never seen substitutes a real word that sounds similar, and the result reads as a confident sentence that happens to be wrong. Nothing on the screen signals that this line needs checking, which is the same loss of surface cues described in quality assurance when AI writes the first draft.

A review pass that takes minutes

Reading every caption line defeats the point of generating them automatically. Reviewing six categories does not. Names of people. Company and brand names. Product names and codes. Numbers, prices and dates. Industry terms. Anything stated as a commitment or a guarantee.

Most caption editors let you search the transcript, so the pass is a series of searches rather than a read-through. Three minutes on a five-minute video, concentrated precisely where the errors are.

Where the same terms recur across your videos, keep a short glossary of correct spellings and feed it to whichever tool you use, since most now accept a custom vocabulary. That turns a repeated manual correction into a one-time setup, which is the general shape of worthwhile automation.

Captions, not just subtitles

Subtitles render dialogue. Closed captions also identify speakers and describe meaningful non-speech audio, and the viewer can switch them on or off. Accessibility expectations are generally written around captions, so producing subtitles and calling the accessibility question answered leaves a gap.

Obligations in Canada depend on who you are. Ontario's accessibility rules place web content requirements on many organisations above a size threshold, and federally regulated organisations have duties under the Accessible Canada Act. Those are different regimes with different triggers, so check which applies to you rather than inferring from another business. This is general information rather than legal advice, and the broader picture is in accessible AI and serving every customer.

Setting that aside, a large share of social video is watched with the sound off. Uncaptioned video loses those viewers whatever the rules say, which makes captions a distribution decision before it is a compliance one.

The French question is not just translation

Machine translation of captions handles straightforward content competently. It struggles with the things that separate good French from merely correct French: register, Quebec usage against European usage, and the terminology your industry has actually settled on.

For customer-facing video, treat machine output as a first draft for a reviewer who speaks the language. Publishing awkward French reads worse than publishing English alone, because it signals effort without care, and that judgment lands harder with the audience you were trying to reach.

Timing also shifts in translation, since French runs longer than English for the same content and captions that were readable become captions that flash past. Any translation pass needs a timing check after it, which the real-time version of this problem also runs into, as covered in real-time AI voice translation.

What captions give you besides access

A caption file is a timestamped transcript, which makes video searchable by content. That is the cheapest route to answering which minute covers a given step, and it is the same capability discussed in video search for business without any new tooling.

The transcript is also reusable material. A training video becomes a written procedure, a recorded webinar becomes a blog post, a product demo becomes help documentation. Most businesses generate this text automatically and then discard it, which is the recurring theme of getting more out of content you already made.

Start with the videos people actually watch

Captioning an entire back catalogue is the version of this project that does not get finished. Take your five most-viewed videos, caption and review those properly, build the glossary while you do it, and make captioning part of publishing from now on.

Older material can be caught up with automatic captions unreviewed, which is better than nothing and honest about what it is. Where a video carries pricing or commitments, it belongs in the reviewed pile regardless of its view count.

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