Last month, a team asked me to evaluate whether AI could replace their manual video review process. They had four people watching customer support call recordings, tagging issues, and writing summaries for eight hours a day. I said yes and built a prototype.
The prototype worked beautifully on the first three test clips. Then I ran it against their actual library and it confidently told me a customer was “demonstrating frustration through aggressive keyboard usage.” The customer was typing their account number. The model was hallucinating emotional context from audio artifacts.
That experience captures video AI right now. It’s genuinely capable. It’s also confidently wrong in ways that are hard to predict and even harder to catch at scale.
Video isn’t just “lots of images”
The fundamental challenge with video understanding is time. An image model looks at a single moment. A video model has to track what happened, in what order, and how things changed. That temporal reasoning is where models still struggle.
The practical failure modes I’ve seen:
- Temporal confusion. The model describes events out of order or merges two separate moments into one. This is especially bad with longer clips.
- Missing key moments. The model summarizes the overall vibe of a clip but misses the specific 10-second window where the important thing happened.
- Overconfidence. The model narrates with authority even when it’s guessing. No hedging. No “I’m not sure.” Just wrong with conviction.
The pipeline that actually works
Forget single-prompt video understanding. It doesn’t scale. What works is a pipeline that breaks the problem into stages you can debug independently.
Here’s the architecture I landed on:
Step 1: Extract audio and transcribe. If the video has spoken content, the transcript is your primary signal. Audio transcription is a solved problem, and the output is reliable. Start here.
Step 2: Sample frames intelligently. Not every N seconds. Use scene detection to identify transitions, then sample the first frame of each scene plus any frame with significant visual change. This reduces the frame count by 60-80% without losing meaningful content.
Step 3: Analyze frames with context. Feed each frame to a vision model along with the surrounding transcript text. The transcript grounds the visual analysis and prevents the model from inventing narratives that don’t match what was said.
Step 4: Synthesize with timestamps. Merge the transcript-grounded visual analysis into a structured timeline. Every claim in the summary must reference a specific timestamp. If the model can’t cite when something happened, it probably didn’t happen.
The key insight: audio-first, video-second. The transcript is your source of truth. The video adds context. Not the other way around.
Where it’s actually useful
After the initial disaster and a week of pipeline tuning, I found the sweet spots:
Meeting summaries with action items. Transcribe, extract decisions and action items, tag them with speaker and timestamp. This works well because the transcript carries most of the signal and the visual component (slides, screen shares) adds structure.
Content moderation. Checking video against a specific policy with concrete criteria. “Does this clip contain product logos?” “Is the speaker reading from a teleprompter?” Questions with binary answers that the model can ground in visual evidence.
Search and retrieval. “Find the part of this recording where they discuss pricing.” Natural language search over video libraries works surprisingly well when you have good transcripts and frame-level annotations.
Compliance review. Structured checks against a rubric. Did the agent identify themselves? Did they read the required disclosure? Was the customer’s consent recorded? This works because the criteria are specific and verifiable.
Where it isn’t ready
Long-form video without speech. Surveillance-style footage. Anything where the important signal is subtle body language or spatial relationships. Anything where the model needs to count reliably or track specific objects across many frames.
Also, anything where a false positive has real consequences. If your video review pipeline flags a customer interaction as “hostile” and that triggers an HR process, you had better have a human in the loop.
Starting without overbuilding
Pick one use case. Keep clips under 10 minutes. Fix your output format before you start – structured JSON, not free-form prose. Build a gold set of 20-30 annotated clips and run every pipeline change against it.
The evaluation loop is everything. Without it, you’re optimizing by vibes, and vibes don’t catch temporal hallucinations.
Video AI is real and useful for the right problems. Just don’t let the first impressive demo convince you it’s ready for the hard ones.