Session Replays + AI: Where the Market Is Going (And Why We’re Taking a Different Route)
Session replay platforms are racing to slap “AI-powered” on their websites, promising faster insights, better prioritization, and fewer hours of manual review.
But if you dig under the buzzwords, most of what’s happening today is AI as a sidecar - not a true rethink of how session replays should work.
We’ve spent the last year building Autoplay on top of real-world session replay data, and the patterns are clear: the current “AI wave” in analytics is missing the mark.
Here’s why - and what we’re doing differently.
The Current AI Landscape in Session Replays
The big players - FullStory, Contentsquare, Hotjar - are all circling the same promise:
“Watch fewer replays, get to insights faster.”
But under the hood, most of these AI features fall into three predictable buckets:
Session Summaries
AI reads a session replay and spits out a transcript or bullet-point summary. It saves a bit of time, but you still have to sift through noise, watch clips, and guess why something happened.
Anomaly Detection
AI flags sessions that look “unusual” - rage clicks, error loops, dead clicks. Useful for debugging, but not actionable for deeper product decisions. You know what went wrong, but not why users behaved that way.
Dashboards with Extra Flair
Some tools layer AI over funnels or heatmaps, highlighting “interesting patterns” without connecting them to real user intent. It’s still the same dashboards, just with an AI highlighter pen on top.
🧐 The result? Incremental improvements.
You save a few hours watching videos, but you don’t fundamentally change how product teams learn from behavior.
The Problem With This Approach
These tools start with the assumption that session replay is for watching sessions.
So their AI goal is simply to watch faster.
But watching sessions is the bottleneck.
The real opportunity is making that step optional.
The point of AI shouldn’t be to speed up the old workflow - it should be to replace the need for it. To extract the full story of user intent, knowledge, friction, and decision-making without needing hours of video review or guesswork.
👉 Instead, most platforms today are giving you a faster horse, not a car.
What We’re Building Instead
Autoplay flips the model.
We don’t treat AI as an assistant for manual review - we treat it as the engine for effective hypothesis testing about your users’ behaviour
We’re training AI to:
Detect intent - What goal was the user trying to accomplish in that session?
Understand knowledge level - Did they know what they were doing, or were they exploring blindly?
Spot hesitation - Where did confidence break down? Where did they pause, hover, or go in circles?
Uncover friction - Where did their journey collapse - not just technically, but cognitively?
Surface workarounds - What did users do when the product didn’t work the way they expected?
This isn’t just about surfacing “better” insights.
It’s about giving product teams a totally different way to investigate.
A Better Hypothesis Testing Workflow
Most PMs today start with a gut feeling - then go hunt for sessions that might validate or disprove it.
The process is slow, subjective, and noisy.
We turn that on its head.
With Autoplay, you either start with a hypothesis - and instantly filter to the sessions that matter - or we give you one based on patterns we’ve already detected through unsupervised learnign techniques.
It’s not just about finding interesting moments.
It’s about making sure product teams are working on problems that keep happening, across many users, not just one-off anecdotes.
Autoplay becomes a tool for high-quality hypothesis testing, at scale.
Want to know if users struggle with team invites? Filter sessions where invite flows triggered hesitation or abandonment.
Curious if your new dashboard is confusing? Pull sessions where users paused, backtracked, or gave up.
Trying to validate a UX improvement? Compare sessions pre- and post-change with intent and friction tags.
No more guessing. No more endless clip reviews.
Just clean behavioral data tied directly to what matters.
Where This Goes Next
We believe the future of session replay + AI isn’t about watching better.
It’s about building products that can self-report on their own usability - tools that tell you where users struggle, where they hesitate, and what’s holding back adoption.
AI shouldn’t just mimic a faster human reviewer.
It should become a behavioral analyst in your product, working 24/7, surfacing what matters.
It should be a decision layer sitting between your users and your roadmap - helping you understand not just what users did, but what they tried to do, where they got stuck, and what they did instead.
🌟 That’s what we’re betting on at Autoplay.
And if we get it right, manually watching replays will feel as outdated as reading server logs to understand user intent.