Product teams collect a lot of data. What they rarely have is enough time to find what matters in it.

AI Insights in product analytics solves that problem directly. Instead of requiring analysts to build queries, watch session replays manually, or dig through dashboards for signals, AI Insights monitors user behaviour continuously and surfaces the patterns worth acting on. It's the difference between reactive analysis and proactive signal detection.

AI Insights Defined

AI Insights are AI-generated observations about user behaviour that have been grouped, scored, and prioritised for product teams. They are not raw data — they are conclusions drawn from data.

In Adora, Insights are distinct from raw Signals. A Signal is a single behavioural observation: a rage click, a dead click, a failed payment, an error loop. An Insight is a group of related Signals that AI has identified as a meaningful pattern, assessed for frequency and impact, and surfaced with enough context to act on.

Each Insight in Adora is scored by impact level:

• Information — a pattern worth noting but not urgently actionable
• Minor — a friction point affecting a small number of users
• Issue — a significant friction point with meaningful impact
• Major — a high-frequency, high-impact problem warranting immediate attention

How AI Insights Analyse User Behaviour

Signal Detection. Adora records every user session. Rage clicks, dead clicks, excessive cursor movement, and sudden abandonment patterns are detected without manual configuration.

Signal Clustering. Individual signals become meaningful at scale. A single rage click might be a one-off. Five hundred rage clicks on the same element across three hundred users in the past week is a clear problem. AI groups signals based on what's happening, where it's happening, and who it's happening to.

Impact Scoring. Once a pattern is identified, AI applies scoring across two dimensions: frequency (how often it occurs) and severity (how much it disrupts the user journey). This scoring turns a queue of observations into a prioritised to-do list.

Contextual Linking. Every Insight links to the underlying session replays. You can read a scored Insight and immediately jump to representative sessions showing exactly what users experience in that moment.

What AI Insights Mean for Different Roles

For Product Managers. AI Insights feed directly into prioritisation. Product managers start with evidence: an Insight scored as Major, affecting a named segment, backed by session replay evidence. The Linear integration lets them create a ticket directly from an Insight, with session evidence pre-filled.

For UX Designers. Designers don't need to wait for user research cycles to understand where users struggle. The Insights dashboard provides a continuous stream of observed problems grounded in real user sessions.

For Engineering Teams. AI Insights can surface bugs that would otherwise go undetected through normal error monitoring. An error loop that doesn't throw a server-side exception still generates a clear pattern of signals.

For Growth and CRO Practitioners. The Insights queue provides a ready-made list of conversion blockers. Major and Issue-scored Insights in checkout flows, trial-to-paid upgrades, or feature activation sequences are directly mapped to revenue impact.