AI & Technical question

Glean can pull structured and unstructured data from systems like ServiceNow, Zendesk, GitHub, and Microsoft Teams. If you were building a leader-facing intelligence feature on top of that data, what architecture and launch tradeoffs would you make around permissions, identity resolution, data freshness, source reliability, personalization, and explainability?

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What this question tests

Tests systems level thinking on architecture and launch tradeoffs for a cross source leader facing intelligence feature built on enterprise data.

Clarify the target insight, for example a leader facing summary of team blockers pulled from ServiceNow, Zendesk, GitHub, and Teams.
Design around permissions first, since the feature must respect the most restrictive access a user has across every underlying system.
Address identity resolution, matching a person's handles across systems, and decide how confident a match must be before merging activity.
Weigh freshness against reliability, since GitHub updates near real time while ServiceNow may batch sync, so staleness must be shown per source.
Build explainability in from day one, always showing which source record backs a claim, since leaders will not act on an unexplained conclusion.

Treats permissions as a hard architectural constraint, filtering at the most restrictive source level rather than aggregating first and filtering later.
Calls out identity resolution as a real risk, since a low confidence match could merge two people's activity, and proposes a conservative threshold.
Proposes surfacing freshness explicitly, labeling an insight as current as of a given time when one source lags.
Ties explainability to trust, linking every insight back to the specific tickets or messages that generated it.

Designing the aggregation pipeline before solving permissions, which risks leaking access across systems.
Treating all source systems as equally fresh and reliable instead of accounting for sync lag and data quality differences.