Design AI features for Slack (knowledge, search, and automation).

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ANSWER MODE

WRITTEN ANSWER

### Signal to interviewer

I can design enterprise collaboration AI that improves knowledge flow without compromising permission integrity.

### Clarify

I would clarify workspace size, tool integrations, access model, and which coordination pain points are highest priority.

### Approach

Build a workspace memory system: permission-scoped search, thread-to-knowledge summarization, and workflow automation templates.

### Metrics & instrumentation

Primary metric: repeat-question reduction in active teams. Secondary metrics: answer citation usage, automation adoption, and time-to-context for new members. Guardrails: permission violations, stale knowledge responses, and workflow misfires.

### Tradeoffs

Broader indexing improves answer coverage but raises confidentiality risk. More automation increases efficiency but can create brittle workflow dependencies.

### Risks & mitigations

Risk: stale context in summaries; mitigate with freshness triggers. Risk: access leaks; mitigate with strict ACL enforcement. Risk: low trust in AI outputs; mitigate with mandatory citations.

### Example

In engineering incident channels, AI summarizes root-cause updates, suggests postmortem tasks, and answers recurring stakeholder questions with source links.

### 90-second version

Design Slack AI as permission-safe organizational memory. Optimize repeated-question reduction and workflow acceleration while preserving access boundaries and source transparency.

FOLLOW-UPS

Clarification

Which workspace use case has the highest cost from repeated questions?
How should permission boundaries be exposed to users in AI answers?

Depth

How would you keep thread summaries fresh without excessive compute?
What ACL checks must run in retrieval and response synthesis layers?

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