Design AI for enterprise productivity (docs, meetings, and tasks).

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ANSWER MODE

WRITTEN ANSWER

### Signal to interviewer

I can design enterprise AI that closes the gap between collaboration artifacts and execution outcomes.

### Clarify

I would clarify tool stack, workflow ownership models, and where handoff delays currently create the most business drag.

### Approach

Use a cross-surface execution graph: map decisions from docs/meetings to tasks, maintain context links, and automate progress synthesis.

### Metrics & instrumentation

Primary metric: decision-to-action lead time reduction. Secondary metrics: task completion reliability, meeting follow-through rate, and context switch reduction. Guardrails: wrong-owner assignments, stale project states, and notification overload.

### Tradeoffs

Unified orchestration improves throughput but can constrain team-specific practices. High automation reduces admin burden but risks brittle workflow assumptions.

### Risks & mitigations

Risk: integration fragility across tools; mitigate with resilient connectors. Risk: low trust in automated assignments; mitigate with approval controls. Risk: action overload; mitigate with priority-aware batching.

### Example

For product launches, AI maps roadmap docs and launch meetings into owner-tagged tasks, then updates weekly executive status summaries from completed milestones.

### 90-second version

Design enterprise productivity AI as a shared execution graph across docs, meetings, and tasks. Optimize decision-to-action speed while preserving role clarity and workflow flexibility.

FOLLOW-UPS

Clarification

Which handoff point causes the largest execution delay today?
What level of automation is acceptable for task assignment?

Depth

How would you represent cross-tool context links in a scalable graph model?
What governance prevents stale or conflicting status updates across surfaces?

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