Review queues organize AI outputs that need human attention before taking action. They prioritize items by urgency, age, and risk so reviewers handle the most important work first. For businesses, this prevents bottlenecks where items pile up unseen. Without review queues, critical decisions wait while low-priority items get reviewed, and aging items slip through the cracks.
The AI flagged 47 items for review. Nobody knows which ones matter most.
That urgent refund request has been waiting 3 days. Nobody saw it.
Your team is drowning in review tasks while critical items age out unseen.
AI moves fast. Human review creates bottlenecks. The queue is where they meet.
HUMAN INTERFACE LAYER - Where AI hands off to human judgment.
A review queue is a managed list of items that AI has flagged for human attention. Instead of items scattering across email threads, chat messages, or forgotten tabs, they land in a single place where nothing gets lost and everything has a priority.
The queue does more than hold items. It prioritizes them by urgency, tracks how long each has been waiting, and ensures reviewers see the most important work first. Without this structure, humans become the bottleneck that breaks the AI workflow.
The goal is not zero queue. The goal is the right items getting the right attention in the right time.
Review queues embody a universal principle: when work requires human judgment, it needs a holding area with visibility and prioritization. The same pattern appears anywhere human attention is a scarce resource.
Items enter a managed queue. Prioritization surfaces the most important work. Aging alerts prevent items from waiting too long. Reviewers process items with full context. Completed items exit with audit trails.
Six items are waiting for review. Select a queue strategy and process items to see the difference.
First in, first out
Items are reviewed in the order they arrived. Simple and fair, but ignores urgency. Works when all items have similar priority and no time sensitivity.
Highest priority first
Items are scored by urgency, value, or risk. Reviewers always see the most important work first. Low-priority items may wait indefinitely unless protected by aging rules.
Priority plus time-based escalation
Items start with a priority score that increases as they age. Old low-priority items eventually rise to the top. Balances urgency with fairness.
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Do items have significantly different urgency levels?
The AI detects a potentially fraudulent refund. It cannot auto-approve due to low confidence. The review queue prioritizes this item, shows the reviewer full context, and ensures it gets attention before aging out.
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This component works the same way across every business. Explore how it applies to different situations.
Notice how the core pattern remains consistent while the specific details change
Your team reviews items as they come in, but nobody tracks overall volume. The queue grows silently until items are days old and customers are complaining. By the time you notice, you have a backlog crisis.
Instead: Display queue depth and average age prominently. Set alerts when thresholds are exceeded. Make queue health a daily metric.
Reviewers open an item and see "AI flagged this for review" with no context. They spend 5 minutes researching what they could have known instantly. Multiply by 50 reviews per day and you have lost half your review capacity.
Instead: Include all relevant context when items enter the queue: AI recommendation, confidence score, supporting data, similar past decisions.
High-priority items always jump the queue. A low-priority but legitimate request waits 2 weeks because something more urgent always appears. The customer churns before you ever review their request.
Instead: Implement aging multipliers that boost priority over time. Set maximum wait times that trigger escalation regardless of initial priority.
An AI review queue is a managed list of items that an AI system has flagged for human attention before proceeding. Items enter the queue when AI confidence is low, risk is high, or policy requires human approval. The queue prioritizes items so reviewers see the most important work first and nothing ages out unseen.
Use review queues whenever AI decisions have real-world consequences that justify human oversight. Common scenarios include high-value transactions, customer-facing communications, content moderation, and compliance-sensitive actions. Review queues are essential when the cost of an AI error exceeds the cost of human review time.
Prevent backlogs by setting clear SLAs for review times, monitoring queue depth and aging metrics, and adjusting AI thresholds to balance volume with accuracy. When queues grow, either add reviewer capacity, raise AI confidence thresholds to reduce volume, or escalate aged items automatically.
Good review queue interfaces show reviewers everything they need to decide quickly: the AI recommendation, supporting context, confidence score, and similar past decisions. They enable one-click approve or reject, support batch actions for similar items, and provide keyboard shortcuts for speed.
Review queues manage the backlog of items waiting for review, focusing on prioritization, aging, and visibility. Approval workflows define the routing rules for who reviews what and in what order. Review queues handle the "what needs review" while approval workflows handle the "who reviews it and how."
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Choose the path that matches your current situation
Review items are scattered across tools and inboxes
You have a queue but items pile up or age out
Queue works but you want faster review throughput
You have learned how to manage items waiting for human review. The natural next step is understanding how to route those items to the right reviewers.