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INSIGHTS

Retail AI agents: Top use cases for your business

May 25, 2026
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Table of Contents

Executive summary

A retail AI agent is more than a chatbot. In 2026, “agentic” AI is showing up as workflow automation that can monitor signals, identify exceptions, recommend next steps, and trigger actions (with human approval) across operations, loss prevention, and safety. 

This guide breaks down what a retail AI agent actually is, the top use cases leaders are deploying in 2026, what inputs make agents effective (video, POS, alarms, access, incidents), and how to implement them.

Key takeaways

  • A retail AI agent is a workflow engine: detect → verify → act → document, not just “answer questions”
  • The highest-ROI use cases are exception-driven (refund fraud, after-hours access, safety hazards, repeat patterns), not generic automation
  • Retail leaders are betting on agentic AI
  • Solink helps by turning cameras into searchable, linkable intelligence connected to POS, alarms and business-critical systems

If you’re a retail leader, you’re probably hearing “AI agents” everywhere right now, and also wondering if it’s just the next buzzword in the world of artificial intelligence.

Fair question. Retail has no shortage of dashboards that promise insights but still leave your teams doing the hard work manually – chasing alerts, hunting for video, building cases, and trying to standardize execution across stores that all run a little differently.

What’s changed in 2026 is that AI is starting to move from insight to action. That’s what “agentic” really means. Instead of showing you a chart, an agent can monitor signals, triage exceptions, and run the first 80% of a workflow – so your team can focus on decisions, not admin.

The timing isn’t accidental. Retailers are under pressure on multiple fronts. Theft is up sharply: NRF reports a 93% increase in average shoplifting incidents (2023 vs. 2019) and a 90% increase in dollar loss due to shoplifting. Shrink is also huge at an industry level. NRF’s National Retail Security Survey reports a 1.6% shrink rate in FY 2022, equating to $112.1B when applied to total US retail sales.

At the same time, there’s real upside in operational efficiency. McKinsey estimates generative AI could unlock $240B–$390B in economic value for retailers, roughly 1.2–1.9 percentage points of margin across the industry. That’s why leaders are taking agents seriously – not because it’s trendy, but because the payoff is measurable.

Let’s break down what a retail AI agent is, where it delivers value, and how to implement it without creating more noise, or more risk.

Learn how AI in retail enhances security, improves day-to-day operations, and gives valuable insights that increase profitability. 

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What a retail AI agent is (and what it isn’t)

A retail AI agent is software that can complete tasks across systems with minimal prompting. The key difference from a normal “AI assistant” is that an agent doesn’t stop at an answer. It can take the next steps in a workflow.

A simple way to explain it:

  • An AI assistant answers questions.
  • An AI analytics tool identifies patterns and produces dashboards.
  • A retail AI agent can monitor signals, decide what’s important, and trigger actions like creating cases, pulling evidence, notifying the right team, and assigning follow-up tasks – usually with a human-in-the-loop approval step.

If you run multi-site retail, this matters because the work of retail operations and security is not knowledge, it’s coordination. Agents help you coordinate consistently across different stores, shifts, and systems.

COO guide to AI-driven retail loss prevention and risk reduction

A man uses a tablet in a clothing store; text highlights an AI-driven retail loss prevention guide and $132 billion in annual retailer losses.
A person monitors multiple security screens; text reads “CSO guide to modernizing your GSOC with cloud AI. How cloud AI helps plug the $1 trillion physical security gap.”.
Retailers lose an estimated $132 billion annually to shrink – and that’s only part of the total loss picture.

And retailers know it. The report found 89% of retail leaders are aware of total retail loss, and 64% report it has already impacted the way their organization manages loss. Yet only 55% say they can actually calculate total retail loss across their business, largely because the data required is still siloed, incomplete, or inconsistently captured across functions.

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The inputs that make AI agents actually useful in retail

Retail AI agents are only as good as their context. In practice, the strongest agents are multi-signal, they combine data sources so they’re not guessing based on one weak clue.

The most valuable inputs include:

  • Video (cameras) as the ground truth of what happened
  • POS transactions (refunds, voids, discounts, no-sales) as the highest-signal risk triggers
  • Alarms and panic/duress buttons as time-sensitive safety signals
  • Access control events (door held open/forced open, after-hours access) as perimeter and internal-risk signals
  • Incident reports and case notes as the narrative layer
  • Tasking/checklists (open/close, safety audits) as operational accountability

This is where many agent projects fail. They start with a smart model and weak integration. The best retail AI agent programs start with strong signals and clear workflows.

Retail AI agent use cases that matter in 2026

Below are the top use cases leaders care about most, organized by outcomes. You’ll notice a pattern. The best agents don’t try to “watch everything.” They focus on exceptions that are expensive when missed.

Use case 1: POS exception review agent (shrink and fraud)

This is one of the highest-ROI agent workflows because it starts from strong signals. Instead of asking loss prevention to randomly audit stores or scrub footage, an agent can:

  • Identify risky transaction clusters (refunds, voids, discounts, no-sales)
  • Create a review queue by store, cashier, shift, and time band
  • Pull the matching video automatically
  • Generate a draft case summary and attach evidence

The win is speed and consistency. Your best investigators don’t become bottlenecks, and you stop relying on “someone noticing something odd.”

Use case 2: Refund fraud and return abuse agent

Refund fraud is especially painful because it blends policy, customer interaction, and operational inconsistency. A return agent can help by:

  • Flagging repeat return patterns across locations
  • Identifying outliers (stores, desks, shifts)
  • Linking the refund event to video and notes
  • Triggering escalation workflows when thresholds are crossed

The value here is not “catching” every return. It’s preventing repeat abuse by identifying patterns early, and ensuring you have clean evidence when you need to take action.

Use case 3: ORC pattern detection agent (multi-store intelligence)

Organized retail crime (ORC) isn’t a single incident. It’s repeatable tactics tested across stores. An ORC agent helps you connect dots by:

  • Clustering incidents by method (pushouts, grab-and-run, concealment, refund fraud)
  • Flagging repeat time windows and locations
  • Identifying cross-store patterns that store managers can’t see
  • Automatically creating a multi-store case file with supporting evidence

This matters because shrink is a board-level number. NRF’s NRSS reports 1.6% shrink in FY 2022, or $112.1B at the total US retail sales level. Agents help you treat ORC like a program, not a fire drill.

Use case 4: Safety hazard follow-up agent (workplace safety at scale)

Safety programs often fail in the gap between “someone noticed a risk” and “it actually got fixed.” A safety agent can:

  • Create follow-up tasks from incident reports or flagged hazards
  • Escalate repeat hazards into corrective action workflows
  • Schedule spot checks for high-risk zones
  • Provide evidence and closure documentation for audits

If you operate large retail footprints, this is a real differentiator. The BLS reports 888,100 cases involving days away from work in private industry. Even small reductions in repeat hazards can move real cost.

Use case 5: Duress and incident response agent (people protection)

This is where agentic feels most tangible. When a panic/duress event occurs, seconds matter, and so does documentation.

A response agent can:

  • Pull up the nearest cameras automatically
  • Preserve pre- and post-event context
  • Notify the right stakeholders (store, GSOC, security, HR)
  • Create an incident record with timeline and actions logged
  • Package evidence for HR/legal/insurer workflows

This is especially important as retail leaders increasingly view theft and violence as connected problems.

Use case 6: Opening/closing compliance agent (operational consistency)

Most operational drift is not malicious. It’s what happens when teams are busy and standards vary by manager. An execution agent can help by:

  • Monitoring completion of key tasks (open/close, cash handling, doors secured)
  • Flagging missed steps and repeat non-compliance
  • Triggering coaching tasks rather than waiting for an incident

This is a perfect example of how AI agents improve both profitability and security. Stores that drift on procedures are often the same stores that become high-risk for shrink and safety issues.

Use case 7: Multi-site performance benchmarking agent (where to focus)

If you run 200 stores, the hardest question is not “what happened?” It’s “where should we focus next?”

A benchmarking agent can:

  • Identify outlier stores by shrink signals, incidents, and operational drift
  • Recommend targeted interventions (training refresh, procedure change, staffing coverage)
  • Track improvement over time so progress is measurable

This is where agents earn executive trust. They don’t just automate tasks, they help allocate scarce leadership attention.
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Where Solink fits in a retail AI agent stack

Solink is an AI-driven video intelligence platform that works with your existing cameras and connects video to business-critical signals like POS and alarms. That matters for agents because video is often the truth layer that turns a pattern into a decision.

In an agentic workflow, Solink helps by enabling:

  • Fast linkage between POS events and the exact moment on video
  • Video verification context for alarms and incidents
  • Multi-site visibility to spot repeat patterns
  • Consistent evidence packages that support outcomes (coaching, HR, law enforcement)

Put simply, agents are only as good as the context they operate on. Solink helps provide that context at scale, without forcing a rip-and-replace camera project.

Interested in learning more? Book a demo today.
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FAQ: Retail AI agent

What is a retail AI agent?
A retail AI agent is software that can monitor signals and execute multi-step workflows (with approvals) across systems like POS, video, alarms, access control, and incident management.
Most retailers see fast ROI from POS exception review (refunds/voids/discounts), incident response automation (duress workflows), and repeat-pattern detection across stores.
Yes. A Fluent Commerce survey cited by TechRadar reports 70% of retailers have piloted or partially implemented agentic AI, while only 8% report full deployment, suggesting the category is early and competitive advantage is still available. 
They help by prioritizing exceptions, linking transactions to evidence faster, standardizing investigations, and identifying repeat patterns across locations, so teams can intervene earlier and more consistently.
Tie it to measurable outcomes – investigation time saved, repeat incident reduction, task closure rates, and shrink-related improvements.
Solink provides the video intelligence layer, connecting cameras to POS and business-critical systems, and enabling real AI agents to verify incidents, accelerate investigations, and detect repeat patterns with real business context.