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How retailers use AI video security in 2026

January 20, 2026
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Table of Contents

The challenges associated with retail security are becoming increasingly complex. 

Shrink is now a $112 billion annual crisis. Organized retail crime continues to rise, now accounting for 24.7% of total shrink cases (according to the NRF). Internal theft is ever-present. Labor is stretched thin. And store managers are juggling more responsibility with less time. 

On top of all that, security leaders are being asked a new question by executives:

“What are we getting back from all this video investment?”

For decades, video security was treated as a necessary expense. Cameras were installed to capture evidence after something went wrong. Footage was reviewed manually. Investigations were slow. And outside of loss prevention, video rarely played a role in how the business operated day to day.

That model no longer works.

In 2026, retailers already have cameras everywhere. The competitive advantage isn’t adding more hardware, it’s what you do with the video you already have. This is where AI-driven video intelligence is changing the game.

Retail video security is shifting from “recording evidence” to driving business decisions. The most successful retailers (both SMB and enterprise) are using AI video security not just to protect stores, but to reduce loss, improve operations, enhance safety, and ultimately increase profitability.

This article breaks down how retailers are using AI video security today, what problems it’s solving, and how video is becoming a profit-driving business tool rather than a security cost center.
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What is AI retail video security?

AI retail video security is the use of artificial intelligence (AI) to transform traditional video surveillance into an intelligent, automated system that detects risk, reduces false alarms, and delivers real-time insight.

Instead of relying on people to watch screens or review footage after an incident, AI video security continuously analyzes video feeds to identify suspicious behavior, unusual patterns, and operational issues as they happen. 

It can detect anomalies, track movement, and surface the moments that matter – without requiring constant human monitoring.

This is a fundamental shift from how video has historically been used in retail.

Traditional CCTV vs AI video security

Traditional CCTV AI retail video security
Passive and reactive Proactive and automated
Manual review after incidents Real-time detection and alerts
High labor cost to investigate incidents Dramatically faster investigations
Limited value outside security Supports security, operations, and compliance
No prioritization or intelligence Turns video into structured, usable data
In short, AI video security turns cameras into intelligent sensors that help retailers understand what’s happening across their stores, not just document it.

CSO guide to modernizing your GSOC with cloud AI

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.”.
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.”.
Today’s physical security leaders must do more than guard assets, they must prove measurable ROI. Security can no longer be viewed as a cost center, it’s a data- driven business function. That means shifting from reactive to proactive protection through AI and cloud-based intelligence.

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Why AI retail video security is important in 2026

AI video security matters because the problems retailers face today are too complex for manual systems to handle.
Traditional approaches struggle with: AI video security addresses these challenges by:
High false alarm rates Automatically detecting, and verifying, real threats
Slow investigations Filtering out irrelevant activity
Fragmented systems Providing instant visual context
Limited store-level visibility Reducing the workload on security teams through automation
Inconsistent execution across locations Helping retailers act faster and with more confidence
But the real value goes beyond security. AI video security supports the entire retail business. It helps reduce loss, improve the customer experience, strengthen compliance, and uncover operational inefficiencies that directly impact profit margins.

That’s why leading retailers now view video as a source of business intelligence, not just security footage.
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How retailers are using AI video security today

In 2026, retailers aren’t using AI video security as a “nice-to-have” upgrade. They’re using it to solve persistent problems that directly impact shrink, safety, labor efficiency, and store performance. 

Below are the most common, high-impact use cases retailers are applying right now – each tied to a specific challenge they’re trying to fix.

1. Reducing investigation time when incidents occur

The challenge:
When something goes wrong – a theft, a dispute, or a suspicious transaction – investigations grind to a halt. Loss prevention teams bounce between video systems, POS reports, and spreadsheets, manually cross-referencing timestamps and transactions. Analysts often review hundreds of clips to find a handful of relevant moments, turning what should be a quick investigation into hours of work, and delaying action when speed matters most.

How AI-driven retail video security helps:
A modern retail video intelligence platform brings video and POS data together in a single view, so investigators can see exactly what happened at the moment of a transaction, without switching tools. AI helps surface risk faster by filtering for suspicious events, enabling spot checks that narrow hundreds of transactions down to a focused shortlist worth reviewing. From advanced search tools to motion-based filtering and transaction-linked video, the entire platform works together to remove manual effort and accelerate investigations end to end.

The impact:
  • Dramatically faster investigations with fewer steps
  • Focused reviews that prioritize high-risk transactions
  • Quicker resolution of theft, fraud, and disputes
  • Lower operational and labor costs for loss prevention teams

2. Identifying organized retail crime (ORC) activity before it escalates

The challenge:
Organized retail crime doesn’t always start with a smash-and-grab. In many cases, it begins quietly – through repeated store visits, prolonged loitering near entrances, parking lots, or back-of-house areas, or coordinated activity across multiple locations. ORC can also take non-physical forms, such as refund fraud or sweethearting, when employees and external actors work together to exploit systems over time. Traditional retail security tools rarely connect these early signals or flag suspicious patterns before losses add up.

What is organized retail theft and how to fight against it

How AI-driven retail video security helps:
AI can analyze dwell time, movement patterns, and behavioral anomalies across both customer-facing and operational areas. It can surface unusual activity like repeated lingering, coordinated visits, or patterns that align with refund abuse or internal collusion. By connecting these behaviors across locations and time, teams gain earlier visibility into potential ORC activity – whether it’s physical theft in planning or organized fraud happening at the register.

The impact:
  • Earlier detection of coordinated ORC activity, physical and non-physical
  • Stronger evidence linking incidents across people, transactions, and locations
  • Fewer surprise losses from long-running fraud schemes
  • A more proactive, data-driven ORC prevention strategy

3. Catching internal theft and sweethearting tied to POS activity

The challenge:
Internal theft, sweethearting, and transaction abuse are some of the hardest losses to catch. POS data alone may show refunds, voids, or discounts, but it can’t explain whether those actions were legitimate.

How AI-driven retail video security helps:
By connecting video to POS data, retailers can flag high-risk transactions and instantly verify through video what actually happened at the register. For example, if a refund is processed but no customer is present, video provides immediate context.

The impact:
  • Reduced internal theft and fraud
  • Fair, consistent accountability for staff
  • Faster resolution of suspicious transactions
  • Stronger loss prevention with visual proof

4. Verifying after-hours access and eliminating false alarms

The challenge:
After-hours alarms generate enormous noise. Motion sensors trigger on cleaning crews, HVAC changes, or harmless movement – overwhelming teams and leading to unnecessary dispatches.

How AI-driven retail video security helps:
AI-driven alerts verify whether after-hours activity involves a real person or threat. Only verified events are escalated, while irrelevant motion is ignored. Some retailers also use visual or audible deterrents once a real intrusion is confirmed.

The impact:
  • Dramatically fewer false alarms
  • Lower monitoring and dispatch costs
  • Faster response to real threats
  • Reduced alert fatigue for security teams

5. Confirming deliveries and reducing receiving disputes

The challenge:
Delivery discrepancies are common – missing boxes, damaged shipments, or disputes over arrival times. Without proof, retailers often absorb the loss.

How AI-driven retail video security helps:
Video analytics can confirm when delivery trucks arrive, how long they stay, and what condition shipments are in during unloading. This creates a visual record tied to time and location.

The impact:
  • Fewer vendor disputes
  • Faster resolution of delivery issues
  • Better accountability in receiving
  • Reduced shrink from supply chain errors

6. Improving store performance through traffic and dwell analysis

The challenge:
Retailers often make staffing and layout decisions based on assumptions or outdated reports. They know traffic fluctuates, but not always where or why customers slow down, linger, or leave.

How AI-driven retail video security helps:
AI video analytics measure foot traffic, dwell time, bounce rate, draw rate, and customer movement patterns throughout the store. Retailers can see which areas attract attention, where congestion builds, and how long customers wait at service points.

You can learn more about how this works in our blog, The best ways to calculate foot traffic and people counting. 

The impact:
  • Better staffing decisions
  • Improved customer flow
  • Reduced wait times
  • Higher conversion opportunities

7. Identifying checkout bottlenecks and service delays

The challenge:
Long lines and slow checkouts directly hurt revenue and customer satisfaction. But it’s not always clear whether delays are caused by staffing, layout, technology, or process breakdowns.

How AI-driven retail video security helps:
Video analytics track queue length, wait time, and service duration at checkout and service counters. This gives operators concrete data to diagnose the real cause of delays.

The impact:
  • Faster checkout experiences
  • Improved customer satisfaction
  • More efficient labor allocation
  • Increased throughput during peak hours

8. Enforcing brand standards and promotional execution

The challenge:
Multi-location retailers struggle with consistency. Promotional displays, planograms, and store layouts often drift from brand standards, reducing campaign effectiveness.

How AI-driven retail video security helps:
Retail teams use video-based checks to remotely confirm that displays are set correctly, layouts match planograms, and marketing initiatives are executed as intended.

The impact:
  • Better brand consistency
  • Stronger ROI from promotions
  • Fewer in-person audits
  • Faster issue correction

9. Ensuring stores open and close on time

The challenge:
Late openings or early closings cost revenue and, in some cases, violate mall or property agreements. These issues are easy to miss until after the fact.

How AI-driven retail video security helps:
Video analytics provide visibility into opening and closing routines, confirming whether stores are operational on schedule and procedures are followed.

The impact:
  • Fewer lost sales opportunities
  • Improved compliance with lease agreements
  • Better accountability at the store level

10. Reducing safety incidents and liability exposure

The challenge:
Slip-and-fall claims, blocked exits, and unsafe behavior are major sources of liability. Without documentation, disputes are costly and slow to resolve.

How AI-driven retail video security helps:
Retailers use video to verify conditions around incidents, ensure walkways and exits are clear, and document compliance with safety procedures.

The impact:
  • Reduced liability exposure
  • Faster claims resolution
  • Stronger safety culture
  • Better protection for employees and customers
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Why Solink is the best AI-driven retail video security system in 2026

Across all of these use cases, the pattern is clear. Retailers aren’t using AI video security just to see what happened. They’re using it to understand why it happened and prevent it from happening again.

That’s the difference between video as a security tool and video as a business advantage.

And that’s exactly what Solink was built for. Solink is an AI-driven video intelligence platform built specifically for multi-location retail environments.

Instead of replacing cameras, Solink works with the infrastructure your retail business already has. It connects video with POS systems, access control, alarms, and other business-critical systems to provide a single, unified view of what’s happening across every one of your stores.

Retailers use Solink to:

  • Reduce shrink and fraud
  • Fight ORC with cross-store visibility
  • Improve checkout speed and service quality
  • Standardize execution across locations
  • Investigate incidents in seconds, not hours
  • Turn video into a profit-driving intelligence layer

Solink doesn’t just help retailers respond to incidents – it helps them prevent loss, improve performance, and run better stores. If you’re ready to see how AI-driven retail video security can deliver real ROI, the next step is simple, book a demo to see Solink in action.

FAQ: Retail video security

What is retail video security?
Retail video security is the use of cameras and analytics to protect stores, employees, and customers while reducing loss and supporting operations.
AI automates detection, reduces false alarms, and provides real-time insight into behavior and operational issues.
Yes. By detecting suspicious behavior early and linking video to transactions, retailers can reduce both internal and external theft.
Video analytics reveal bottlenecks, training gaps, and execution issues that reports alone can’t show.
It depends on the solution provider you invest in. Some providers will not work with your existing hardware, while others, such as Solink, will work with your existing camera systems.
AI helps identify patterns across locations, detect staging behavior, and surface coordinated activity faster.
No. Retailers of all sizes benefit from better visibility, faster investigations, and improved consistency.
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