In 2026, AI in retail is no longer about experimentation, it’s about execution. Retailers are seeing the strongest ROI when AI connects video to business data and drives actionable workflows, not when it simply generates more alerts.
The most effective AI programs in retail share common traits:
Exception-based investigations replace manual video review.
Self-checkout, returns, and ORC remain the fastest paths to measurable impact.
Safety, compliance, and dispute resolution now rival shrink reduction as top ROI categories.
Generative AI is becoming a practical copilot for reporting, analysis, and executive communication.
Governance and consistency matter more than ever as AI scales across locations.
The winning pattern is clear. Retailers that unify video and operational data, and operationalize the insight, move from reactive loss prevention to real ROI. AI-driven video intelligence solutions like Solink enable teams to see what matters, understand why it’s happening, and act faster with confidence – turning AI in retail into measurable, repeatable impact.
If you lead retail security, loss prevention, or asset protection, you’re being asked to do something that’s harder every year. Reduce loss, protect staff, and improve profitability – without slowing down the business or creating friction for customers.
In 2026, artificial intelligence (AI) in retail is a real competitive difference. It’s about building repeatable workflows that turn everyday store activity into measurable margin protection and profitability growth.
Today’s retailers don’t need more alerts. You need the right alerts. You need fewer blind spots and faster decisions. And the stakes are real.
Shrink remains a major profitability drain. NRF’s National Retail Security Survey (NRSS) for FY 2022 reported a 1.6% shrink rate, representing $112.1B in losses (US).
ORC is evolving beyond in-store theft into phone scams, digital fraud, cargo theft, and more – forcing loss prevention leaders to think of their entire “ecosystem,” not just “store.”
Today, the best AI strategies follow one rule:
AI delivers ROI when it helps your team act, not just watch.
So as you read the 10 use cases for AI in retail below, keep this lens in mind:
Detection: Did the AI surface the right event?
Decision: Did it provide context (video and data) so you can decide quickly?
Action: Did it trigger a workflow your team can execute consistently?
Measurement: Can you tie it to outcomes (loss reduced, claims avoided, labor saved or increased profitability)?
That’s the “AI in retail” playbook for 2026. With that in mind, let’s look at 10 critical ways successful multi-location retailers are using AI in 2026 to enhance security, increase ROI, and reduce loss.
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The retail landscape is in a period of significant transformation as businesses weigh up the most effective and impactful way to leverage artificial intelligence. And while this shift is happening at different speeds for different businesses, Solink’s recent ‘State of AI in retail’ whitepaper found that AI is moving beyond a buzzword into a solution that delivers measurable results.
The study, which surveyed 150 decision makers across the retail industry, found 86% of smaller retailers (50-199 stores), 92% of mid-size retailers (200-499 stores) and 88% of large retailers (500+) have a dedicated budget for AI.
Small retailers are more likely to be in the exploration or pilot phase with AI technologies, while mid-size retailers are the fastest movers with fewer legacy deployments, and large retailers have the largest budgets but move slower due to organizations complexity and governance.
Overall the top strategic drivers of AI adoption were found to be improving the customer experience, increasing operational efficiency, as well as driving revenue and profitability.
Solink partnered with a research firm to survey 150 retail leaders, with a goal of answering core questions around who is adopting AI in retail settings, what drives success or future adoption, and how confident are different teams when it comes to implementing AI within their function of the retail business.
Top ways retailers are using AI to enhance security and improve profitability in 2026
1. Exception-based investigations that connect video to POS reality
If your team is still doing manual searches like “Find register 4 at 2:10 PM,” you’re paying an investigation tax you don’t need.vIn 2026, leading retailers use AI to build exception-based investigation queues – so investigators focus only on moments that correlate with loss.
Investigators jump straight to video tied to the transaction and timestamp.
Repeat patterns (by cashier, store, lane, or time window) become visible quickly.
Where the ROI comes from
Less time scrubbing footage, more time correcting the root cause.
Faster identification of coaching issues versus intentional fraud.
Better consistency across multi-store operations.
Metrics to track
Time-to-evidence (minutes vs hours)
Cases reviewed per investigator per week
Exception rate trends by lane and store
Confirmed loss rate per exception type
Quick start playbook
Start with 3–5 exception types that are common in your business.
Set review thresholds (example: “Top 10 lanes by void rate each week”).
Create a standard outcome per case (coach, policy update, HR escalation, process fix).
Report weekly outcomes to Ops using the same template.
2. Self-checkout shrink reduction without damaging customer experience
Self-checkout is still a pressure point. The mistake many retailers make is treating every SCO issue like a crime problem. In 2026, the best programs use AI to separate:
Accidental loss (missed scans, produce misidentification, confusion)
AI flags risk moments and routes them to an attendant or manager in real time.
Interventions are “assist-first” (help the customer complete the transaction) instead of confrontational.
Your thresholds change by store risk tier and time of day.
Where the ROI comes from
Fewer missed scans and policy bypasses.
Better throughput (because attendants intervene earlier and more calmly).
Reduced escalation risk for staff.
Metrics to track
SCO interventions per 1,000 transactions
Loss rate per SCO transaction
Queue time impact (make sure you don’t “fix shrink” by hurting throughput)
Quick start playbook
Pilot in a small set of high-volume stores.
Focus on one category first (example: “Missed scans”).
Add coaching loops: “Top 3 SCO patterns per store” shared weekly with store leadership.
3. AI-driven detection of returns and refund abuse
Returns aren’t just an operations issue. For many retailers, they are a margin leak hiding in plain sight. AI in retail is increasingly used to flag:
No-receipt return clusters
“Wardrobing” patterns (buy, use, return)
Item swapping and packaging fraud
Refunds without the customer present (a classic red flag)
What this looks like in practice
Refunds above a threshold trigger a quick video check.
Customer service disputes get resolved with visual evidence.
High-risk return patterns get escalated to a consistent workflow.
Where the ROI comes from
Prevented fraudulent refunds
Faster customer resolution (less goodwill loss)
Less time spent debating “what happened”
Metrics to track
Refund loss prevented (be conservative and document assumptions)
Refund exception volume by store
Dispute resolution time
Quick start playbook
Identify your top refund abuse patterns (example: “Refunds with no merchandise visible”).
Set a review workflow that store leaders can actually follow.
Standardize coaching: “Here’s what good looks like at customer service.”
4. Multi-store ORC pattern detection and evidence packaging
ORC isn’t only getting more common, it’s getting smarter. NRF’s research shows retailers reporting increases across multiple ORC-driven categories like phone scams and digital fraud, not just in-store theft.
In 2026, retailers are using AI to do what humans struggle with, connect patterns across stores.
What this looks like in practice
Similar incidents are grouped by product type, time band, and behavior patterns.
Loss prevention can see “hot stores” and repeat methods quickly.
Evidence packages get created with consistent exports, timelines, and metadata.
Where the ROI comes from
Faster case closure
Less duplication of effort across regions
Better outcomes when partnering with law enforcement (because your evidence is cleaner)
Metrics to track
Repeat incident reduction in targeted stores
Investigator hours saved per ORC case
Time from incident to “submission-ready” evidence package
Operational reality check
NRF notes many retailers report fewer than half of store-related theft incidents to law enforcement – often due to lack of response. That makes it even more important to prioritize cases where evidence and follow-through are likely to produce outcomes.
5. Safety detection that reduces claims and protects teams
AI in retail isn’t only about loss. Safety is one of the clearest ROI categories because it affects:
Claims costs
Staff retention
Store uptime
Brand risk
What this looks like in practice
AI flags slip-and-fall events, aggressive behavior, after-hours presence, or restricted-area access.
6. Compliance verification that turns audits into proof
Retail leaders are tired of compliance “checkboxes” that don’t reduce risk. In 2026, AI helps you validate compliance with visual evidence, not just signatures.
High-value compliance use cases
Opening and closing procedures
Cash handling steps (drops, safe access, till counts)
Better training outcomes because feedback is specific and visual
Metrics to track
Repeat non-compliance rate by store
Audit completion and follow-up rates
Cash variance trends (where applicable)
7. Faster dispute resolution for customers, employees, and vendors
Disputes quietly destroy margin:
Unnecessary refunds
Manager time
Brand damage
HR complexity
AI in retail is increasingly being used for faster truth-finding.
What this looks like in practice
A customer claims they were overcharged – find the POS event and video clip in minutes.
An employee dispute needs context – pull the timeline and review the moment quickly.
Vendor receiving disputes (where cameras cover receiving) – validate delivery time and handling.
Where the ROI comes from
Fewer unnecessary refunds and payouts
Less manager time spent “investigating”
Faster, fairer outcomes (which also helps culture)
Metrics to track
Average dispute resolution time
Refund rate tied to disputes
Manager hours saved
8. Labor optimization through queue insights and service-level visibility
Loss prevention leaders know a truth most people ignore: Understaffing increases risk. When service breaks down, opportunity spikes – both for external theft and internal policy drift.
What this looks like in practice
Queue detection alerts prompt backup before lines become unmanageable.
Traffic and service patterns inform scheduling decisions.
Stores get staffed to real demand patterns, not guesswork.
Where the ROI comes from
Fewer walkouts and abandoned baskets
Better customer experience (and conversion)
Reduced stress on associates, which helps retention
Metrics to track
Peak queue duration
Abandonment proxies (where measured)
Overtime and schedule adherence
9. Inventory integrity and shelf availability through visual verification
Inventory distortion is one of the hardest retail problems because it hides behind systems that look “accurate” until they aren’t. AI can help spot operational signals like:
Shelf gaps (when camera placement supports it)
Unusual replenishment patterns in high-risk SKUs
Backroom-to-floor movement inconsistencies
Where the ROI comes from
Improved on-shelf availability (protecting sales)
Reduced loss in high-theft categories
Fewer emergency replenishment cycles
Metrics to track
On-shelf availability in priority categories
Cycle count accuracy improvements
High-risk SKU loss rate trends
Quick start playbook
Pick one high-theft category (example: OTC, cosmetics, batteries).
Establish a weekly routine: “Top 10 anomalies and corrective action.”
Tie results back to sales impact and shrink impact separately.
10. Generative AI copilots that accelerate reporting and executive ROI stories
Generative AI has finally found a practical home in loss prevention, turning raw incidents into usable communication. And in 2026, communication is part of the job. You’re not just preventing loss, you’re proving impact to leadership.
What this looks like in practice
Auto-drafted case narratives (with humans approving)
Weekly summaries of drivers by region
Natural language queries like “Show me top stores by refund exceptions last week”
Where the ROI comes from
Faster reporting cycles
Better alignment with operations leaders
Less analyst time formatting and compiling
Prevent loss and boost ROI with Solink AI
Find out how Solink enables retailers to leverage AI for smarter operations.
How Solink helps teams turn AI in retail into measurable impact
For retail loss prevention and security leaders, AI only matters if it changes outcomes. Solink is built to help teams move beyond reactive investigations and use AI-driven video intelligence to protect margins, reduce risk, and operate with confidence across every location.
Solink delivers practical impact by helping teams:
Unify existing cameras across locations: Gain consistent visibility without ripping and replacing infrastructure, so security and loss prevention teams can see what’s happening anywhere, anytime.
Connect video with POS and operational data: Instantly link transactions, access events, and operational activity to video context, dramatically reducing investigation time and guesswork.
Reduce false alarms with verified video alerts: By validating alarms with real video context, teams can cut noise and focus on true risk. Customers report false alarm reduction of up to 99.9% through video-verified monitoring.
Reinforce compliance and audit workflows: Use video evidence to confirm procedures were followed, support audits, and coach teams consistently based on real behavior.
Operate confidently at scale: Solink supports security and loss prevention programs across tens of thousands of locations globally, helping teams standardize workflows and insights as their footprint grows.
The pattern behind successful AI adoption in retail is clear: unify video and data, then operationalize the insight. Solink helps teams see what matters, understand why it’s happening, and take action faster – turning AI in retail into measurable, repeatable impact.
In 2026, AI in retail usually refers to tools that analyze video, transactions, and operational data to detect risk, highlight exceptions, and automate workflows. The most valuable AI doesn’t just “detect” – it helps teams act and measure outcomes.
Which AI use cases produce ROI fastest for loss prevention?
For many retailers, the fastest ROI comes from:
Exception-based investigations tied to POS
Self-checkout shrink reduction
Returns and refund abuse detection
Safety incident documentation and response workflows
These are frequent, measurable, and operationally actionable.
How do retailers use AI with existing cameras?
Leading retailers deploy AI through platforms that integrate with existing camera infrastructure, then apply analytics in the cloud or through connected systems. The key is not the camera, it’s the workflow and the data connection.
Solink is built to work with existing cameras while connecting video to POS and other business systems.
How do I measure ROI from AI in retail security and loss prevention?
Start with a small set of KPIs tied to outcomes, such as:
Investigation time saved
Refund loss prevented
Exception rates reduced
Claims cycle time improved
Repeat incident reduction in high-risk stores
Will AI replace loss prevention investigators?
In practice, AI helps investigators focus on higher-value work. It reduces time spent searching and increases time spent solving root causes (coaching, process fixes, store hardening, ORC coordination).
What should I ask vendors when evaluating AI in retail platforms?
Ask questions like:
How do you connect video to POS and other data sources?
What are the workflows after an alert is triggered?
How do you measure false positives and improve performance?
What controls exist for retention, access, and audit trails?
Can we pilot quickly using existing cameras?
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