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The top 6 AI solutions transforming retail in 2026

May 27, 2026
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Table of Contents

Executive summary

This article explores how artificial intelligence (AI) is transforming retail by enhancing security, improving customer experience, and driving operational efficiency. It highlights the benefits of AI-powered video analytics, including theft prevention, real-time insights, and automation of routine tasks. The content is tailored for retail managers and industry professionals seeking to leverage AI technologies to optimize store performance and reduce losses.

Key takeaways

  • AI enhances retail security through advanced video analytics that detect suspicious behavior and reduce theft.
  • Real-time AI insights help retailers understand customer behavior and optimize store layouts and staffing.
  • Automation powered by AI reduces manual monitoring, allowing staff to focus on improving customer service.
  • AI tools integrate with existing retail systems to provide comprehensive, actionable data.
  • Retailers adopting AI solutions gain a competitive edge by increasing operational efficiency and minimizing shrinkage.

Artificial intelligence (AI) is no longer just a trend in retail – it’s the infrastructure behind smarter, more scalable operations.

As margins tighten and shopper expectations rise, combined with the fact retail teams are expected to do more with less, businesses are turning to AI to automate tasks, unlock new insights, and create seamless customer experiences across their physical storefronts.

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

At Solink, we believe AI isn’t just about automation. It’s about business visibility. It’s about connecting data across systems – point-of-sale (POS), video, inventory – and surfacing insights retailers can act on to increase ROI and drive profitability. 

In this guide, we’ll walk through the top AI solutions driving meaningful change across retail in 2025.

What is AI in retail and why does it matter?

AI in retail refers to systems that can analyze data, identify patterns, and make intelligent decisions without human intervention.

These systems rely on technologies like:

  • Machine learning: Learns from data over time to improve outcomes.
  • Computer vision: Understands and interprets visual inputs (e.g., video streams).
  • Natural language processing: Enables tools like AI chatbots and voice assistants.

The retail AI market is growing rapidly as more businesses adopt these technologies to solve real business problems, like preventing shrink, optimizing stock levels, and improving conversion across channels.

Through the use of AI, retailers can realize benefits such as: 

  • Efficiency gains: AI automates repetitive tasks, freeing up staff to focus on higher-value work that drives further ROI to your business. 
  • Data-driven decisions: Retailers can use AI to gain insights and analyze behaviour that helps to both prevent loss and increase profitability. 
  • Personalized experiences: AI tools can help dramatically enhance the customer experience by analyzing customer behavior and how they interact with your store.
Cover page featuring a surveillance camera, a retail clothing rack, and text promoting a guide on turning video security into retail video intelligence by Solink Corp.

How to turn video security into retail video intelligence

Cover page featuring a surveillance camera, a retail clothing rack, and text promoting a guide on turning video security into retail video intelligence by Solink Corp.
Learn how you can use video data analytics to drive revenue and mitigate theft for your retail business.

This guide will show you how you can turn your security video feed into a business data tool that drives ROI. Tracking store performance, training teams, optimizing labor, and catching revenue-killers before they spiral out of control.

We call it vision intelligence.
Download the AI retailer's guide
Download the AI retailer's guide

Key use cases: How retailers are using AI today

For loss prevention, IT, and operations leaders, the value of AI isn’t in abstract theory; it’s in measurable results. Fewer incidents, faster investigations, better-performing teams, and tighter control across every location.

With that in mind, here are some of the most relevant, ROI-driven use cases where AI is delivering impact for multi-location retailers. 

AI-Powered loss prevention and exception-based investigation

Shrinkage doesn’t just cut into profits, it erodes visibility. The unfortunate thing is that most retailers are significantly underestimating the amount of loss they are experiencing, and that’s simply because they don’t have the visibility needed to see it. 

AI-powered platforms can now detect, surface, and prioritize incidents based on actual risk. For example, retailers can draw a virtual line to count how many people cross it in either direction to easily count entrances and exits, search their VMS by clothing, vehicle make/model and other identifiable objects, as well as get alerts on high-risk activity such as a discount with no customer present. 

Self-checkout monitoring and theft detection

Self-checkout can be fast and efficient for your customers improving their overall experience with your brand, but it’s also a top driver of retail theft. AI helps bridge the gap between convenience and control by analyzing self-checkout behavior at scale. It detects:

  • Item switching or scan avoidance
  • Basket-to-scan discrepancies
  • Frequent weight overrides or voids
If your stores have self checkout and you want to better protect them, check out our blog – What is the self checkout theft rate, and how to prevent it.

Inventory forecasting

AI predicts demand by analyzing historical sales, seasonality, local events, and even weather. For example, a convenience chain might adjust soda restocks ahead of a heat wave. This reduces overstock and missed sales.

Solink integrates POS transaction data and in-store video so retailers can match inventory movement with what’s happening in the aisle, identifying discrepancies and theft patterns more accurately.

Operational compliance and store performance visibility

From food safety checks to employee productivity, AI doesn’t just protect, it improves. Retailers use video and AI to verify that procedures are followed, displays are in place, and store open/close routines happen as scheduled.

Supply chain optimization

AI can be used to monitor supply chains to predict and respond to disruptions, rerouting shipments, suggesting alternative vendors, or dynamically reallocating stock based on sell-through velocity. 

Unified AI insights for cross-functional collaboration

In many retail organizations, loss prevention, IT, operations, HR, and compliance operate in silos. Your business can break down these walls by using AI to centralize event detection, investigation workflows, and reporting – so the right teams can act fast, together.

Whether it’s an LP issue, a scheduling mistake, or a safety protocol breach, AI-classified incidents ensure the right department gets notified instantly. Role-based access makes collaboration easy and secure.

6 AI solutions transforming retail in 2025

Whether you manage 10 stores or 1,000, here are the most impactful AI technologies reshaping brick-and-mortar retail today

1. AI-powered inventory management systems

AI inventory platforms help retailers maintain accurate stock levels, flag anomalies, and reduce losses tied to overstocking, understocking, or phantom inventory.

By analyzing POS transactions, shipment data, and sales velocity, these systems can automatically suggest reorders, identify high-risk SKUs, and highlight discrepancies that signal theft or operational issues. 

Many also integrate exception reporting to identify when inventory changes deviate from the norm – like sudden shrinkage in a high-margin category or unusually fast depletion of limited-stock items. These tools support better replenishment decisions while surfacing loss-related red flags that would otherwise go unnoticed.
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2. In-store behavior analytics

Computer vision and sensor-driven AI can map how customers navigate your stores – tracking dwell time, foot traffic, and engagement with displays or products. This data can help you optimize store layouts, improve signage placement, and make better merchandising decision.

For example, if AI heatmaps reveal customers consistently skipping a specific section, you now have the data you need to redesign that area, relocate items, or adjust staffing during peak periods.

3. AI-powered video management systems

Traditional security footage is reactive. AI-enhanced vendor management systems (VMS) make it proactive by applying computer vision and data tagging to every frame of video, turning cameras into tools for loss prevention, operations, and compliance.

Key capabilities include:

  • Searchable video tied to transaction or time data
  • Automated event detection like loitering, tailgating, or door propping
  • Visual verification of refunds, voids, or self-checkout activity
  • Integration with POS for frictionless incident investigation

These systems don’t just record – they deliver context-rich insights across multiple store locations.

4. Automated store audit tools

Store audits are traditionally time-consuming, subjective, and inconsistent across locations. AI automates this by using computer vision and IoT sensors to monitor and score compliance with corporate standards.

What you can evaluate:

  • Visual merchandising (planogram compliance, promo placement)
  • Cleanliness and sanitation (especially in grocery or QSR environments)
  • Safety risks like blocked fire exits or wet floors
  • SOP adherence for opening/closing or stocking procedures

AI enables district and regional managers to prioritize in-person visits based on flagged risk, not just schedules.

5. Smart shelf sensors

Smart shelf systems integrate pressure sensors, RFID readers, and edge-based computer vision to give retailers real-time visibility into what’s happening at the shelf level, without relying on staff checks or centralized processing.

What these systems detect:

  • Product removals and restocking: Identify when items are picked up, returned, or when shelves are empty, triggering automated replenishment alerts.
  • Empty facings and phantom inventory: Sensors detect shelf gaps and compare them to POS sales data, helping resolve discrepancies between recorded and physical stock.
  • Shelf disorganization: Computer vision identifies misplaced items, incorrect facings, or pricing errors.
  • Customer engagement metrics: Track how long customers interact with specific products, improving category management and merchandising decisions.

6. Dynamic digital signage

AI-powered digital signage transforms static screens into responsive marketing tools that adjust in real time based on environmental and behavioral cues inside the store.

Inputs that drive dynamic changes:

  • Customer demographics: Facial analytics estimate age and gender (when compliant with privacy laws) to tailor promotions – for example, showcasing kids’ snacks when families approach.
  • Foot traffic and dwell time: If a customer lingers near a product category, signage nearby can update with complementary promotions or cross-sells.
  • Inventory and promotional data: Signage can display promotions based on stock levels, advertising clearance items when inventory is high, or pausing a campaign when an item is out of stock.
  • Time of day and weather: Offers can automatically shift based on morning/evening traffic or rainy/hot conditions (e.g., promoting umbrellas during storms).

By connecting signage to POS, inventory, traffic sensors, and AI models, your retail business can turn digital screens into real-time decision engines boosting conversions and improving shopper engagement without manual intervention.
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Implementation roadmap for retail AI

Implementing AI in retail requires careful planning and a phased approach. Here’s how retailers can get started:

  1. Identify specific business problems that AI could solve, such as inventory management challenges or customer service bottlenecks.
  2. Start small with one AI solution that addresses a clear pain point before expanding to other areas.
  3. Ensure data quality by organizing and cleaning existing customer and inventory data.
  4. Train staff on how to work with AI systems and interpret their recommendations.
  5. Measure results by comparing key metrics before and after implementing AI.

How Solink helps retailers drive profit margins and reduce loss

Solink empowers physical retailers to combat shrink, streamline operations, and improve visibility across all locations through video and data analytics, all without the need to rip and replace their existing hardware.

Solink is more than a video platform, it’s a vision intelligence system that integrates security footage, POS data, and AI to surface real-time insights and automate incident detection. 

Solink links your video footage with key store data, making it easy to investigate issues like theft, procedural violations, and cash discrepancies. You can search footage by transaction, keyword, or behavior (e.g., “no-scan at self-checkout”), saving hours of manual review.

With the insights you get from Solink, you can gain complete visibility into what’s working, what’s not and where to act across your business to prevent loss and drive profit margins.

Want to see first-hand how 30,000+ customers are benefiting from using Solink? Book a demo today.
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FAQs about AI solutions for retail

What is the average time to see results from retail AI solutions?
Most retailers see measurable improvements within 3-6 months after implementing AI solutions, with inventory management and pricing optimization typically showing the quickest results.
Retail AI systems commonly use transaction history, inventory levels, customer profiles, website behavior, in-store traffic patterns, and competitor pricing data to generate insights and recommendations.
AI prevents stockouts by analyzing historical sales data, seasonal trends, and external factors to predict future demand, then automatically generating purchase orders before inventory reaches critical levels.