Top 9 Retail Technology Trends & Innovations in 2026

Regional retail market leaders, SaaS innovators, and vast omnichannel empires crave the answer to one question: how will your business stay relevant? Retailers need to offer what their customers want, while SaaS providers need to make sure their clients are succeeding in that mission.
MobiDev’s expertise in retail software development extends over a decade. Their experience is an incredible resource for any business leader looking to get ahead of the curve. That’s why I have been eager to learn about innovative retail technology trends each year with MobiDev’s Solution Architect, Iurii Luchaninov, and his team, so that you can keep your value proposition competitive.

Retail Technology Overview 2026

Retail technology includes every system, tool, and technical practice that advances shopping experiences, supports the business operations of retailers, and strengthens market competitiveness.

We’ve covered a lot of trends in retail technology over the last several years. Many technologies that were innovative at one point have become mainstream. You can explore some of these later in the article. It’s amazing how quickly brand-new technologies can embed themselves into an industry and become a must-have! It’s important to get ahead of the game before you fall behind.

What Are the Top Retail Technology Trends for 2026?

Some of the trends we’ll explore below include generative AI agents for merchandising and content, computer-vision checkout and smart carts, IoT shelf sensors and ESLs, augmented reality product visualization, and omnichannel BOPIS. Let’s explore those trends in more detail.

The table below shows a broad overview of the technologies that are defining retail this year:

Trend #1: Agentic Commerce and Generative Engine Optimization (GEO)

Omnichannel marketplaces are and should be built for humans. However, reaching more humans might mean reaching more bots. The number of customers who shop with AI assistants is rising. That’s why agentic commerce is a frontier retail technology trend in 2026.

This trend pushes businesses to invest more in Generative Engine Optimization (GEO) or AISEO, as it’s sometimes labelled. Businesses need to build and optimize content to secure their visibility to AI tools and ensure their products will be offered by AI assistants.

Furthermore, agentic commerce goes beyond chatbots simply providing information about products. AI agents directly assist customers in shopping and checkout experiences.

In an advanced, near-future case, simply asking an AI agent to order a new pair of shoes in your size would result in the agent finding shoes you like, checking with you conversationally, and then placing an online order on your behalf.

Taking advantage of this growing opportunity means making your storefronts agent-ready with easily digestible and navigable web content so AI can recommend your products.

How to Implement the Agentic Commerce Trend

To prepare your platform for the agentic commerce, you need to :

Step 1. Build or extend commerce APIs so external agents can query the catalog, calculate totals, apply promotions, and initiate checkout securely.

Step 2. Track emerging standards like UCP and partner requirements.

4 GEO Tips to Get Your Website Ready for the AI Tools Discovery:

1. Ensure that your storefront has accurate titles, attributes, tags, descriptions, and policy information.
2. Provide clear answers to the questions your customers might ask. For example, they may ask a chatbot questions about your product, and that information on your website will allow the chatbot to answer more accurately.
3. Instead of growing your website’s traffic, grow trust with your customers by providing them with information about the products they need.
4. Monitor your website performance in AI tools and make necessary changes where your store isn’t cited.

Example: Walmart and Gemini

Google and Walmart’s CEOs recently announced that Walmart would be adopting the Gemini Universal Commerce Protocol, a way to more deeply integrate end-to-end shopping experiences into Google. Consumers will soon start seeing buy buttons embedded directly in various Google experiences like search, AI mode in Chrome, and in Gemini conversations.

In early 2026, Google Chrome announced the launch of WebMCP, a new protocol that enables AI Agents to interact with the website functionality at increased speed and with higher reliability. The new protocol covers cases like customer support, ecommerce, and bookings for travel. It offers 2 APIs: Declarative (for standard HTML) and Imperative (for JavaScript).

Trend #2: Retail Media Networks 2.0 (Especially In-store!)

Stores are seizing opportunities to improve omnichannel experiences through in-store and online retail media networks (RMNs). eMarketer predicts that retailers will spend over $74B this year in the United States alone. The spend will continue growing with a projected CAGR of 17.2%.

Nielsen’s 2025 Global Annual Marketer Survey revealed that 55% of retailers globally use RMNs as a primary sales driver, though the percentage varies depending on the industry: from 61% of automotive firms to 47% of businesses operating in travel and tourism. The rising value of RMNs across industries presents an opportunity for retailers to use RMNs as a profit center both online and in-store.

How to Implement Retail Media Networks 2.0 Trend

Brands are looking for ways to use retail media networks to support their needs. Appealing to these needs is key to staying ahead in 2026. To implement this trend, your retail business needs to:

Step 1. Map your RMN inventory and standardize each ad placement.

Step 2. Set privacy and data governance rules.

Step 3. Implement consistent tracking of your ads across multiple channels.

Step 4. Build a reporting system that lets brands connect campaigns to sales performance and compare channels.

Step 5. Enable incrementality testing to understand the impact of ads on sales vs demand shift.

Step 6. Provide operational controls, e.g., for ad frequency.

Trend #3: Unified Data Foundations and Data Governance for AI

Messy data is a pain, but successful retail businesses know how to manage it. One of the most important retail trends of 2026 is producing clean and unified data systems; achieving that requires new governance models.

According to Gartner, 75% of IT application leaders are dabbling in AI agents in some capacity with their teams. 15% reported they were even experimenting with AI agents that are fully autonomous.

BizTech Magazine cited IDC Retail Insights Vice President for Research Ananda Chakravarty, who claimed that retailers aren’t ready for the shift to autonomous agentic AI. With a lack of data governance standards, agentic technologies are the wild west. Understandably, retailers are skeptical of the technology; only 19% of respondents to the above-mentioned Gartner’s study expressed trust in the ability of the vendors to tackle the hallucination issue. Three-quarters believed that those agentic technologies present serious security vulnerabilities. Addressing these pain points with better data foundations and data governance will be a key trend to follow in 2026.

How to Implement Unified Data Foundations and Data Governance for AI

Step 1. To set better standards for your data, identify your highest-value data domains: think products, customers, inventory, orders, pricing, and promotions.

Step 2. Assign owners to each KPI to maximize quality.

Step 3. Bring together all your important data into one organized system. You might use a Customer Data Platform (CDP), Master Data Management (MDM) tool, knowledge graph, or use a lakehouse approach.

Step 3. Clean data using consistent identifiers and deduplicating as much as possible. Governance reinforces trust and reliability.

Step 4. Set access controls, PII handling, model risk checks, and human approvals. Focus on high-impact actions, like pricing, refunds, credit, and compliance.

Step 5. When adding AI workflows, start with two to three that enforce discipline, like customer service deflection, replenishment suggestions, and fraud flags. Use these workflows to harden your data contracts.

Read our Guide to Leveraging AI for Overcoming Data Silos in Omnichannel Retail Systems

Trend #4: Enterprise Orchestration (End-to-end Decisions Across Product, Demand, Supply, Fulfillment, Finance)

In the service world, providing consistent service is key to the success of a brand. When you order a burger from a major fast-food chain, we expect it to taste the same no matter which franchise we go to. In 2026, successful retailers are following this example across the entire enterprise: every action is coordinated. Use shared rules and real-time signals to guide decisions across the entire business.

How to Implement Enterprise Orchestration

Step 1. Start by identifying the costliest cross-functional decisions:

• Promos vs inventory
• Substitutions
• Ship-from-store vs distribution center
• Labor allocation

Step 2. Collaborate with your teams to agree on clear decision guidelines that prioritize what matters most. Consider profit, quality of service, sustainability, or risk management.

Step 3. Connect all your systems (OMS, WMS, ERP, forecasting, POS, ecom) to make sure that they share the same information.

Step 4. Test coordinated approaches in one area, such as how you manage orders across different channels.

Step 5. Once you can see the real impact on things like profit margins, on-time delivery, canceled orders, and labor hours, you’ll know it’s working and can roll it out to more parts of your business.

Trend #5: Autonomous and Resilient Supply Chains (AI-driven Planning and Execution)

Reducing disruption to supply chains is another key retail trend in 2026. Ultra-responsiveness is the new standard. Whether it’s customer behavior, shrinkage, or disruptions to other parts of the supply chain, forecasting isn’t always enough. Businesses are already looking to address this pain point. Upshop 360 is aiming to advance grocery stores with a real-time, AI-integrated bird’s-eye view of their operations. At the National Retail Foundation’s 2026 expo, Upshop demonstrated that demand, production, and fulfillment can come together in one platform, making it easier for stores to respond and predict changes.

In the past, businesses periodically performed supply and demand forecasting to drive ordering processes. However, novel techniques this year add continuous sensing to the equation. Using AI and other technologies to monitor data on product counts, exceptions, disruptions, substitutions, and allocation allows enterprises to automate routine decisions. Humans can then focus more on edge cases that deserve greater attention.

How to Implement the Autonomous and Resilient Supply Chains Trend

For this trend, data is gold; without good data, you can’t reliably automate effective supply chain decisions. Here’s how it works:

Step 1. Improve signal quality by focusing on areas like sell-through, lead times, supplier performance, returns, and local events. You can then feed that higher-quality information into forecasting and replenishment systems.

Step 2. Add exception-driven workflows when you detect forecast error spikes, lead time drift, and fill-rate drops.

Step 3. Hook AI into your data to propose actions in response to the data you collect, like reorder, reallocate, substitute, and expedite.

Step 4. Don’t trust AI blindly; demand human approval until performance is proven. Make collaborative processes more visible to your suppliers, too.

Learn How To Integrate AI In Supply Chain Management To Optimize Business Processes

Trend #6: Returns and Reverse Logistics as a Profit System (Not Just a Cost Center)

Return and reverse logistics are great for maintaining customer loyalty, but no one would blame you for writing the whole thing off as just another cost center. Store employees and supervisors are trained to minimize returns as much as possible, and customers are given strict deadlines and rules when returning products.

It’s for good reason. According to Statista Research Department, in 2024, returns from online sales totaled at $362.16B. According to NRF Research, expected retail industry returns exceeded that value twofold in 2025, reaching $849.9 billion. With the cost of returns on businesses rising, what can you do to mitigate that cost, or even transform it into a profit system?

In 2022, researchers from the Technical University of Denmark explored the profitability of a number of reverse supply chains at the end-product, component, and packaging levels. They found that processes like product refurbishment and resale, reducing waste and reusing packaging, and avoiding procurement and material costs can make reverse logistics profitable in a sort of ‘circular economy.’

Despite its perceived costs, reverse logistics can save budgets and provide opportunities to make more money, making it a valuable retail trend to consider in 2026.

3 tips to turn your returns and reverse logistics into profit:

1. Treat returned goods as an opportunity, not waste. Refurbish, resell, and upsell. Launch a ‘certified refurbished’ program to maintain customer trust.
2. Prioritize value recovery over logistics optimization. Consider component-level recovery and reuse of packaging materials. Route returns to the highest-value disposition, not the fastest.
3. Make returns a strategic input: share returns data across the business, especially product design, to avoid future defects. Track how refurbished sales influence customer lifetime value, not just unit margin.

How to Implement Returns and Reverse Logistics as a Profit System

Step 1. Implement Analytics Tools to identify the major reasons of returns and fix them through pre-purchase prevention.

Step 2. Automate the Return Intake and Inspection by barcode/RFID scanning and camera checks that combine AI and computer vision.

Step 3. Adopt the analytics supply chain tools to reroute returns to the best possible location.

Step 4. Use analytics tools to make a decision on whether each particular returned item should be restocked, resold, refurbished, liquidated, donated, or recycled.

Step 5. Incorporate AI for fraud detection (identification of suspicious patterns and timely response).

Trend #7: AI, ML & Generative AI in Retail

How can AI transform retail operations in 2026? AI models analyze and operate on data to perform demand forecasting, eliminate stock shortages, automate purchase orders, and generate recommendations for customers. Computer vision can aid inventory management and loss prevention operations as well. According to IHL Group, retailers with AI-analytics see 5-6% higher sales and profit growth than their competitors.

Use case #1: AI Demand Forecasting

When we explore how leading retailers are using data and analytics in 2026, AI demand forecasting is one of the biggest examples. Utilizing real-time data from POS systems and loyalty and supply-chain signals, AI/ML platforms can act automatically to deliver personalized offers, optimize pricing, and most importantly predict when items will be in most demand.

Although the AI hype cycle is a recent development, AI’s use in demand forecasting has been a trend for a lot longer than that. In fact, retailers started using demand forecasting powered by machine learning as early as the 2010s. A few years earlier, McKinsey (2022) reported that AI demand forecasting can reduce supply chain errors by 20-50%. They also report a reduction in lost sales of 65%.

AI demand forecasting has been a growing technology for over a decade; a few key market evolutions are responsible:

1. Greater data availability: thanks to grander omnichannel strategies in-store and online, more sources of buying data are available. Data from POS systems can be collected, analyzed, and fed directly into ML demand forecasting systems.
2. Consumer expectations: AI demand forecasting has grown in use because customers are demanding more personalized experiences and immediate availability of the products that they need.

As a result, not only has it become easier to begin using demand forecasting systems and upgrade existing ones, but it’s become a market necessity. According to Nvidia’s State of AI in Retail report from 2024, 27% of surveyed businesses answered that they were using AI for demand forecasting to enhance their supply chain systems. AI demand forecasting can help you put the products your customers are shopping for on the shelf when they need it.

Use Case #2: Personalizing User Experiences with Data Analytics

As explored in the data governance section, higher quality and quantity of data improve supply chains, but what about the shopping experience? The same data that supports demand forecasting and automated supply chain optimization techniques can be used to build unique customer profiles.

AI/ML systems can analyze what individual shoppers are buying and use their profiles to power recommendation engines. This has been used to significant effect by e-commerce platforms like Amazon for years. The Salesforce “State of the Connected Customer Report” (2022) found that 56% of customers expect all retail offers to be personalized, making AI-recommendation engines critical to compete.

Use Case #3: Geofencing and AI Personalization

Combining AI with geofencing unlocks allows retail businesses to deliver hyper-personalized marketing campaigns to their customers while spending most of their time and resources on implementation. Once a customer enters an area in physical space, they’ll receive tailored notifications and set off timely automations that are relevant to that customer and location.

Use Case #4: Shopping with Generative AI

AI’s ‘slop’ era is ever pervasive, but that hasn’t stopped brands from finding relevant and valuable generative AI use cases in retail. Some newer players like Stitch Fix and AIUTA have introduced new AI features and tools. Stitch Fix’s tools can return a set of generative suggestions for shoppers based on their preferences. Meanwhile, AIUTA uses ML to enhance virtual try on; shoppers and businesses alike can use the tool to visualize products on themselves or virtual models.

That’s the core superpower of generative AI and ML synergy. Many of these tools share the following common components:

• A database of their various products with keywords that describe their various scents.
• Semantic and vector search techniques based on NLP, extract keywords from user queries.
• A recommendation engine that understands the user’s profile and suggests products that match the keywords of their query, returning an answer with humanlike responses like ChatGPT.

It’s critical to ensure that you are providing something valuable to your customers, not just “slop.” A 2025 study conducted by Northwestern University’s Kellogg School of Management investigated how 50,000 participants distinguished AI-generated content from reality. Respondents were able to identify AI-generated content 83% of the time. This is critical when considering the findings of a Washington State University survey found that including the phrase “artificial intelligence” in the description of a product can reduce customer purchase intent.

Use Case #5: Conversational BI for Self-Service Access to Insights

Good business leaders know that the closer they can get to their data, the better. That’s why conversational business intelligence (BI) is a hot retail tech trend. By simply starting a conversation with a chatbot, users can get rich information about company data with visual reports.

There are two components that make this kind of interaction possible:

• NLSQL (Natural Language to SQL): the compatibility layer; takes user queries and creates commands that work with databases.
• NL2GraphQL: a similar technology that focuses on GraphQL APIs. These APIs are common among SaaS platforms.

Today’s leading platforms go even further by layering in knowledge graphs, vector search + retrieval-augmented generation, and auto-charting, so the bot “understands” business concepts, finds the right data on the fly, and responds with instant charts and insights. Together these additions close the loop from query to visualization and even proactive alerts.

Conversational BI is valuable for any use case that can benefit from data analytics and rich, visual data reporting. This might include inventory & supply chain management, sales performance monitoring, customer behavior analysis, and loyalty and promotion optimization. Since this technology is so easy to use, it empowers store managers and regional directors to take charge of their stores’ data and create visual reports quickly and easily to guide their strategic decisions.

Use Case #6: Sentiment Analysis and Review Management Systems

According to the Northwestern Medill Spiegel Research Center, reviews and ratings are important for product sales, especially for higher price items. With 95% of shoppers looking to online reviews before making a purchase, retailers need to consider how their stores and products are discussed in testimonials online. They also recommend responding to reviews, especially negative ones.

For small retailers, human-led responses are the most cost-effective. However, larger businesses might resort to templates, outsourcing them to external firms, or copying and pasting from ChatGPT. Many of these options are just not effective, scalable, or cost-effective depending on your business size.

Some startups, like RightResponse AI, have begun to explore directly connecting review management software with generative AI. By providing AI models with relevant information about your store and products, agents can respond to customers at scale with relevant and useful information.

Learn more about Artificial Intelligence in Retail, Its Use Cases, Challenges and Best Practices for 2026

Trend #8: In-Store IoT: ESLs, RFID, and Smart Shelves

Which retail technologies are helping increase in-store sales in 2026? Computer-vision can help reduce abandoned carts, and electronic shelf labels with dynamic promotions can boost sales. Predictive analytics powered by in-store IoT devices can deploy personalized offers via shopping apps and kiosks.

As we saw in the previous trend, IoT in all industries is growing rapidly. In retail, several technologies become immediately relevant:

• Electronic Shelf Labels (ESLs): allow retailers to dynamically change prices of items without manual label replacements by workers.
• Radio-Frequency Identification (RFID): allows workers to track items more easily in a store. This is effective for inventory tracking and online order fulfillment.
• Smart Shelves: sensors built to detect when products are present on a shelf and the quantity of those items.
• Point of Sale (POS) Systems: self-checkouts and cash registers are valuable data sources that can support inventory management efforts.

These technologies benefit retail businesses in ways beyond just inventory management and dynamic pricing. They can also advance the efforts of supply chain visibility, customer behavior analytics, customer experience, personalization, and energy management. By installing more sensors and leveraging data, you can make strategic decisions that can benefit all facets of your stores.

What Future Technology Trends Should Retailers Watch to Stay Competitive?

Expect to see composable, API-first architectures soon. These allow retailers to swap in AI services or new payment systems without replacing your entire platform. Edge computing will protect privacy in computer vision processes, which is critical as regulations tighten.
Multimodal sensing (vision, RFID, and LiDAR) research suggests that more scalable autonomous-store rollouts are on the horizon. Digital twins in retail aren’t science fiction anymore; they are becoming a foundational layer for real-time decision-making in modern retail operations.

Recent Retail Technology Trends That Have Become Must-Haves

Some trends that we’ve covered in previous years have become mainstream. These are must-haves that you can’t ignore. If you aren’t taking advantage of these trends yet, it’s time to start.

Trend #1: Computer-Vision Checkout & Smart Stores

Is now the right time to invest in smart retail shelves or cashierless checkout? With nearly 40 billion IoT devices set to be installed worldwide by the end of 2029 per Ericsson, businesses in all industries have been greatly valuing the power of data. One of the greatest opportunities to utilize that data is to build smart stores or computer vision-assisted checkout systems. However, these systems must prioritize the needs and well-being of customers and employees alike.

Automated checkout technology has been growing, but not without obstacles. In 2024, Harvard surveyed 14,000 employees across 135 service industry brands. The results of the survey suggest that there may be a relationship between understaffing and the presence of self-checkout. Additionally, they found that employees were more likely to be disrespected by customers if a store was understaffed, and 14% more likely to be bullied if a self-checkout was installed in their store.

That tracks with major retailers’ decisions related to customer experiences. Walmart and other stores have been rolling back self-checkout at its stores, with Walmart in particular explaining that this will offer “more personalized and efficient service”.

Although self-checkout has experienced these obstacles, it still has potential. Instead of fixating on “just walk out” models, computer-vision checkout and smart stores can focus on more scalable solutions, such as:

• Loss prevention cameras: computer vision systems can be trained to identify theft and alert human assets protection team members to investigate.
• Shelf analytics: shelf sensors can help keep inventory counts accurate, supporting forecasting systems and alerting employees about zoning needs.
• Digital twins: computer vision systems can be a critical data source for mirroring your entire store’s operation virtually.

These options make operations more efficient while still retaining the human element of service in your stores.

Trend #2: In-Store IoT: ESLs, RFID, & Smart Shelves

Which retail technologies are helping increase in-store sales in 2026? Computer-vision can help reduce abandoned carts, and electronic shelf labels with dynamic promotions can boost sales. Predictive analytics powered by in-store IoT devices can deploy personalized offers via shopping apps and kiosks.

As we saw in the previous trend, IoT in all industries is growing rapidly. In retail, several technologies become immediately relevant:

1. Electronic Shelf Labels (ESLs): allow retailers to dynamically change prices of items without manual label replacements by workers.
2. Radio-Frequency Identification (RFID): allows workers to more easily track down items in a store. This is effective for inventory tracking and online order fulfillment.
3. Smart Shelves: sensors built to detect when products are present on a shelf and the quantity of those items.
4. Point of Sale (POS) Systems: self-checkouts and cash registers are valuable data sources that can support inventory management efforts.

These technologies benefit retail businesses in a number of ways beyond just inventory management and dynamic pricing. They can also advance the efforts of supply chain visibility, customer behavior analytics, customer experience, personalization, and energy management. By installing more sensors, collecting more data, and crucially, leveraging that data, you can make strategic decisions that can benefit all facets of your stores.

Read our complete guide on IoT in Retail, Transforming Shopping with Smart Tech

Trend #3: Augmented Reality & Spatial Commerce

What new technologies are improving customer experiences in retail? Among all the technologies on this list, like chatbots and electronic labels, the most unique and engaging of these technologies is augmented reality. Customers continue to demand augmented reality in retail as a necessary part of an omnichannel shopping experience. In 2022, Allied Market Research reported that AR in retail will reach a market size of $61.3 billion in 2031.

The most widely used application of augmented reality in retail is virtual try on. The “try before you buy” model remains a powerful marketing and sales asset that makes customers feel more comfortable before they buy something.

Another opportunity for AR to improve retail operations is by assisting with planogram compliance. This improves consistency, product visibility, sales, inventory management, and provides a high-quality shopping experience for the customer. To support that mission, it’s possible that 3D planograms overlaid on top of a shelf or section of the store using AR could support retail workers. This could help instruct employees on how to arrange shelves, zoning devices, merchandising displays, and products in accordance with the planogram.

Many stores are already using devices that could be used to display 3D planograms. For example, the Zebra TC-51 portable computer and its successors are ARCore-capable Android devices. This has potential to make retail workers more efficient and compliant with planogram setups.

Learn more about Augmented Reality in Retail, Use Cases, Challenges and Best Practices for Leveraging in SaaS

Trend #4: Omnichannel Retail & BOPIS

Customers expect consistency. Salesforce reports that 71% of customers prefer omnichannel experiences.

Buy Online Pick Up in Store (BOPIS)

More retailers than ever are using order pickup solutions at brick-and-mortar stores. According to Capital One, 2023’s BOPIS sales accounted for 9.93% of ecommerce sales, with over $132.8 billion spent and is the third most-used fulfillment method by online shoppers.

BOPIS offerings require strong supply chain management, as well as the proper staffing to reach maximum effectiveness. As stores reduce hours and positions, it becomes harder for the remaining staff to maintain BOPIS operations. As a result, the quality of service for the customer decreases, which in turn decreases customer satisfaction and loyalty. However, when staffed appropriately, the benefits are worth it for both your organization and for consumers.

Trend #5: Secure and Frictionless Payments

Data breaches and identity theft are becoming more common over time. Experian’s 2024 US Identity and Fraud Report found that 84% of surveyed consumers were worried about identity theft. Since over 3,000 data breaches occurred in 2023 alone, those fears are warranted.

Meanwhile, as customer security concerns continue to rise, so do options for consumers to pay for products.

Mag Stripe Phase Out

The evolution of payment methods continues with saying goodbye to the mag stripe. MasterCard recently decided to start phasing out magnetic stripe payments on its credit cards. This is cheaper and safer, as it will eliminate the threat of credit card skimmers that thieves use to copy the information off mag stripes. However, smart card microchips on credit cards can still be stolen with small card shimmers that are difficult to detect when shoved inside of a POS terminal. It’s critical that you educate your staff on how to detect these devices and protect your customers.

Contactless Payments

Contactless payments are also exceedingly popular and secure. NFC-based cards are convenient, fast, and difficult to steal and replicate. NFC integration with smartwatches is also popular.

Digital Payment Services

Partnering with the right digital payment gateway can be a great supplement to your existing POS system or even a great alternative to running your own POS system. Options like Stripe, PayPal, Authorize.net, and others are popular options and can make paying for your products more convenient for your customers.

Integrating payment processing with a POS system may seem like a relatively easy task, but even here some pitfalls can hinder the success of your product. Knowing the challenges and best practices, you can mitigate risks and implement powerful payment processing capabilities in your POS product.

Preventing Payment Fraud with AI

AI plays a crucial role in preventing payment fraud by detecting and mitigating suspicious activities in real-time. One powerful application is the biometric liveness detection, which verifies the authenticity of a user by assessing real-time physiological or behavioral features and ensuring the presence of a live person during identity verification processes. Liveness checks in payment authentication help prevent fraudulent transactions and account takeovers.

Digital Currencies

This really depends on your audience. Given high transaction fees for popular currencies like Bitcoin, the volatility of their value, accepting cryptocurrency isn’t worth investing resources into unless you have a niche audience.

Trend #7: Digital Transformation & Application Modernization

Should you keep your existing retail software, or should you upgrade? It’s no secret that older systems are subject to maintenance costs, scalability issues, compatibility problems, challenges with adding new features, and a host of other problems. If your legacy systems are still going strong, that’s great! However, how much longer are your legacy systems going to last, and what opportunity cost are you suffering by ignoring product modernization?

Your competitors are investing deeply in artificial intelligence, augmented reality, omnichannel experiences, and other technologies for retail software development. Even if your legacy retail systems are working as planned, it may be difficult to take advantage of the full potential of these technologies and keep up with your competition if you don’t modernize. Let’s look at some examples.

Application Modernization

According to a survey conducted by Retail Consulting Partners in 2021, over half of industry POS software and hardware systems are over 5 years old. Assuming those surveyed still haven’t upgraded, those systems are now over 8 years old. In fact, according to that report, only 21% of those surveyed utilize POS software newer than two years.

Upgrading your systems is expensive. However, you need to start planning for when you need to make that upgrade before your competition gets too far ahead in the race and before your customers lose faith in your brand.

Application modernization isn’t just about keeping up with the pack. It’s also about enhancing security, increasing flexibility, and improving the performance of your business. Security is a hot topic right now. Just think—how many vulnerabilities are lurking undiscovered in your legacy system? What’s the risk of your customer data being leaked to the dark web?

Working with a strong and experienced tech company can help you audit your legacy system and help you decide if and when it will be time to upgrade.

If the topic of modernization resonates with you, check out the preview of our webinar, where MobiDev experts explain why upgrading your IT infrastructure and legacy software systems is key to driving innovation in your business. You’ll find the link to the full webinar recording below the video.

POS Modernization Trends

Modern Point of Sale systems are evolving to support more popular payment methods, cloud-based systems and omnichannel experiences. One of the most important POS technology trends is the industry realization that POS systems generate a wealth of data. By using this data with artificial intelligence and machine learning systems, businesses can better understand the buying habits of their customers and better oversee their inventory management.

As an example, AI can help POS systems by advancing retail demand and sales forecasting. By processing omnichannel purchasing data from both digital and in-person POS systems, forecasting can be more accurate. This can enable your business to enhance its ordering and stocking mechanisms by predicting what your customers will want and when.

AI product recommendation systems are another technology that brands are enhancing with their modern POS systems. By tracking what each specific customer is buying in-store and online and via apps, POS screens can deliver targeted recommendations to customers. These sources can also upsell, suggest coupons, and encourage customers to buy other items that they are likely to want based on their unique profile.

POS systems are incredibly valuable as data collection tools. However, they can only function in this way and provide omnichannel experiences if the systems are interconnected. You should connect POS systems to a wider, centralized network that allows you to get a clearer picture of inventory movement.

Build, Modernize, and Scale Your Retail Software Product with MobiDev

When you build software with MobiDev, you work with a team that’s been in custom software development since 2009. SmartTab and Comcash built their software products with MobiDev’s support. You can trust MobiDev with development of ERP software, virtual try-on products, demand/sales forecasting, AI agents and chatbots, recommendation engines, and other innovative retail systems.

You can build, modernize, and scale your retail software with MobiDev’s retail software development services because of our team’s deep understanding of what it takes to keep your product agile and competitive. Contact us today to learn how you can build your retail software product and stand out against the competition.