Retail technology encompasses the digital software, platforms, and innovations that help retailers manage operations and serve customers. While categories like payments, back-end infrastructure, and in-store systems remain relevant, AI has emerged as a defining force in retail tech for 2026. In 2025, AI moved from experimentation to infrastructure, with 89% of retailers and CPG marketers saying AI-based solutions increased their revenue, according to NVIDIA's State of AI in Retail survey. This shift is reshaping product discovery, customer service, retail media, and supply chain operations.

What is retail technology?

Retail technology refers to digital software, platforms, and innovations that help retailers manage and optimize operations. The category spans front-end tools that shape customer experience (payments, product discovery, personalization) and back-end systems that drive efficiency (inventory management, demand forecasting, customer data platforms).

The scope has expanded in recent years. Where retail tech once meant point-of-sale terminals and ecommerce platforms, it now includes AI-powered shopping assistants, agentic commerce tools, and automated fulfillment systems. Retailers with AI capabilities saw 14.2% sales growth between 2023 and 2024, compared to 6.9% for those without, according to Capital One Shopping research. This performance gap explains why AI technology in retail boasts a projected 32.5% compound annual growth rate (CAGR) in the US between 2025 and 2030.

What are the main categories of retail technology?

Retail technology spans multiple functional areas, each serving distinct operational needs:

• Payments and checkout. Digital wallets (Apple Pay, Google Pay), buy now, pay later services (Affirm, Klarna, Afterpay), and contactless payment systems streamline transactions. Cashierless checkout and self-service kiosks reduce friction in physical stores.
• Ecommerce infrastructure. Headless commerce separates front-end interfaces from back-end systems via APIs, enabling flexibility across channels. Composable commerce allows retailers to select specialized vendors for each function rather than relying on monolithic platforms.
• Customer data and personalization. Customer data platforms (CDPs) like Segment, BlueConic, and Tealium consolidate first-party data to create unified customer profiles. These systems power recommendation engines, loyalty programs, and targeted marketing.
• Operations and fulfillment. Demand forecasting, inventory optimization, and warehouse automation improve efficiency. Walmart now offers drone delivery in five states as part of its fulfillment network investments, according to EMARKETER.
• In-store technology. Digital signage, electronic shelf labels, and retail media network screens create new advertising inventory while enhancing the shopping experience.

How is AI reshaping retail technology in 2026?

AI has transitioned from add-on tools to core infrastructure across retail operations. This shift manifests in three areas: customer-facing applications, operational systems, and advertising optimization.

Consumer-facing AI includes shopping assistants like Amazon's Rufus and Walmart's Sparky, which handle product discovery, answer questions, and guide purchases. AI chatbot traffic to US retail sites increased 670% year-over-year during the 2025 holiday season, according to Adobe data cited by EMARKETER. Beyond customer service, AI powers visual search, personalized recommendations, and dynamic pricing across ecommerce platforms.

Operationally, retailers deploy AI for demand forecasting, inventory management, and supply chain optimization. Walmart has embedded internal AI "super agents" for sellers, suppliers, and employees to automate routine decisions, according to EMARKETER. On the advertising side, AI drives programmatic ad buying, audience targeting, and creative optimization across retail media networks.

What is agentic AI and how does it apply to retail?

Agentic AI refers to systems that can plan, reason, and act autonomously with minimal human input. Unlike traditional AI that responds to commands, agentic AI combines memory (recalling user preferences), tools (accessing external databases and APIs), and reasoning (breaking up complex requests into actionable steps) to complete tasks end-to-end.

In retail, agentic commerce allows AI agents to handle the entire shopping journey: searching for products, comparing options, applying coupons, and completing purchases. Amazon's Rufus now includes an "Auto Buy" feature that authorizes purchases when items hit target prices. OpenAI launched Instant Checkout, transforming ChatGPT into an end-to-end shopping platform. Perplexity's agentic shopping tool pulls from user search history to personalize recommendations, according to EMARKETER.

The market is nascent but growing quickly. AI platforms will account for 1.5% of US retail ecommerce sales in 2026, or $20.9 billion, nearly quadruple 2025 figures, per EMARKETER projections. Among Gen Z and millennials, 58% say they would trust an AI agent to compare prices and recommend options, according to SAP research.

How is AI changing retail media and personalization?

Retail media networks (RMNs) use retailer first-party data to target advertising across on-site placements, social extensions, and connected TV. AI enhances every layer of this ecosystem: audience segmentation, bid optimization, creative personalization, and measurement.

US advertisers will spend $69.33 billion on retail media in 2026, up 17.9% from $58.79 billion in 2025, according to EMARKETER's H2 2025 forecast. Amazon Ads and Walmart Connect dominate, capturing 89.5% of incremental spending in 2026. Walmart Connect is the only RMN expected to gain share through 2027, with 11% of incremental spending in 2026, according to EMARKETER’s forecast.

AI-powered personalization extends beyond advertising. Recommendation engines now drive 71% of ecommerce product suggestions, per EComposer data. Pinterest's AI "taste graph" processes billions of signals to personalize discovery for 600 million monthly users, with Performance+ campaigns often delivering more than a 20% reduction in cost-per-acquisition, according to EMARKETER.

What challenges should retailers consider with AI adoption?

Despite rapid adoption, AI introduces specific risks that retailers must manage:

• Data privacy and security. Conversational AI interfaces encourage users to share detailed preferences and personal information, expanding the attack surface for data breaches. Nearly 70% of organizations identify the fast-moving generative AI ecosystem as a top security concern, according to Thales research.
• Platform dependency and fragmentation. External AI shopping agents threaten retailer control over product discovery. Shopify, Amazon, and Walmart have begun restricting external AI agent activity within their ecosystems, and Walmart recently added guidelines preventing agents from completing checkout, per The Information.
• Scalability gaps. While 44% of retailers use AI weekly or more, only 11% say they are ready to scale AI across the business, according to Amperity’s 2025 State of AI in Retail study.

How should retailers prioritize retail technology investments in 2026?

Investment priorities depend on retailer scale, category, and existing capabilities. Four principles apply broadly:

• Start with data infrastructure. AI effectiveness depends on data quality. Retailers lacking unified customer data should prioritize CDP implementation before advanced AI applications. First-party data becomes more valuable as third-party cookies deprecate and external AI agents seek retailer partnerships.
• Evaluate build vs. buy vs. partner. For most retailers, partnering with established AI platforms offers faster time-to-value than building proprietary systems. Walmart's OpenAI partnership exemplifies this approach: enabling ChatGPT shopping via Instant Checkout rather than building competing infrastructure.
• Prepare for agentic commerce disruption. External AI shopping agents could reshape product discovery and reduce visibility for brands relying on traditional search and sponsored placements. Retailers should implement generative engine optimization (GEO) strategies, including clear product metadata, indexable reviews, and full-sentence descriptions that AI systems can extract.
• Balance automation with oversight. AI can automate pricing, inventory, and customer service decisions, but retailers need governance frameworks to monitor for errors, bias, and brand-damaging outputs. The 11% of retailers ready to scale AI distinguish themselves through established oversight processes, not just technology investment.

We prepared this article with the assistance of generative AI tools and stand behind its accuracy, quality, and originality.

EMARKETER forecast data was current at publication and may have changed. EMARKETER clients have access to up-to-date forecast data. To explore EMARKETER solutions, click here.