Retail AI Software Options 2026

Retail has always been a margin game - but AI is changing the rules of how those margins are won and lost. From demand forecasting that learns from thousands of signals in real time, to personalisation engines that adapt every customer interaction individually, to autonomous pricing that responds to competitor moves in minutes rather than days, artificial intelligence is no longer a future investment for retailers - it is becoming the baseline requirement for competing effectively. The retailers pulling ahead in 2026 are those that have moved beyond AI pilots and embedded intelligent automation into merchandising, supply chain, store operations, and customer experience as a core operating model.

This guide covers the leading AI software platforms available to enterprise and mid-market retailers - both purpose-built AI-native vendors and established retail technology players that have embedded AI deeply into their platforms. Viewpoint Analysis is a Technology Matchmaker, helping businesses find and select the right technology fast - and helping IT vendors to get found by the right buyers.

Included Retail AI Software Vendors

This guide covers the following retail AI platforms, evaluated independently across enterprise, mid-market, and specialist tiers. Our viewpoint on each vendor follows below.

Blue Yonder | o9 Solutions | Relex Solutions | Edited | Phrasee | Dynamic Yield (Mastercard) | Algolia | Ocurate | Focal Systems | Quorso | Crisp | Inteliigence | Nextail | Yoobic | Peak AI

What is Retail AI Software?

Retail AI software encompasses platforms and applications that use machine learning, predictive analytics, natural language processing, and computer vision to automate and optimise decisions across the retail value chain. The category spans several distinct functional areas: demand forecasting and inventory optimisation (predicting what will sell, when, and where); pricing and promotion optimisation (dynamically adjusting prices and promotional spend to maximise margin and revenue); personalisation and customer experience (tailoring product recommendations, content, search results, and communications to individual shoppers); store operations intelligence (using computer vision and sensor data to optimise in-store execution, labour scheduling, and shrinkage prevention); and merchandising and range planning (using AI to inform buying decisions, assortment architecture, and markdown strategies).

What distinguishes retail AI software from traditional retail analytics is the shift from reporting on what has happened to recommending or automating what should happen next. Modern retail AI platforms ingest data from multiple sources - ERP, POS, e-commerce platforms, third-party market data, weather, and social signals - and produce decisions or recommendations at a speed and granularity that is impossible for human analysts alone. For a broader view of the AI technology landscape, see the AI Technology Selection pages on the Viewpoint Analysis website. Retail AI also intersects closely with supply chain technology - the Supply Chain Technology pages cover the broader planning and execution landscape relevant to retailers.

How to Find Retail AI Software

The retail AI market is one of the fastest-moving in enterprise technology, with new vendors emerging regularly and established platforms adding AI capability at pace. The risk for retail technology buyers is evaluating vendors based on marketing claims rather than demonstrated outcomes - AI capability in retail is highly use-case specific, and a platform that delivers proven demand forecasting results for a grocery retailer may be poorly suited to a fashion or home goods business with fundamentally different demand patterns and data characteristics.

The fastest way to build a relevant, matched vendor longlist is to use the Longlist Builder at Viewpoint Analysis. It takes a few minutes and asks the right questions about your retail format, the specific AI use case you are prioritising, your existing technology stack, and your organisation size - returning a tailored vendor list rather than a generic market overview.

For buyers who want vendors to come to them and pitch against specific requirements, the Technology Matchmaker Service manages that process end to end - Viewpoint Analysis writes your Challenge Brief, qualifies the most relevant vendors, and runs a structured pitch process that gets you to a credible shortlist fast. Think Dragons' Den or Shark Tank for retail technology.

Retail AI for Demand, Supply Chain, and Merchandising

Blue Yonder is one of the most established AI-driven retail planning platforms, with deep capabilities in demand forecasting, replenishment, supply chain planning, and workforce management. Blue Yonder's Luminate platform uses machine learning to generate demand forecasts at SKU and store level, incorporating external signals including weather, promotions, local events, and economic indicators alongside internal sales history. Its replenishment and inventory optimisation capabilities are used by some of the world's largest grocery, general merchandise, and fashion retailers, and the platform's breadth - spanning supply chain planning, warehouse management, and store execution - makes it a natural choice for retailers that want AI embedded across the full supply chain rather than in a single point solution.

o9 Solutions is an enterprise AI platform for integrated business planning, with significant retail and consumer goods adoption for demand planning, merchandise financial planning, and supply chain optimisation. o9's Enterprise Knowledge Graph provides a unified data model that connects demand, supply, finance, and commercial data - enabling retailers to run integrated scenario planning that connects a demand signal change all the way through to margin and cash flow impact in real time. o9 is positioned for large, complex retailers and consumer goods businesses that have outgrown legacy planning tools and need an AI-native platform capable of handling the data volumes and planning complexity of modern omnichannel retail operations.

Relex Solutions is a supply chain and retail planning platform with particular strength in demand forecasting, replenishment, and space planning for grocery, convenience, and general merchandise retailers. Relex's unified platform connects demand forecasting directly to replenishment, production planning, and space and assortment management - reducing the planning silos that cause inventory imbalances in multi-format retail operations. Its AI forecasting engine is consistently rated among the most accurate in the market for fresh and short-shelf-life categories, making it the go-to platform for food retailers where forecast error translates directly into waste, lost sales, or both. Relex has grown significantly across European and North American markets and is a frequent shortlist entry for mid-market and enterprise grocery and general merchandise retailers.

Nextail is an AI-native merchandising platform built specifically for fashion and apparel retailers, focusing on inventory optimisation, allocation, and replenishment across store networks and e-commerce. Nextail's machine learning models account for the particular complexity of fashion retail - short product lifecycles, high SKU proliferation, size and colour fragmentation, and the interaction between online and physical inventory - to optimise stock distribution and minimise both overstock and lost sales. It is used by fashion retailers and brands across Europe and the Americas and is a specialist alternative to the broader supply chain planning platforms for buyers whose primary challenge is fashion merchandise optimisation rather than end-to-end supply chain planning.

Crisp is a retail data network and AI analytics platform that connects consumer goods brands with retailer point-of-sale and supply chain data, enabling real-time visibility of on-shelf availability, sales performance, and replenishment status across retail partners. Crisp is primarily a vendor-side tool - used by FMCG and CPG brands to monitor and optimise their retail performance without waiting for retailer data to arrive via slow EDI feeds - but it is also deployed by retailers seeking to improve supplier collaboration and data sharing. For brands selling through major retail chains, Crisp's ability to surface and act on retail execution issues in near-real-time is a meaningful commercial advantage.

Retail AI for Personalisation and Customer Experience

Dynamic Yield (now part of Mastercard) is one of the most widely deployed personalisation platforms in retail, enabling retailers and brands to deliver individualised product recommendations, content, search results, and offers across web, mobile, email, and in-store digital touchpoints. Dynamic Yield's machine learning engine builds individual customer profiles in real time - incorporating browsing behaviour, purchase history, contextual signals, and affinity data - to personalise every interaction without requiring manual segmentation or rules-based logic. Its acquisition by Mastercard has brought additional data assets and payment intelligence into the personalisation engine, strengthening its relevance for retailers with significant card payment data. Dynamic Yield is used by major global retailers across fashion, grocery, and home categories.

Algolia is an AI-powered search and discovery platform used extensively in retail and e-commerce to deliver fast, relevant product search results and personalised category browsing experiences. Algolia's NeuralSearch capability combines traditional keyword search with vector-based semantic understanding, allowing shoppers to find products using natural language queries rather than exact keyword matches - a significant improvement in search conversion for retailers with large and complex product catalogues. Its personalisation layer adapts search rankings to individual shopper behaviour, surfacing the products most likely to convert for each user. Algolia is typically deployed as a search infrastructure layer integrated into an existing e-commerce platform, and is a standard shortlist entry for retailers investing in search and discovery as a conversion optimisation priority.

Phrasee is an AI-native language optimisation platform that generates and tests marketing copy for email subject lines, push notifications, social ads, and digital content using large language models trained on retail and e-commerce performance data. Phrasee's platform continuously experiments with language variants at scale - testing thousands of copy combinations across a retailer's customer base and learning which language patterns drive the highest open rates, click-through rates, and conversion for specific customer segments. For retailers running high-frequency email and push notification programmes, Phrasee delivers measurable uplift in engagement metrics without requiring manual copywriting resource for every communication. It integrates with major marketing automation and CDP platforms.

Ocurate is a customer lifetime value AI platform that helps e-commerce retailers identify their highest-value customers, predict future purchasing behaviour, and optimise marketing spend allocation accordingly. Ocurate's models predict individual customer CLV at acquisition and throughout the customer lifecycle, enabling retailers to shift marketing investment toward acquiring and retaining customers with the highest long-term value rather than optimising purely for short-term conversion metrics. For retailers with significant paid media spend and a need to improve return on advertising spend while building a more loyal customer base, Ocurate provides the analytical foundation for a value-based marketing strategy.

Retail AI for Store Operations and Execution

Focal Systems uses computer vision and shelf-edge camera technology to automate on-shelf availability monitoring, planogram compliance checking, and out-of-stock detection in physical retail stores. Focal's AI analyses images from cameras mounted on store shelves in real time, identifying empty shelf positions, misplaced products, and planogram deviations without requiring manual store audits. For grocery and convenience retailers where on-shelf availability directly drives sales and customer satisfaction, Focal Systems reduces the labour cost of store audits while improving the speed and accuracy of restocking decisions. It integrates with existing store operations and replenishment systems to trigger automated task assignments when issues are detected.

Yoobic is a frontline operations platform that uses AI to improve task execution, communication, and learning for retail store teams. Yoobic's platform combines task management, digital communications, and microlearning in a mobile-first application designed for frontline retail workers - using AI to prioritise tasks, surface performance insights for store managers, and deliver targeted training based on individual skill gaps identified from operational data. For multi-site retailers where consistent in-store execution is a material driver of sales and customer experience, Yoobic addresses the persistent challenge of ensuring that head office strategies are reliably executed at the shelf and service counter level.

Quorso is an AI-powered store intelligence platform that analyses data from multiple retail systems - sales, inventory, labour, and customer metrics - and surfaces prioritised, actionable recommendations for store managers and area managers. Rather than presenting dashboards of data that require managers to identify and interpret issues themselves, Quorso's AI diagnoses performance gaps, identifies their likely root causes, and recommends specific actions ranked by commercial impact. For retail operations teams managing large store networks, Quorso reduces the analytical burden on store and area managers while improving the quality and consistency of operational decisions across the estate.

Peak AI is a decision intelligence platform with significant retail adoption, helping retailers apply AI to inventory management, customer marketing, and supply chain decisions through a managed AI-as-a-service model. Peak's platform is distinctive in combining proprietary AI models with implementation support and ongoing optimisation - addressing the common retail challenge of having insufficient data science resource to build, deploy, and maintain AI models in production. Peak works with a number of major UK and international retailers and has particular strength in fashion, grocery, and home retail, where it has demonstrated measurable improvements in inventory availability and markdown reduction through AI-driven replenishment and allocation optimisation.

Inteliigence is a specialist AI analytics platform for retail and consumer goods, focused on providing granular, AI-driven insights from retail performance data to inform commercial and operational decisions. Inteliigence aggregates data from multiple retail data sources and applies machine learning to surface patterns and anomalies that are difficult to detect through traditional BI and reporting tools - helping category managers, buyers, and commercial teams identify margin leakage, promotional effectiveness, and range optimisation opportunities. It is positioned for mid-market retailers and consumer goods businesses that need advanced analytics capability without the complexity and cost of a full enterprise AI platform deployment.

Ready to run a structured retail AI vendor selection?

Use the Rapid RFI to assess vendors quickly and build a shortlist, or the Rapid RFP to reach a vendor decision in weeks.

How to Select Retail AI Software

Retail AI selection requires a different evaluation approach to traditional enterprise software, because the value proposition is probabilistic rather than functional - you are not buying a set of features, you are buying an expected improvement in a commercial outcome. This means that vendor selection criteria need to include demonstrated results from comparable retail environments, not just capability demonstrations. Insist on reference customers with similar retail formats, product categories, and data maturity levels to your own organisation, and ask for specific, quantified outcome data - forecast accuracy improvements, margin uplift, waste reduction percentages - rather than generic case study narratives.

Data readiness is the most underestimated success factor in retail AI implementations. Most retail AI platforms require clean, consistent, and sufficiently historical data to train their models effectively - and many retail organisations discover during implementation that their data quality, labelling, or accessibility is not sufficient to support the outcomes the vendor demonstrated in a pre-sales environment. Before shortlisting, conduct an honest internal assessment of your data estate: the completeness of your transactional history, the consistency of your product master data, and the accessibility of data from your ERP, POS, and e-commerce platforms for integration into an AI platform.

For retail AI evaluations, the Rapid RFI provides a structured way to assess vendors against your specific use case and data environment, and narrow to a shortlist of four to five credible options. The Rapid RFP gives you a lean, time-bound selection process that reaches a vendor decision in weeks. For retailers with an urgent commercial requirement - a peak season deadline, a margin recovery programme, or a competitive response - the 30-Day Technology Selection compresses the full process into a single month.

For broader guidance on technology selection methodology, the Enterprise Software Selection Playbook 2026 is the definitive reference.

Summary

Retail AI in 2026 is not a single category - it is a family of AI-powered capabilities spanning demand forecasting, inventory optimisation, personalisation, pricing, store execution, and customer analytics, each with its own vendor ecosystem and implementation profile. The platforms covered in this guide range from broad supply chain AI suites - Blue Yonder, o9 Solutions, and Relex Solutions - through specialist merchandising AI for fashion in Nextail, to customer experience leaders in Dynamic Yield and Algolia, to store operations intelligence in Focal Systems, Yoobic, and Quorso, to outcome-focused managed AI in Peak AI. The right starting point is always the use case: identify the commercial problem you are trying to solve, the data you have available to support it, and the organisational capability to operationalise AI-driven decisions before choosing a platform.

Three takeaways for retail AI buyers in 2026. First, prove value in a defined use case before expanding - the retailers that have realised the most value from AI have started with a specific, measurable commercial problem and demonstrated ROI before scaling. Second, data quality is not a pre-condition you can skip - if your data estate is not ready, invest in fixing it before buying an AI platform, because a sophisticated model on poor data produces confidently wrong recommendations. Third, vendor longevity and product roadmap matter more in AI than in most software categories - the pace of AI development means the platform you choose today needs a credible capability trajectory, not just strong current functionality.

How Viewpoint Analysis Can Help

Viewpoint Analysis supports retail technology buyers across every stage of an AI platform evaluation:

• To build a tailored vendor longlist by use case and retail format, use the Longlist Builder.

• To have the most relevant vendors pitch directly to your requirements, the Technology Matchmaker Service manages that process end to end.

• For structured selection, the Rapid RFI and Rapid RFP provide a fast, rigorous route from longlist to decision.

• For urgent timelines, the 30-Day Technology Selection delivers a vendor decision in under a month.

• The Enterprise Software Selection Playbook 2026 is available for buyers who want to go deeper on selection methodology.

• For related reading, visit the AI Technology Selection and Supply Chain Technology pages on the Viewpoint Analysis website.

Request a Call

If you are currently evaluating retail AI software and would like independent guidance on your options, request a call with the Viewpoint Analysis team. Retail AI vendors who would like to be considered for future content, matchmaking opportunities, or buyer introductions are also welcome to get in touch.