Centric Software is previewing Centric AI, a new platform designed to bring generative and agentic AI into retail product lifecycle decisions, at NRF 2026: Retail’s Big Show Europe in Paris. The platform connects product data, workflows and AI agents across planning, design, sourcing, pricing and commercialization as retailers move AI from experimentation toward operational use.
Retailers have spent the past several years testing generative AI for customer service, marketing and content creation. The next battleground is moving further upstream: the complex decisions that determine what products get designed, sourced, priced, launched and ultimately sold.
Centric Software is targeting that layer with Centric AI, a new AI platform for product lifecycle decisions that the company is showcasing at NRF 2026: Retail’s Big Show Europe in Paris.
The platform brings together several AI capabilities, including a conversational assistant, an AI connectivity layer based on the Model Context Protocol (MCP), specialized AI agents and applications for specific product-development tasks.
Centric’s broader proposition is that AI becomes more useful when it has access to the context surrounding a retail decision. Product specifications, bills of materials, supplier information, inventory, pricing, market intelligence, compliance requirements and commercial objectives all influence whether a product should move forward.
Generic AI assistants typically lack that context.
Centric is attempting to build it into the platform itself.
The company says Centric AI combines more than two decades of product data, industry workflows and domain expertise with generative AI and agentic workflows. It is designed to operate across Centric’s existing product lifecycle management, planning, pricing, market intelligence and product experience management products.
That puts the announcement within a broader shift in enterprise AI applications: businesses are moving from standalone copilots toward AI systems embedded directly in operational workflows.
The National Retail Federation identified AI as one of the defining forces shaping retail in 2026, noting that retailers are moving toward implementation while confronting questions around data quality, governance, security and return on investment. Its research found that 86% of surveyed retailers already had AI governance policies, while 39% expected AI to account for more than 10% of their technology spending within three years.
Centric’s timing therefore reflects a wider market transition.
Its AI Companion is designed to let employees retrieve product and commercial information and initiate actions using natural-language commands. Meanwhile, Centric MCP is intended to allow external AI systems to interact with Centric data while maintaining permissions and business context.
The more ambitious feature is the company’s AI Agents layer.
Centric says agents can execute complete tasks rather than simply provide recommendations. Its example is a BOM & Tech Pack Agent that can take a rough sketch or supplier document and generate a multi-line bill of materials, including fabric, trim and packaging information, while matching the closest existing style.
If that workflow performs reliably in production, it illustrates an important distinction between generative AI and agentic AI. A conventional AI assistant might summarize a supplier document. An agentic system attempts to interpret the information, make decisions according to predefined constraints and complete the downstream workflow.
That approach is particularly relevant to fashion, footwear, luxury and consumer-product businesses, where product creation involves large numbers of interconnected decisions and frequent changes.
Centric is also positioning Centric AI Apps as purpose-built experiences for higher-value activities such as product ideation, design and visual content generation. The company says these applications are intended to accelerate product development and reduce time to market.
The commercial case is central to the pitch. Centric says customers using its AI alongside its product lifecycle solutions have recovered 90% to 98% of time previously spent on manual activities including data entry, approval routing, bill-of-materials creation and status tracking. It also reports customer-reported gross-margin improvements of 2% to 5% and sell-through gains of up to 5%.
Those figures are company-reported outcomes and should not be treated as independently verified industry benchmarks.
The underlying market opportunity is significant. McKinsey estimates that generative AI could create between $240 billion and $390 billion in annual economic value for retailers, equivalent to a potential 1.2 to 1.9 percentage-point industry margin increase. Its research also found that 90% of surveyed retail executives had begun experimenting with generative AI, but only a small number had successfully implemented it across their organizations.
The gap between experimentation and scaled deployment is important for Centric. Retailers do not necessarily need another general-purpose chatbot. They need AI that can work with existing product information, business rules and operational systems.
That is also where the competitive landscape gets crowded.
Microsoft, Google, Salesforce, Adobe and other enterprise technology vendors are embedding generative AI and increasingly agentic capabilities into business applications. Retail-specific platforms, meanwhile, are adding AI to merchandising, planning, customer experience and supply chain systems.
Centric’s differentiation is therefore its concentration on the product concept-to-commercialization lifecycle.
The strategy resembles the evolution of enterprise software itself. Instead of treating AI as a separate destination, vendors are increasingly placing it inside the systems where employees already make decisions.
NRF’s 2026 research describes a similar movement toward execution, with retailers focusing on connecting existing technology investments and using AI to produce faster, more reliable decisions.
There is, however, a significant governance challenge. Giving AI agents access to product information and permission to execute actions creates a larger attack and error surface than using AI solely for content generation. NRF has warned that agentic AI requires stronger security and governance because agents can act independently across multiple systems.
Centric says its approach is designed to keep product data, permissions and business context under control when external AI systems connect through its MCP layer.
Whether that architecture can deliver consistent business value will depend on adoption, data quality and the reliability of agentic workflows in real retail environments.
The bigger trend is nevertheless clear. Retail AI is moving beyond generating product descriptions or marketing images. The next generation of systems will increasingly influence the decisions behind the products themselves.
Centric AI is an attempt to position product lifecycle management as one of those AI-native decision environments.
Market Landscape
Retail AI is moving from generative content and experimentation toward agentic workflows, merchandising intelligence and connected product decisions.
McKinsey found that 71% of companies across industries were regularly using generative AI in at least one business function in 2025, while retail-specific research shows many organizations are still struggling to move pilots into scaled deployments.
For Centric, the opportunity is to make AI useful within a specialized enterprise context rather than compete directly with general-purpose AI assistants. Its competitive position will depend on how well its product data, AI agents and existing enterprise applications work together.
The broader retail market is also becoming more agentic. AI is increasingly involved not only in internal merchandising and supply-chain decisions but in how consumers discover, compare and purchase products. NRF reports that AI agents are already beginning to reshape shopping and internal retail operations.
Top Insights
- Centric AI embeds generative and agentic capabilities across the retail product lifecycle rather than positioning AI as a standalone productivity assistant.
- The platform combines product data, workflows and industry context to support decisions spanning design, sourcing, pricing, assortment and commercialization.
- Centric’s reported 90%–98% time savings and margin improvements are company claims, making independent customer validation important to assessing the platform’s impact.
- Retail AI is shifting toward agentic workflows that can execute multi-step tasks, increasing both automation potential and governance requirements.
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