Retailers are increasingly exploring agentic artificial intelligence (AI) systems as they seek to address the growing disparity between consumer expectations for instant, personalised digital commerce and the realities of legacy operational infrastructures.
Modern shoppers expect digital interactions that are intuitive and seamless, including voice search, tailored product recommendations, and next-day delivery, with AI-powered tools guiding their decisions. In contrast, many retail operations remain constrained by outdated systems and laborious manual processes. Content teams contend with legacy content management systems (CMS), eCommerce managers rely on spreadsheets for search engine optimisation (SEO) tasks, and brand and legal teams manually update numerous product listings to comply with changing marketplace requirements.
This operational misalignment leads to inefficiencies and missed opportunities. The press release notes, "The expectations are real-time. The systems? Anything but. This growing gap between what shoppers expect and what brands can deliver is no longer just a tech challenge. It's a strategic fault line."
Replatforming for speed
The case for replatforming is emphasised by the rapid evolution of the eCommerce environment. Traditional retail IT systems were designed for static catalogue schedules, not the dynamic, marketplace-driven cycles of today. They are ill-equipped to meet demands such as weekly SEO keyword shifts, AI-driven shopping journeys, and the constant need for product data synchronisation across fragmented platforms.
Describing the urgency, the press release states, "Replatforming is no longer a 'someday' project — it's the foundation for staying in the game. But this isn't just about tech modernisation. It's about enabling smarter operations, faster content cycles, and agent-led decision-making at scale."
Agentic AI systems are distinct in that they take proactive action within retail operations. Rather than waiting for human prompts, they identify tasks, determine optimal execution strategies, and automate content updates, SEO optimisation, and compliance checks. This autonomy is at the heart of a shift towards integrated, intelligent, and responsive commerce platforms.
"Unlike traditional AI, agentic systems don't just support tasks — they act. They operate as intelligent agents within the business: identifying what needs doing, deciding how best to do it, and taking action — all without waiting on human prompts."
Operational transformation
Agentic AI systems enable automation that extends beyond simple task acceleration. They facilitate changes in workflows, such as dynamically updating thousands of product detail pages (PDPs) for SEO or compliance requirements, personalising content for individual users in real time, and integrating seamlessly with customer service channels.
"Where traditional AI follows preset rules, Agentic AI systems operate with initiative. These aren't passive engines waiting on prompts. They act. They prioritise. They optimize. They trigger updates when KPIs dip, when SEO shifts, or when retail rules change — without anyone needing to chase them," the release states.
Use cases outlined in the release include enhancing search and discovery through natural language understanding, automated content generation across various marketplaces, predictive personalisation of recommendations, branded chat experiences for customer service, and proactive compliance monitoring and content quality assurance.
Limitations of legacy platforms
The effectiveness of agentic AI is, however, often hampered by the inflexibility of legacy retail platforms. These platforms typically lack modularity and real-time capabilities, leading to operational bottlenecks and incomplete automation. As highlighted, "Agentic AI is only as powerful as the systems it can plug into. And that's where many retailers hit a wall."
The release gives examples of AI tools being constrained by manual upload requirements or rigid approval workflows that prevent output from being deployed efficiently. A modern composable architecture, built with API-first integrations and real-time feedback loops, is presented as a solution to these challenges.
Modern retail stack
Looking ahead, the future-ready retail architecture features a flexible CMS and Product Information Management (PIM) system, real-time data infrastructure, an orchestration layer such as Genrise.ai to coordinate content and compliance tasks, and human-in-the-loop quality assurance.
The phased approach to replatforming that focuses first on high-impact use cases, integrates agentic systems in support of existing teams, prioritises agile modularity over a singular overhaul, and seeks partnerships with retail expertise is recommended. As stated, "Tech alone won't get you there. Work with partners who understand the mechanics of retail — from legal claim review to digital shelf real estate to PDP formatting across Amazon and Walmart."
Agentic AI, when effectively implemented within a flexible operational infrastructure, serves to enhance team outputs, increase speed to market, and ensure ongoing adaptability in a fast-changing retail environment. The release concludes, "Replatforming is the strategy. Agentic AI is the engine. The brands that combine both — intelligence and infrastructure — won't just keep up. They'll lead the next wave of eCommerce growth, with content that's smarter, faster, and deeply in sync with what today's shopper actually wants."
