By Richard Gilling, Head of Technology, and

Zhivko Rusev, Chief Technology Strategist

Most retailers are already using basic forms of artificial intelligence, yet few have developed a roadmap to nurture its ongoing, practical potential and develop true AI automation into their business workflows.

That’s delaying a lot of value and efficiency.

Working closely with large brands, Tryzens regularly takes the temperature of retailers.

Through these conversations on the state of the industry, we know that AI is top of mind. An overwhelming majority saying that AI was an area where they wanted to learn more – there were strong signals that retailers are eager to cut through the noise and understand how it can be applied practically.

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Gaining
While AI remains the number 1 topic retailers want to know more about, it increased from being an area that 29% wanted to understand to 94%. Most concerns were around practical application of AI in digital commerce.

Why reducing manual tasks matter 

For many retailers, the journey to growth is clogged by unnecessary manual effort. These inefficiencies translate directly into: 

• Higher operational costs, with hours of admin that could be automated.

• Slower CX improvements, with fewer resources left for strategic initiatives. 

• Greater error risk, with more touchpoints meaning more chances for mistakes. 

• Team burnout, as staff stuck in lower-value work are less engaged.

Even retailers with advanced digital commerce platforms can still find themselves bogged down in the mud of manual tasks.

For example, think about product data entry and enrichment: adding SKUs, updating specs, and tagging imagery all burn up time. So does inventory and order management, with stock checks and backorder handling, and customer service queries of frequently asked questions.

These tasks are necessary. But they don’t have to be manual.

How AI powers ecommerce automation 

Here’s where AI in retail makes a real difference and clear impact:

Automated product categorization & tagging

AI scans new SKUs, applies consistent metadata, and enriches product descriptions to boost search relevance. 

According to one supply chain intelligence platform, using AI for SKU optimization can increase sales by up to 2-4% while reducing inventory costs by almost 36%.

At Tryzens, we’ve developed our own AI agent for product data management. It helps clients reduce the manual effort involved in generating product attributes and descriptions. We’re now focusing on expanding its use into other areas, such as product content generation.

Predictive inventory management

Machine learning forecasts demand patterns, updates allocations automatically, and reduces overstock or stockouts. 

According to McKinsey, AI is helping retailers to reduce inventory levels by 20-30% and logistics costs by between 5 and 20%. 

Intelligent chatbots & virtual assistants

Natural language processing (NLP) enables chatbots to handle routine inquiries, freeing agents for complex or high-value conversations. 

For example, AI assistants powered by LLMs from OpenAI, Anthropic, Microsoft, Google and Amazon, handle two-thirds of customer service chats in some cases. AI solutions are blending automation with human intervention, and this is being observed across a wider variety of industries. 

Dynamic content generation

AI tools can draft and adapt content for product pages, emails, and ads, with humans prompting, editing, and refining for brand voice and compliance. 

In testing in the UK, Ebay’s AI-assisted listing reduced the number of steps in the listing workflow by half, streamlining the process and leading to faster list times. 

AI interoperability and emerging protocols

Tryzens is increasingly seeing AI tooling and copilots embedded in the administration centers of content management and ecommerce platforms like Contentful, Contentstack, Salesforce, and Shopify. 

AI standards like Model Context Protocol (MCP) are driving a new level of interoperability. Tryzens predicts that connecting copilots and agentic applications with a retailer’s systems will soon be common and straightforward. 

MCP interoperability enables copilots to utilize a retailer’s information system, helping ground AI responses with real-time, organizational data, which significantly improves the relevance, recency, and accuracy of AI agent responses. 

The use cases for MCP are endless and we predict it may eventually become the default way to interact with applications as more retailers adopt enterprise-wide AI tools like Claude.AI, ChatGPT Enterprise, and Microsoft Copilot. 

Supported by Cloudflare, Tryzens has developed a framework for secure MCP server development and hosting that can be tailored to any retailer environment.  

We offer prebuilt MCP servers for NewStore and Salesforce Commerce Cloud. Our MCP framework can integrate any retail systems with API connectivity into copilot or agentic networks. 

Virtual employees and agentic teams

AI is enabling retailers to build teams of virtual employees, which are preconfigured AI agents that automate and accomplish routine, organizational tasks.  

Platforms that create teams of AI agents are gaining traction: from large enterprises like Salesforce, OpenAI, and Microsoft to lean startups like Glean, CrewAI, and LangGraph. They empower retailers to streamline operations, cut low-value admin work, and free employees to focus on high-value tasks. 

Micro-applications

Advances in prototyping tools like Lovable.dev and Replit are helping retailers to quickly test applications and ideas with minimal development experience.  

Before AI, many applications would require complicated software procurement or teams of developers. Now it’s possible to build at a fraction of the cost, prototyped quickly, and tailored to specific business needs. 

AI integration workflows

Platforms like N8N, Microsoft Power Platform, and Zapier are adding AI capabilities that enable retailers to build “situational applications.” These connect enterprise applications into workflows that can be developed in low-code or no-code environments by non-technical or semi-technical staff.

In some cases, the applications can be developed using plain language prompts. The days of requesting a backlogged IT department to develop simple automation may soon be behind us. 

Automated insights & reporting

Instead of spending hours on spreadsheets, AI generates actionable dashboards with anomaly detection and trend forecasting. 

Retailers using AI-driven analytics platforms or solutions, like Tableau from Salesforce, Shopify Sidekick, or Adobe Commerce Intelligence, are cutting down on monthly reporting cycles, while improving the speed and accuracy of decision-making.

Best practices for implementing AI in retail 

Strengthen your data foundations

Effective AI in retail relies on robust data management. Without clean, well-structured, and accessible data, even the most advanced AI models will produce limited value. 

A customer data platform (CDP) is often the missing piece, consolidating customer information from multiple sources into a single, unified view.  

This foundation powers AI-driven insights and truly personalized experiences.

Focus first on high-impact cases 

Don’t deploy AI everywhere at once. Start with clearly defined problems where automation or predictive intelligence will have the biggest operational and financial return. 

AI-powered analytics can highlight exactly where the friction is: whether that’s slow product onboarding, inconsistent pricing, or inefficient campaign targeting. This targeted approach avoids wasted investment and delivers measurable wins that build momentum for wider adoption.

Nurture internal champions

AI adoption succeeds faster when there are advocates inside the organization pushing it forward. These internal champions can demonstrate value and share quick wins as well as support colleagues in learning new workflows. 

For example, a merchandising manager showing how AI tagging cuts hours of admin, or a customer service lead proving the efficiency of an AI-assisted chatbot. Peer-led examples often create more buy-in than top-down directives.

Invest in continuous training

AI tools and capabilities are evolving rapidly. Ongoing learning ensures your teams can get maximum value from new tools and features. 

This could include short learning modules baked into yearly targets, certification programs, and launch and learn sessions. Companies that commit to regular upskilling see a 17% increase in productivity and 21% boost in profitability, according to a Harvard Business School study.  

Fortunately, there is a strong appetite for AI-special training, with 74% of professionals willing to upskill or reskill to adopt AI.

Embed AI into daily work

To realize long-term value, AI needs to be embedded into how people work every day, not just a one-off project. This means integrating it directly into the processes your teams already use. 

For example, merchandising teams can use AI auto-tagging inside their existing PIM system. Customer service agents can get suggested responses in their live chats. 

While the workforce is generally ready to upskill and learn how to incorporate AI into daily practices, only 13% have received training or support. 

This disconnect enforces the need for retailers to embrace their teams’ eagerness to adapt and invest in a broad AI learning framework and, importantly, communicate it effectively and widely offered. 

This will help to bridge the gap in not only alleviating concerns about AI’s impact on future employment but to empower them to use AI in daily work to enhance productivity and innovation.

For new possibilities in global branded commerce, Look Up. 

Tryzens is helping brands turn AI ambition into everyday efficiency. If you’re ready to cut the manual drag and start delivering more value, connect with us.