NRF Europe 2026 recap – Part 2 of 2
The Next Trends: From AI Assistance to Autonomous Retail
Part 2 of our two-part recap of NRF Europe 2026. Part 1, “The Next Now: How AI Is Rewiring Retail”, covers the scale of the AI opportunity and the organizational change needed to capture it.
In our previous article, “The Next Now: How AI Is Rewiring Retail”, we explored the scale of the AI opportunity in retail and the organizational transformation required to capture it. We looked at how retailers across Europe are already using AI to improve operational efficiency, connect their businesses, and create more relevant and engaging customer experiences.
But the pace of change is accelerating. For retailers, the question is no longer whether AI will transform the industry, but how to prepare for what comes next and turn emerging technologies into sustainable business value. In this article, we look at the next wave of AI-driven retail trends and translate them into practical priorities and actions for retailers.
The next trends
AI in retail has evolved from a competitive differentiator into a foundational capability. As highlighted in the QBCS blog article “2026 Retail Trends: What Retailers Need to Know to Stay Ahead”, retailers are moving from AI experimentation to execution – embedding AI into everyday operations, decision-making, and customer experiences. So, what comes next, and which emerging AI trends should retailers be preparing for now?
AI applications can be broadly grouped into three subfields: Analytical AI, Generative AI, and Agentic AI. Most retailers are already familiar with or have begun exploring the first two. Agentic AI, however, remains less understood and less widely adopted.
Agentic commerce: the next structural shift
Agentic commerce has the potential to become one of the biggest structural shifts in retail since the emergence of e-commerce. Today, most e-commerce traffic still originates from search engines and other established channels, while AI agents such as ChatGPT and Gemini account for a relatively small share of traffic. However, emerging evidence suggests that traffic originating from AI agents can have significantly higher conversion rates, driven by a greater degree of personalization and intent.
Early indication: Shoptimus AI
Shoptimus AI, a Barcelona-based startup providing AI-powered smart shopping list platforms for grocers, reports that AI agents can increase monthly customer spend by 8%. While such figures should be viewed as an early indication rather than an established industry benchmark, they highlight the potential commercial impact of agentic shopping.
To capture this emerging opportunity, retailers will need to ensure that their products can be easily discovered, understood, compared, and recommended by AI agents. This means providing accurate, structured, and sufficiently detailed product, pricing, availability, fulfillment, and customer-related information in formats that AI agents can readily consume.
As we wrote in “AI Shoppers: A New Wave of Customers”, AI agents may become the new shopping interface, but humans remain the ultimate decision-makers. Retailers therefore need to optimize not only for AI discoverability, but also for the customer preferences, values, and brand relationships that influence what AI agents ultimately recommend and select.
From AI assistants to AI-powered retail roles
Agentic AI is also beginning to reshape how retail corporate functions operate and how human roles evolve. As explored in the QBCS blog article “AI Agents in Retail Roles: A Corporate Transformation”, AI agents are moving beyond simple task automation toward performing entire data-intensive, repetitive, and rule-based activities across key retail functions:
AI agents can support forecasting, assortment, allocation, and replenishment, allowing merchandisers to focus more on strategy, supplier relationships, and commercial decisions.
Agents can optimize campaigns, personalize communications, and continuously adjust activities based on customer behavior and performance.
Agents can automate data consolidation, forecasting, and scenario analysis, enabling finance teams to focus more on strategic planning and decision-making.
This points toward a hybrid workforce, in which AI provides scale, speed, and execution while humans focus on strategy, creativity, judgment, and relationships. Realizing this potential will require more than deploying new technology. Retailers will need to develop new skills, redesign workflows, establish clear accountability, and strengthen governance around AI decisions, data, and security.
Seen at NRF Europe: Kingfisher
This shift was also reflected at NRF Europe, where Kingfisher plc discussed its experimentation with website agents designed to provide an alternative to traditional store-advisor interactions, particularly in situations where customers do not have access to a physical store.
From recommendation to autonomous execution
From an order-management perspective, Agentic AI is similarly evolving through several stages: from providing recommendations, to preparing orders, and ultimately to executing purchases based on predefined customer mandates.
The transition from recommendation to autonomous execution introduces a critical new dimension: trust. For agentic commerce to reach its full potential, both retailers and consumers need confidence that AI agents will act accurately, transparently, securely, and in accordance with the customer’s preferences and authorization.
As AI moves from suggesting what to buy to actually buying on our behalf, trust will become just as important as convenience, personalization, and price.
Action plan: preparing for the next wave
The next phase of AI in retail will not be about adopting as many AI tools as possible. It will be about orchestrating AI, data, technology, and people to create measurable value for both customers and the business.
Retailers should start by identifying the areas where the business faces its greatest pain points or where AI can deliver the most significant benefits. Priorities should be driven by business value, feasibility, data readiness, and the potential to scale – not simply by what is technically possible.
At the same time, technology is an enabler, not the transformation itself. Culture, people, processes, and governance are equally important. Employees should be involved early and given opportunities to shape how AI is introduced into their roles. Clear communication, training, and transparency will be essential to address concerns and uncertainty about how AI may change the way people work.
A practical action plan can be built around three interconnected priorities:
1Build an AI and Agentic AI foundation
Rather than deploying individual AI solutions in isolation, retailers should develop an AI orchestration strategy that determines how different AI capabilities work together and where human decision-making remains essential. Priority applications include:
Prepare product, pricing, inventory, fulfillment, and customer data so AI agents can discover, evaluate, recommend, and ultimately purchase products on behalf of customers.
Deploy AI agents to provide personalized assistance, resolve routine queries, and seamlessly hand over more complex interactions to employees.
Use AI to improve demand forecasting and continuously incorporate new signals such as customer behavior, market trends, promotions, weather, and external events.
Combine customer, product, behavioral, and contextual data to deliver more relevant recommendations, offers, and experiences.
Apply AI and agents across replenishment, inventory management, supply chain, workforce management, pricing, and store operations to improve efficiency and responsiveness.
The objective is not to create a collection of disconnected AI applications. Retailers should establish common data, integration, governance, and AI-agent capabilities so that these applications can share information and work toward common business objectives.
2Reimagine the customer experience
AI should ultimately make the customer journey more connected, relevant, and frictionless across channels.
Retailers should connect technology, data, and AI capabilities to create a consistent view of the customer and enable personalization across the entire journey – from discovery through service, loyalty, and conversion.
For example, an AI agent could help a customer discover a product, understand its suitability, answer questions, recommend complementary products, check availability, and ultimately complete the purchase. The same customer preferences and interactions should inform future engagements rather than being lost between channels.
To achieve this, retailers should:
- Establish a unified customer and product view across channels.
- Connect e-commerce, stores, customer service, loyalty, marketing, and fulfillment data.
- Use AI to personalize interactions based on customer context and intent.
- Ensure that recommendations and offers are consistent across channels.
- Prepare for AI agents to become an increasingly important customer interface.
- Measure the impact of personalization not only through engagement, but also through conversion, retention, customer lifetime value, and profitability.
The goal is to move from channel-based personalization to connected, customer-centric experiences.
3Accelerate data and retail transformation
AI is only as effective as the data, processes, and technology infrastructure supporting it. Retailers therefore need to treat AI adoption as part of a broader data and retail transformation.
The objective should be to create data-driven, connected operations in which information flows seamlessly across merchandising, supply chain, stores, e-commerce, finance, and customer-facing functions. Key areas include:
Establish trusted, accessible, governed data across customers, products, inventory, transactions, suppliers, and operations.
Modernize data platforms and integration capabilities to enable real-time or near-real-time access to the information AI agents require.
Break down organizational and technology silos so AI can act across processes rather than optimizing individual functions in isolation.
Apply AI to forecasting, supply chain optimization, store operations, pricing, assortment, inventory, and margin management.
Establish feedback loops so AI models and agents continuously learn from outcomes and human decisions.
The ultimate goal is to move from data-driven individual functions to a connected, AI-enabled retail operating model.
A practical roadmap
Retailers can translate these priorities into four stages:
The destination: an orchestrated AI retailer
The retailers that benefit most from the next wave of AI will not necessarily be those with the greatest number of AI applications. They will be those that can connect AI capabilities, data, technology, people, and processes into one coherent operating model.
The transformation is therefore not simply “Where can we use AI?”, but rather “How can we orchestrate AI across the enterprise to make better decisions, improve the customer experience, empower our people, and create measurable business value?”
This shift from individual AI use cases to an orchestrated AI-enabled retail enterprise will be a critical step in moving from AI experimentation to sustainable, enterprise-wide execution.
The future of retail is relational
“We won’t experience 100 years of progress in the 21st century – it will be more like 20,000 years of progress at today’s rate.”
Ray Kurzweil, Chief Engineer, Google, and recipient of the US National Medal of Technology and Innovation
The pace of technological progress is accelerating dramatically: what once took years to achieve may soon happen in months, weeks, or even days. For retailers, this means the ability to adapt will become as important as the technology itself.
But progress is not only about how fast technology evolves. It is about how effectively retailers use it to create value. Relational retail puts customers and employees at the heart of the business – using AI to enhance experiences, empower people, strengthen relationships, and make better decisions.
The retailers that thrive in this rapidly changing environment will be those that combine the power and speed of AI with the uniquely human qualities of trust, empathy, creativity, and judgment. The future of retail is not AI replacing relationships – it is AI helping retailers build stronger, more meaningful relationships at scale.