AI Solutions in Retail 2026–2027: What Will Define the Success of Your Store Network

Why the Next Two Years Will Be a Turning Point for Retail

Retailers are operating in an environment where customer expectations are rising faster than business margins. That leaves little room for hesitation when it comes to technology transformation. Artificial intelligence has become one of the most powerful forces reshaping how the industry engages with customers, designs products, and runs operations. According to IBM Institute for Business Value, the next two years will be especially critical for the sector.

The decisions retail leaders make about AI in 2026 and 2027 will affect more than competitive advantage. They will determine how effectively brands can meet the needs of customers who have already integrated AI into everyday life – using it to make decisions, navigate product choices, and shape preferences about how and where to shop. Consumers are changing faster than many companies can adapt.

The industry needs a clear, practical action strategy. Research data points to areas where AI is already delivering measurable impact – and where retailers should focus to unlock real business value. Companies that act with intent today will set the standards tomorrow. Those who wait risk discovering that the pace has already been set without them.

AI Is No Longer an IT Tool – It’s a Business Strategy

One of IBM’s most significant findings is the shift in where AI investments come from. By 2027, 35% of total AI spending is expected to sit outside IT budgets, up from 28% in 2025. At the same time, AI spending within IT budgets is also growing – from under 10% of annual IT budgets today to 13% by 2027. This signals an important industry shift: AI has become a business decision, not just a technology initiative.

Merchandising teams increasingly fund AI to improve product discovery and optimize shelf execution. Marketing teams deploy AI to personalize content and offers. Supply chain leaders use AI for demand forecasting and exception management. As investment moves beyond traditional silos, it reflects a broader organizational shift toward operationalizing AI across the enterprise.

The research shows that 80% of retail and consumer goods companies now have a long-term AI innovation strategy. This underscores that AI is no longer framed as a side experiment, but as a core growth engine. The 2026–2027 investment window represents an inflection point: AI spend is moving from experimenting with tools to building capabilities required to compete in a market shaped by real-time decisioning, demanding customer expectations, and new forms of digital commerce.

The Data Problem: Retail Has Information, but It’s Not Used Effectively

Retail has always relied on data to understand customers, yet much of that data remains underutilized. According to the research, 64% of companies say their proprietary data is accessible for AI, but only 49% of that data is actually usable. Moreover, only 26% of data is currently used to train AI models. This gap represents a major growth opportunity.

Unlocking proprietary data is one of the most critical components of an AI strategy for 2026–2027, because the performance of high-value AI use cases – whether personalization, inventory optimization, or forecasting – depends on data quality and accessibility. Retail has an advantage: the industry has historically collected rich, high-volume customer and transaction data. As data becomes cleaner, better governed, and more connected, the downstream value of AI rises sharply across everything from customer experience to operations.

Customers are already feeling the impact. 58% of executives report that AI improves retention and satisfaction, and over the past year AI has contributed to an average 31% improvement in these metrics. In a loyalty-driven industry, that’s significant. High-quality data from points of sale, shelves, warehouses, and online channels becomes the fuel for AI systems that can predict shopper behavior and optimize supply chains in real time.

Companies that invest in structuring, cleaning, and integrating data now will gain a competitive edge in the coming years. For example, SmartMerch’s SM Visor automatically captures shelf execution data using computer vision, turning unstructured visual inputs into accurate decision-grade analytics. This enables retailers not just to collect information, but to make it a usable asset for AI models.

From Analytics to Action: Autonomous AI Agents in Retail

AI’s role in retail is shifting from advisory to action-oriented. Research indicates that 84% of executives expect AI to significantly improve their ability to respond quickly to market shocks and changing customer needs. But the most transformative shift is not just speed – it’s AI’s ability to take action within complex workflows.

Agentic and autonomous AI systems already manage multi-step processes: coordinating inventory, personalizing offers, and guiding customers through complex purchase decisions. According to the data, 76% of executives are transforming their business models to use AI not only to drive efficiency, but to create new revenue streams. This marks a move from passive analytics to active delegation of tasks to AI.

This action-driven shift is becoming a defining characteristic of retail transformation over the next two years. Organizations that build the foundation for agentic AI in 2026 will be better positioned to improve customer experience and capture new value by 2027. AI agents will be able to make decisions within defined parameters – from automated replenishment to dynamic pricing based on demand and competition.

A compelling example is Al Futtaim Group’s Blue platform, where an AI engine integrates loyalty, payments, and purchases into a single customer journey. Shoppers can instantly earn and redeem points across categories, browse products frictionlessly, and complete transactions in one flow. This demonstrates the impact of AI orchestration combined with human oversight – technology coordinates, while people add context and strategy.

AI + Humans = The New Standard for Retail Customer Experience

The research clearly shows that AI augments rather than replaces human roles. AI agents increasingly take on repetitive, multi-step processes across the value chain – connecting inventory, payments, logistics, and customer data – while employees step in where judgment, empathy, and brand trust are required.

This model frees frontline assistants and service teams to focus on higher-value interactions and complex customer needs. Executives report that AI delivers the most value in marketing, customer service, supply chain operations, and digital commerce – areas where operational precision intersects with human connection. The result is a hybrid operating model where AI manages complexity and humans strengthen what makes retail uniquely human.

For example, SmartMerch AI chatbots for field teams automate routine merchandiser tasks – planogram checks, product information retrieval, and report submission. This enables agents to concentrate on real work in stores: high-quality shelf execution, building relationships with store staff, and handling exceptions. Technology removes administrative load; humans bring expertise and flexibility.

This approach is especially relevant for FMCG, where operational speed is high but execution quality depends on details. AI handles scale and speed; people ensure accuracy and adaptability. Companies adopting this hybrid model now are building a foundation for durable growth in an era where both technology and the human factor are critical for success.

The Ecosystem Approach: Why Isolated Solutions No Longer Work

The research also highlights the growing importance of ecosystems in retail. AI can be truly transformative only when tools, partners, platforms, and agents work together securely and seamlessly. Autonomous AI cannot reach its potential inside fragmented systems. Isolated solutions create barriers to data sharing, slow decision-making, and limit scalability.

To prepare for this shift, retailers need to modernize commerce platforms, rethink processes for non-linear customer journeys, and strengthen data governance. When AI systems can understand and act across the business, retailers can orchestrate customer experience holistically rather than through isolated touchpoints. A shopper who starts in a mobile app, continues in-store, and finishes through customer support should receive consistent, personalized service at every step.

That’s why ecosystem integration sits at the center of the 2026–2027 AI agenda. The future belongs to retailers who can coordinate intelligence across the full value chain, not only within individual functions. This requires not just deploying new technologies, but changing the architecture of the IT landscape – from disconnected systems to unified platforms capable of real-time data exchange.

SmartMerch designs solutions with this principle in mind: its products integrate with existing accounting systems, ERP, CRM, and analytics platforms. For instance, SM Visor shelf data can automatically feed into inventory management, trigger replenishment orders, and update demand forecasts. This approach eliminates manual work, reduces errors, and enables AI models to operate on up-to-date, complete information from all sources.

Three Steps Retailers Should Take Right Now

The analysis points to three areas retail leaders can focus on to accelerate progress in AI transformation. These steps do not require radical change, but they create the foundation for sustainable growth and competitive advantage in the coming years. Each lever is grounded in real capabilities available today and can be implemented incrementally without disrupting core business processes.

1) Deploy hyper-personalization based on secure first-party customer data

Personalization has long been standard in retail, but AI-driven hyper-personalization takes it to the next level. It means adapting every element of the customer journey based on behavioral data, preferences, and real-time context – from product recommendations to communication channels and pricing offers. The key condition is using first-party customer data with strong privacy and security.

Deploy AI in high-impact processes such as order-to-cash and supply chain orchestration. Ensure AI tools reflect your brand truth – even when customers interact through external AI assistants like voice assistants or third-party chatbots. Data must be structured so AI can extract insights and act without losing context. This is especially critical for FMCG, where purchase decisions are fast and loyalty is built through consistent experience.

2) Automate critical operational tasks with AI

Make product information accessible and AI-friendly. Start by automating repetitive operational tasks, then expand AI’s role as trust grows – both with customers and internal teams. Conversational commerce is becoming the standard: shoppers expect to ask questions in natural language and get accurate, relevant answers instantly.

Examples include shelf compliance monitoring, inventory checks, promotion management, and planogram compliance. SmartMerch’s SM Visor uses computer vision for real-time shelf monitoring: it recognizes SKUs, detects out-of-stocks, verifies planogram compliance, and sends alerts to merchandising teams. This removes the need for manual audits, reduces lost sales from empty shelves, and frees employees for strategic work.

Automation also applies to managing field teams: AI chatbots can answer agents’ questions in real time, provide access to knowledge bases, and help complete reports. This is especially valuable for distributed teams across many stores, where coordination and information access are essential for efficiency.

3) Orchestrate collaboration between AI agents and business systems through an ecosystem

Break down silos between departments. Connect business systems so AI agents can coordinate work across functions – from procurement to marketing, from logistics to customer service. Ensure AI recommendations and transactions remain secure, accurate, and aligned with your brand values. This requires not only technical integration but organizational change: teams must be ready to work with AI as a colleague, not just a tool.

AI agent coordination is especially important in complex scenarios like omnichannel promotion management. For example, AI can analyze promotion performance in real time based on POS data, adjust inventory recommendations through WMS integration, and simultaneously optimize content across digital channels based on customer response. Such orchestration is impossible without a unified ecosystem where data flows freely and securely between systems.

SmartMerch builds products with this principle in mind: its solutions integrate easily with ERP, CRM, analytics platforms, and category management systems. This enables retailers to adopt AI gradually – without replacing their entire infrastructure – while gaining the benefits of automation and intelligent process orchestration.

The Gap Between Leaders and Laggards Will Grow Faster Than Ever

In retail, every click, basket, and conversation matters – and AI amplifies the best of what makes the industry human. Over the next two years, the gap will widen between retailers who use AI to fundamentally rethink how they operate and those who treat it as just another layer of technology. The defining difference will come down to clear decisions about data foundations, operating models, and where AI is allowed to act autonomously.

Strengthening these foundations now is shaping the next era of retail productivity. Companies investing in data quality, system integration, and workforce enablement for AI will gain an advantage not only in efficiency, but in their ability to adapt quickly to market change. This is especially relevant for Russia and the CIS, where retail digitalization is accelerating and competition for customers is intensifying each quarter.

The opportunity ahead is significant – and very real. Retailers who act with intent today will help define what “good” looks like tomorrow. Those who wait may find the pace has already been set by competitors who started earlier. Delaying AI transformation now is not merely a missed opportunity – it’s a strategic risk that can cost market share and customer loyalty in the coming years.

How SmartMerch Helps Retailers Prepare for AI Transformation Today

SmartMerch offers a suite of AI solutions designed specifically for retail and FMCG. The company’s products cover the key AI transformation directions highlighted in the IBM research – from automating operations to building ecosystem integration and enabling hyper-personalized customer experience. Importantly, SmartMerch implementations are phased, without needing to halt business operations or fully replace existing IT infrastructure.

SM Visor automates shelf execution management using computer vision and SKU recognition. In real time, it verifies planogram compliance, detects out-of-stocks, monitors promo material quality, and collects analytics-ready data. This eliminates manual audits and turns visual shelf data into structured inputs for AI models, which can be used for demand forecasting and inventory optimization.

SmartMerch AI chatbots give field teams instant access to corporate data in natural language. Merchandisers can ask questions about products, planograms, and promotions and receive precise answers without calling headquarters or searching through documents. This accelerates execution in stores and reduces back-office load, enabling teams to focus on strategic tasks.

SM Camera provides remote monitoring of refrigeration equipment and temperature compliance using AI. The system prevents spoilage, reduces operational risk, and enables centralized control of a distributed equipment fleet. This is especially critical for short shelf-life products and strict storage requirements.

All SmartMerch solutions integrate with existing accounting systems, ERP, CRM, and analytics platforms – creating a unified ecosystem where data circulates freely and AI can coordinate processes across departments. The company offers a step-by-step rollout approach: from a pilot in a few locations to scaling across a national network. This enables retailers to start AI transformation with minimal risk and prove business value before full deployment.