Strategic technology trends for 2026 in retail

With the sudden acceleration of digital transformation, 2026 will be an extremely important year for the retail industry. Gartner’s ‘Top 10 Strategic Technology Trends for 2026’ report presents key technology trends that will enable retailers to build resilience to external factors, optimize margins and create unique shopping experiences in the new market reality.

Foundations for next-generation commerce

The foundation of any modern retail business today is its digital architecture. It is becoming crucial to move away from traditional software development models in favour of AI-native platforms. Secure, scalable and adaptive systems are essential to fully leverage the potential of AI and machine learning solutions.

Traditional, multi-person engineering teams are giving way to so-called ‘tiny teams,’ which, thanks to AI-native platforms, are able to deliver solutions faster and at a significantly lower cost. Gartner predicts that by 2030, as many as 80% of companies will have ‘tiny teams’ within their structures, which will drastically reduce the time it takes to bring products to market.

AI-native platforms enable ‘vibe coding’ programming, which does not require deep technical knowledge, but rather the ability to orchestrate AI agents. This allows a retail chain, for example, to respond very quickly to new legal requirements, such as the obligation to place country flags on fresh product labels, by modifying electronic shelf labels (ESL).

– In 2026, the competitive advantage will go to those organisations that can transform dispersed data into fuel for agent systems. Our experience in implementing AI solutions shows that the key to success is not only the technology itself, but also its deep integration with business processes. The future of retail lies in the synergy between artificial intelligence and human creativity, which will enable the creation of truly unique shopping experiences – comments Anna Schabikowska, Marketing Director at Exorigo-Upos.

Supercomputing AI in the service of predictive analytics

The growing complexity of AI models used in commerce, from advanced demand forecasting to hyper-personalisation, requires computing power that exceeds the capabilities of traditional infrastructure. AI supercomputing platforms are becoming essential for training models that operate on billions of parameters, taking into account, for example, weather data, social media trends and dynamic changes in supply chains.

Investing in hybrid computing architectures is becoming a strategic imperative for retail industry leaders. Among other things, it helps avoid out-of-stock situations, which can potentially generate losses of up to several per cent of total sales. Supercomputing enables the simulation of millions of shopping paths in real time, allowing bottlenecks in logistics processes to be identified before they become a real problem.

Confidential Computing: the foundation of trust in the cloud solutions

Data protection during processing is becoming increasingly important. Confidential computing, using trusted execution environments (TEEs), protects sensitive information, including from cloud providers. It is estimated that by 2029, 75% of processes in untrusted infrastructure will be secured with this technology.

In the retail industry, this is particularly important in the context of loyalty schemes and personalised offers. Customers are more willing to share their preferences if they are confident that their shopping profile is secure and will not be used in an unauthorised manner. Confidential computing enables secure cooperation between retail chains and FMCG manufacturers in the sharing of analytical data without the risk of trade secrets being leaked.

Will agentic technologies define a new era of commerce?

Yes, in 2026, retail will enter the phase of agentic commerce – intelligent autonomous systems will take on the role not only of advisors, but also of active participants in purchasing and operational processes. There will be a shift from reactive algorithms to proactive AI agents that can plan, negotiate and act on behalf of the user or company.

Multi-agent systems in supply chain optimisation

Multi-agent systems (MAS) are networks of specialised AI agents that work together to perform complex tasks. In commerce, this means a transformation from linear operations to adaptive, self-organising ecosystems. For example, when disruptions such as port closures or sudden weather changes occur, the MAS system can autonomously reorganise logistics. Specialised AI agents include, among others:

• Demand agent: analyses market data and predicts growth in interest in a given product category.
• Warehouse agent: monitors inventory levels in real time and initiates orders to suppliers.
• Logistics agent: negotiates routes and deadlines with carriers, selecting the most cost- and time-effective options.
• Financial agent: settles transactions and monitors margins, ensuring the profitability of operations.

Interest in MAS systems is growing rapidly, which should demonstrate the enormous potential of this technology in solving problems – much greater than that offered by monolithic AI systems.

Domain-specific language models (DSLM) and precise customer service

Traditional language models (LLM) can fail in specific industry contexts. The year 2026 brings the development of domain-specific language models (DSLM), trained on unique retail sector data sets. They eliminate hallucinations and provide answers with a much higher level of accuracy.

In retail, DSLMs can be used, for example, to create virtual advisors who know the full product range, complaint history and unique language of benefits of a given brand. According to the report ‘Top 10 Strategic Technology Trends for 2026’, by 2028, 60% of generative AI models in enterprises will be domain-specific.

The use of DSLM should also reduce service costs while increasing customer satisfaction, as customers receive accurate and substantive support in the purchasing process.

Physical AI: Artificial intelligence enters shops and warehouses

Physical AI is the integration of artificial intelligence with physical devices. In 2026, robotics will no longer be the domain of large distribution centres alone – it will also enter points of sale. Examples such as Adam, a robot that serves drinks and adapts its recommendations to the time of day or weather, show how Physical AI can improve the quality of service in a shop. By 2028, five of the ten largest AI providers will offer Physical AI products. For the retail industry, this means the introduction of, for example:

• Almost complete robotisation of warehouses – by 2028, 80% of warehouses will use automation.
• Intelligent shortage detection systems – AI cameras and sensors monitoring shelves and informing staff when goods need to be replenished.
• Autonomous inventory drones – speeding up the process of counting goods without interrupting store operations.

Vanguard – protecting value and building trust in the age of AI

In a world dominated by algorithms, trust is becoming an extremely valuable currency. Trends from the Vanguard group focus on preventive defence, transparency and technology risk management.

What is preventive cybersecurity?

Preventive cybersecurity (PCS) uses techniques such as advanced cyber deception and automated defence to neutralise attacks before they occur. By 2030, half of all spending on security software will be on preventive solutions.

For retail chains that process millions of transactions every day, downtime caused by cyber attacks is catastrophic. PCS allows sales to continue even in the face of massive ransomware attacks, protecting not only finances but also brand reputation.

Digital provenance and product transparency

Today’s consumers want to know where their products come from and whether sustainability claims are true. Digital provenance uses digital certificates and attestation databases to verify the authenticity of every item in the supply chain. The introduction of the Digital Product Passport (DPP) in the European Union (in stages from 2026 to 2027) makes transparency a legal requirement. Thanks to digital provenance, companies can, for example:

• Ensure the authenticity of luxury goods, eliminating the risk of selling counterfeits.
• Conduct surgical product recalls: Instead of removing an entire batch of goods, a company can identify exactly the 50 packages that come from a contaminated source.
• Build market advantage – verified product origin information helps consumers make purchasing decisions.

AI security platforms (AISPs) and internal risk management

The implementation of AI brings with it new threats, such as data leaks through queries to generative artificial intelligence tools (prompt injection) or uncontrolled actions by agents. 80% of unauthorised AI transactions will result from internal policy violations rather than external attacks. AISPs consolidate control over all AI tools in an organisation and enable secure scaling of innovation without exposing the company to legal and operational risks.

The full Gartner report, ‘Top 10 Strategic Technology Trends for 2026,’ can be downloaded here.

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