A new era of technology in retail: From isolated deployments to structural transformation

Thanks to the advancement of artificial intelligence, the retail industry is gaining the opportunity to create innovative services and business models. This presents a massive opportunity – as highlighted in Solita’s “Ways of Tech 2026–2027” report, incrementally improving existing processes is no longer enough. Gaining a competitive edge requires stepping outside established patterns and thoroughly rethinking the role of technology within the organization. Retailers face the challenge of redesigning their business model from the ground up – with AI as a key foundation for growth.

Key Insights:

• From pilots to comprehensive transformation: Building a competitive advantage requires breaking out of organizational silos and reorganizing end-to-end business processes.
• Convergence of retail and finance: Customers do not separate buying from paying–whoever optimizes the purchase decision moment within the mobile app (through the integration of financial services and personalization) will win.
• A new division of roles in architecture: ERP systems should focus on reliable transaction processing and accounting, freeing up space for AI agents and dynamic business logic within a modular, flexible environment.
• Data is the foundation: Without structured data models and governance standards, autonomous AI agents generate chaos instead of efficiency.
• A new cost discipline: Scaling generative models requires strict token budget management and measuring the value of individual transactions.

What technology trends will shape retail?

Technology trends for the coming years center around the development of autonomous AI agents, integrating the entire purchase and financing journey into a single app, and building modular systems ready for continuous change. For retailers, this implies a deep transformation: extending from the core IT architecture and business models to the entire consumer experience along the purchasing journey.

The rise of advanced artificial intelligence models means that traditional technological advantage depreciates faster now than it did just a few years ago. As Solita’s “Ways of Tech 2026–2027” report points out, standalone tools or isolated deployments no longer guarantee market dominance.

– Companies should stop treating artificial intelligence as a nice-to-have add-on and start using it to connect previously siloed areas of business. A seamless integration of the mobile app, POS systems, and flexible data architecture is currently one of the best ways to build a sustainable competitive advantage – claims Marek Nowakowski, Head of New Client Acquisition at Exorigo-Upos .

From silos to structural AI

Artificial intelligence generates the greatest value when it optimizes the entire value stream – a complete chain of operations from warehouse replenishment, through sales, to delivery and service – rather than just single, isolated tasks. However, organizational silos remain a hurdle in many retail chains. When individual teams develop AI initiatives independently, the company loses the opportunity to gain a competitive edge.

The key to tangible process optimization across the entire organization lies in the concept of structural AI. This organizational and technological approach redefines the division of labor between humans and autonomous AI agents. Instead of merely assisting individual employees with specific tasks, AI agents assume responsibility for complex, multi-step processes across the entire enterprise.

Implementing an operating model based on value streams significantly transforms business outcomes and workplace dynamics, while providing:

• Deep customer-centricity: The focal point shifts from internal KPIs to delivering real value to the end consumer.
• Process optimization: Bottlenecks, downtime, and redundant tasks become visible and actionable.
• Increased employee engagement: Ownership over the entire process fosters a sense of agency and boosts motivation.

Convergence of retail and financial services in a single value stream

Value stream thinking goes beyond reorganizing internal company processes – it extends to the customer’s entire experience ecosystem. The most prominent example is the blurring of boundaries between retail and financial services. Today’s consumer no longer views purchasing and paying as two distinct stages. They expect full integration and maximum convenience in one place – most often on their mobile app screen.

Technological convergence enables retail chains to boldly deploy their own financial services (such as Buy Now Pay Later, micro-loans, or digital wallets), while banks expand their apps with e-commerce modules. The battle is fought over key decision points along the purchasing journey – the winner is the entity that delivers the most seamless, frictionless experience.

However, this requires more than just surface-level system integration. A modular architecture and real-time advanced data analytics are essential. Thanks to these, financial services cease to be an external add-on and become a native digital component, triggered precisely at the moment the customer makes a purchase decision.

How agentic AI changes the role of ERP systems and IT architecture in retail

Autonomous AI agents require an open environment, clean APIs, and business logic accessible for software integration. A monolithic architecture implemented in traditional ERP systems can become a bottleneck to innovation under these conditions. When business logic remains locked within a monolithic ERP core, any process rule modification demands time-consuming and costly implementation efforts rather than flexible reconfiguration.

This does not mean abandoning ERP systems altogether, but rather deliberately and gradually scaling back their scope: first exposing existing systems to agents, then eliminating direct user interactions with the ERP system, followed by transferring control over company rules to agents, and finally evaluating which transactions still need to be executed within the ERP.

These systems do not disappear; instead, they transition from being the central brain of the organization to serving as a stable transactional backbone. Agentic AI becomes the new command center – assuming control over business logic and orchestrating operations right where value is generated.

How to prevent chaotic AI deployment?

To prevent chaotic AI deployment, businesses must build a solid foundation made of high-quality data. Digital transformation in retail cannot succeed without a coherent and verified source of truth regarding products, inventory levels, and consumer behavior.

However, while humans can intuitively catch gaps or interpret inconsistencies, AI agents operating on flawed data multiply those errors exponentially, leading to large-scale operational chaos.

Therefore, building a data platform optimized for AI agents is becoming a top priority for retailers. Such a flexible, modular architecture must be rooted in mature Data Governance, metadata standardization, and full automation of data integration and flows (DataOps).

The end of low costs: How to maintain profitability in AI projects

Budget control is becoming an equally critical challenge. The era when running generative artificial intelligence meant low expenses is over. The usage-based pricing model means that as solutions scale, inference costs – the operational generation of responses by AI – rise exponentially.

To maintain AI project profitability, companies must institute financial discipline, including:

• Token budgeting: Monitoring AI resource consumption at the level of specific processes and functions.
• Prompt and model optimization: Deploying smaller, specialized local models (open-source) for repetitive tasks instead of expensive general-purpose models.
• Paved paths / Standardized safe routes (Platform Governance): Creating pre-built, secured integration components that development teams can leverage without undergoing repeated, lengthy security audits.

Implementing mature Platform Governance eliminates the risk of uncontrolled spending and shadow IT, while guaranteeing full compliance with regulations such as the EU AI Act and GDPR – which is paramount when processing data for millions of retail consumers.

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