According to Fortune, a Summer 2025 IBM Institute for Business Value survey of 100 global retail and consumer products executives reveals that 80% of companies have clear strategies to integrate AI into their long-term innovation roadmaps. The study shows that 35% of total AI spending is expected to occur outside IT budgets by 2027, with business domain leaders taking direct control of AI investments. Two-thirds of executives rank continuously improving customer service as their top AI driver, while 58% believe AI solutions will improve customer satisfaction and retention, contributing to an average 31% improvement in these areas over the past year. The research indicates that 76% of executives are already transforming business models to leverage AI for both operational efficiency and new revenue streams, with 84% believing AI will significantly enhance their ability to respond to market trends and disruptions. This comprehensive shift toward autonomous AI systems represents a fundamental transformation in how retail operates.
Sponsored content — provided for informational and promotional purposes.
The Technical Foundation of Agentic Retail AI
The transition from recommendation engines to agentic AI represents a fundamental architectural shift in retail technology. Traditional AI systems operate as passive advisors—analyzing data patterns and suggesting actions for human operators to execute. Agentic AI, by contrast, builds upon foundational models that can autonomously coordinate complex workflows across inventory management, pricing systems, customer service platforms, and supply chain operations. These systems require sophisticated orchestration layers that can manage multi-step processes while maintaining context across different retail domains. The technical challenge lies in creating AI agents that can handle exceptions gracefully, escalate appropriately when confidence thresholds aren’t met, and maintain consistency across customer touchpoints without human intervention.
The Data Infrastructure Imperative
Retailers face significant technical hurdles in operationalizing the study’s findings about leveraging proprietary data. Most retail organizations operate with fragmented data architectures—point-of-sale systems disconnected from e-commerce platforms, customer relationship management databases siloed from inventory management systems, and loyalty program data isolated from supply chain visibility tools. Building effective agentic AI requires creating unified data fabrics that can provide real-time access to structured and unstructured data across the organization. This necessitates substantial investment in data governance, quality management, and integration pipelines. The technical complexity increases exponentially when dealing with real-time personalization that must reconcile customer preferences, loyalty status, live inventory availability, and dynamic pricing algorithms simultaneously.
Implementation Challenges and Technical Debt
The rapid adoption timeline suggested by the study—with 76% of executives already transforming business models—creates substantial technical risk. Many retailers are attempting to layer advanced AI capabilities onto legacy systems never designed for real-time, autonomous operation. The integration of agentic AI with existing enterprise resource planning systems, warehouse management platforms, and customer engagement tools requires sophisticated API gateways and middleware solutions. Technical debt from decades of incremental system upgrades becomes a critical constraint, as brittle interfaces and outdated data models struggle to support the fluid, cross-functional workflows that agentic AI promises. Organizations must balance the urgency of AI adoption with the practical realities of their existing technical landscapes.
Security and Compliance in Autonomous Systems
As AI systems transition from advisory roles to autonomous action, security considerations become exponentially more complex. Agentic AI systems that can execute pricing changes, process returns, or manage inventory levels without human approval create new attack surfaces and compliance risks. Technical teams must implement robust guardrails, audit trails, and rollback mechanisms to prevent unintended consequences from AI decisions. The regulatory landscape for autonomous retail systems remains unclear, particularly around data privacy, algorithmic fairness, and consumer protection. Retailers building these systems must architect for explainability and accountability from the ground up, ensuring that every AI-driven action can be traced, justified, and if necessary, reversed.
The Future Technical Landscape
Looking beyond the immediate implementation challenges, the study’s findings point toward a fundamentally different technical architecture for retail. The traditional model of separate systems for e-commerce, in-store operations, supply chain, and customer service will give way to integrated AI-native platforms where agentic systems coordinate across all functions. This evolution will require new development paradigms, with AI systems capable of learning from cross-domain patterns and optimizing for enterprise-wide outcomes rather than departmental metrics. The technical teams that succeed will be those that can architect for continuous learning and adaptation, building systems that become more effective as they process more data and encounter more scenarios across the retail value chain.
