The retail industry is undergoing a fundamental transformation. Mounting pressures from rising operational costs, unpredictable consumer demand, and the aggressive rise of digital-first competitors to capture market share have made traditional operational levers insufficient for protecting margins. As 2025 unfolds, retailers are doubling down on two strategic imperatives: driving operational intelligence through autonomous AI systems that self-optimize forecasting, pricing, and fulfilment; and monetizing first-party data through retail media networks (RMNs) to create high-margin, data-driven revenue streams.
According to Avasant’s Retail Digital Services 2025 Market Insights™ report, autonomous AI is becoming the backbone of operational excellence, enabling real-time decision-making and frictionless customer experiences. At the same time, the surge in consumer demand for personalized, instant interactions is accelerating the adoption of RMNs, helping retailers leverage proprietary data for precision-targeted advertising that maximizes ROI and deepens shopper engagement.
The National Retail Federation (NRF) has declared 2025 as the “Year of the AI Agent,” reflecting how autonomous systems are moving from pilots to production, automating critical decisions across forecasting, pricing, and fulfillment. At the same time, RMNs have emerged as one of the fastest-growing digital commerce levers, monetizing their first-party data to drive both retail and media revenues. According to Avasant’s IT Spending and Staffing Benchmarks 2024/2025: Chapter 8: Retail report, the IT investments in the retail sector account for 2.9% of total revenue, and NRF projects that US retail sales will grow between 2.7% and 3.7% over 2024, reaching $5.42–$5.48 trillion in 2025.
Autonomous AI: Building Self-Optimizing Retail Operations
Retailers are deploying agentic and autonomous AI to transition from static, rule-based workflows to intelligent platforms that sense, learn, and act autonomously. These AI agents optimize end-to-end processes such as demand forecasting, dynamic pricing, workforce allocation, and supply chain resilience, delivering both cost efficiency and agility. For example, AI-driven forecasting models continuously adapt inventory strategies in response to real-time data such as weather shifts, local events, and search trends, reducing both stockouts and surpluses. Similarly, autonomous pricing engines dynamically adjust prices to maintain competitive positioning while protecting margins in volatile markets.
On the store floor, AI-powered shelf scanners, real-time footfall analytics, generative AI-infused virtual try-ons, and intelligent planograms ensure that inventory and visual merchandising dynamically respond to shopper behavior. Store associates are equipped with task automation apps to align workforce scheduling with peak traffic or promotional events. In the back office, generative AI is being integrated into CRM, ERP, and marketing systems, enabling hyper-personalized campaigns, dynamic content generation, and automated customer engagement. This shift from rule-based automation to autonomous AI platforms is turning retail operations into adaptive, self-optimizing ecosystems, delivering operational savings while elevating customer experience.
The pace of transformation is also being shaped by demographic shifts and digital-native consumer behaviors. According to Avasant’s IT Spending Trends in Retail 2024 report, retail transformation is accelerating under the influence of Gen Z’s digital-first mindset and the looming Great Wealth Transfer. This generation demands seamless, tech-driven experiences, from touchless checkout and interactive social media engagement to fully integrated omnichannel journeys. Simultaneously, as Baby Boomers, who currently hold 57% of US wealth, begin transferring an estimated $70–$90 trillion in assets to younger generations, retailers must prepare for a seismic shift in consumer behavior and spending priorities. This generational handover will require retailers to realign strategies, technologies, and engagement models to meet the expectations of this new wave of consumers.
Retail Media Networks: Turning Data into a Growth Engine
In parallel, monetizing first-party data has become a top priority for retailers. RMNs are emerging as powerful advertising ecosystems that leverage proprietary data, purchase histories, loyalty behaviors, in-store interactions, and app usage patterns to deliver hyper-targeted campaigns with full privacy compliance. AI-powered audience segmentation, predictive analytics, and campaign optimization engines enable real-time performance tracking and precise ROI measurement, helping both retailers and brand partners improve engagement and conversion.
Generative AI and machine learning play a pivotal role in scaling RMNs. Dynamic creative generation, context-aware targeting, and automated campaign optimization are helping retailers increase click-through rates (CTR) and the return on ad spend (ROAS). Attribution engines map media exposure to in-store and online sales, providing closed-loop analytics that further increase brand investments. RMNs are not just new monetization models; they are strategic platforms that allow retailers to position themselves as media powerhouses, own the customer relationship, and grow non-traditional revenue with optimized inventory costs.
The Road Ahead: Intelligent, Monetized Retail Ecosystems
The convergence of autonomous AI and RMNs is reshaping the retail value chain from both operational and revenue perspectives. Retailers that embrace this dual transformation are building intelligent retail engines, where autonomous AI ensures operational agility, and RMNs unlock scalable, high-margin advertising revenue. Together, they enable precision retailing where every decision, from supply chain adjustments to personalized offers, is informed by real-time data and predictive intelligence.
According to our Applied AI Services 2024–2025 Market Insights™ report, AI adoption surged 25% in CY 2024, driven by the rapid transition from experimental pilots to enterprise-scale production deployments. While 52% of enterprises are leveraging Gen AI to optimize workflows and boost productivity, only 11% are focused on direct monetization strategies. Furthermore, 68% of Gen AI projects are now in production, compared to just 30% of agentic AI initiatives that have progressed beyond pilot stages. This reflects a growing emphasis on autonomous decision-making, enabling systems to create, reason, and act with speed, precision, and cost-efficiency across the value chain.
Looking ahead, agentic AI adoption is set to accelerate, moving from 30% pilot-stage projects today to enterprise-wide deployments as retailers recognize its potential to drive speed, accuracy, and cost-efficiency. Simultaneously, RMNs will expand beyond core retail categories, integrating AI-driven content creation and programmatic ad buying, creating retailers-as-media-platforms. The winners will be those that connect operations, data, and customer experiences into a single intelligent ecosystem, futureproofing both margins and market share in a volatile digital-first economy.
By Norkit Lepcha, Lead Analyst, Avasant, and Sahaj Kumar, Associate Research Director, Avasant