The Operational Excellence Tools Series | #36: Retail Enters The AI Era.

91% Of Companies Redesign Supply Chains And Customer Experience.

Welcome to the unique weekend article for the Loyal Fan subscribers-only edition.

This is the #36 article of The Operational Excellence Tools Series.

Outlines and Key Takeaways

Part 1 – Official Announcement

Part 2 – Background and Meaning

Part 3 – Analysis Through the Lens of Operational Excellence

Part 4 – Lessons for Businesses

Part 5 – Conclusion

PART 1: OFFICIAL INFORMATION

During the period 2024–2026, artificial intelligence (AI) is being recognized as one of the fastest-deployed and most far-reaching technologies in the global retail industry. According to recent industry reports from reputable consulting, technology, and market research organizations, approximately 91% of retail companies worldwide have already invested, are currently investing, or plan to further invest in AI across their core business and operational activities. This figure reflects a systemic trend that is no longer limited to high-tech retailers or market-leading corporations, but has expanded across multiple segments and company sizes.

Industry reports indicate that AI in retail is no longer being deployed primarily as technology-driven pilot projects, but is instead being integrated directly into supply chains, inventory management, demand forecasting, logistics, price optimization, customer experience personalization, and internal operational management. This demonstrates a clear shift of AI from a supporting role to an operational infrastructure role, directly affecting efficiency, cost structures, and the ability of retail companies to respond to market volatility.

In the area of supply chains, industry reports note that AI is being widely used to analyze real-time demand data, forecast consumption trends, optimize ordering plans, and coordinate product flows. In the context of global supply chains experiencing significant disruptions following the pandemic, geopolitical conflicts, logistics cost volatility, and changing consumer behavior, traditional forecasting models based solely on historical data are no longer sufficiently accurate. AI, with its ability to process large volumes of data from diverse sources, is being leveraged by retailers to improve forecast accuracy and reduce delays in supply planning adjustments.

One area emphasized across most reports is intelligent inventory management. AI enables retailers to monitor inventory levels in real time, detect early risks of stockouts or overstock situations, and recommend inventory reallocation decisions across stores, central warehouses, or different sales channels. Industry reports document that the application of AI in inventory management helps retailers reduce dead stock, improve inventory turnover, and limit revenue loss caused by out-of-stock situations, particularly in sectors with large product assortments and short product life cycles.

Beyond supply chains, customer experience is another domain where AI has a strong impact. Official reports show that AI is being used to personalize product recommendations, optimize marketing content, enhance customer service, and support omnichannel sales. AI tools can analyze shopping behavior, transaction history, and customer interactions across multiple channels, enabling more tailored recommendations for individual customers. This not only drives revenue growth, but also improves engagement and customer loyalty in an increasingly competitive retail environment.

A key point emphasized by industry reports is that AI in retail is not solely focused on revenue growth, but also on cost reduction and operational productivity improvement. AI applications in process automation, workforce scheduling optimization, frontline staff management, order processing, and decision support help retailers reduce reliance on manual tasks, minimize errors, and accelerate operational execution. In an environment of rising labor costs and shrinking margins, this is identified as a major driver behind the acceleration of AI investment.

Reports also highlight that AI is increasingly being deployed at real-time operational levels, rather than being limited to post-hoc analysis. This is evident in applications such as dynamic pricing, promotion optimization, regional inventory allocation management, and rapid response to demand fluctuations during peak events. The ability to process continuous data streams and generate near-real-time recommendations enables retailers to enhance responsiveness and reduce the lag between information and action.

From an investment perspective, industry reports indicate that retail companies are increasing spending on data infrastructure, AI platforms, and analytical capabilities, rather than purchasing isolated point solutions. Many organizations are focusing on building unified data platforms that integrate data from physical stores, e-commerce channels, supply chains, and CRM systems to enable effective AI deployment. This reflects a maturing approach to AI adoption, shifting from tool acquisition to rebuilding operational foundations around data.

Another important point emphasized in official reports is that AI in retail is not viewed as a complete replacement for human labor, but as a tool to support and augment workforce capabilities. Retailers prioritize using AI to handle repetitive tasks, complex data analysis, and decision support, while humans focus on management, creativity, exception handling, and high-value customer interactions. This approach reflects a broader industry trend toward rebalancing roles between humans and technology.

Overall, the synthesis of industry reports shows that the fact that 91% of retail companies are adopting or planning to expand AI investments is not a marketing figure, but a clear reflection of reality: AI has become an essential component of the operational and competitive models of the global retail industry. The core of this wave lies not in any single technology, but in how retailers use AI to optimize supply chains, manage inventory, enhance customer experience, and improve overall operational efficiency in an increasingly volatile and cost-constrained business environment.