Retail executives have heard the transformation pitch before. AI is the first time the data backs it up. According to a 2026 NVIDIA survey of retail and CPG leaders, 89% of respondents said AI has helped increase revenue, while 54% cited improved employee productivity and 52% pointed to meaningful operational efficiencies. With nine in ten retailers planning to increase their AI budgets this year, the industry has clearly moved past the question of whether to invest in AI and into the far more consequential question of where it actually works — and where the returns justify the spend.
Where AI Is Actually Moving Margin
The honest answer, for most retailers today, is that AI is delivering its clearest and most measurable returns in the backroom, not the showroom. The most impactful applications are not the customer-facing chatbots or personalization engines that tend to dominate the headlines. They are the unglamorous but operationally critical functions: demand forecasting, inventory optimization, and dynamic pricing. AI-powered demand forecasting alone has been shown to reduce forecasting errors by 20–50% and decrease lost sales from product unavailability by as much as 65%. These are not incremental gains. They are the kind of margin-moving improvements that fundamentally change the economics of a retail operation, particularly in an environment where consumers are increasingly value-driven and inventory missteps carry a steep cost.
What makes today’s AI different from prior waves of retail technology is its ability to integrate and act on data across the entire supply chain in near real time. Lowe’s, for example, recently partnered with AI platform RELEX Solutions to move beyond manual, reactive workflows toward a system that continuously senses and responds to demand as conditions change. As Lowe’s SVP of Inventory Replenishment and Planning described it, “the power of AI allows us to be more precise in how we position inventory, improving in-stocks on high-demand items while reducing excess.”
The shift here is structural: rather than treating forecasting, replenishment, and allocation as separate activities managed by separate teams, AI enables them to function as a single, continuously optimizing system. That integration, not any individual capability, is where the greatest value lies.
The Infrastructure Gap
Despite the momentum, a meaningful gap persists between the retailers who are capturing this value and those still running on outdated infrastructure. A striking 73% of supply chain leaders still rely on manual methods — primarily spreadsheets — for supply chain planning. For distressed or underperforming retailers, this is not just an efficiency problem; it is a liquidity problem.
Excess inventory tied up in the wrong locations, chronic stockouts in high-velocity categories, and reactive markdown strategies are among the most common drivers of margin erosion and cash burn. The retailers most at risk are often those carrying the legacy of fragmented systems built up over years of acquisitions or under-investment in technology — where data lives in silos and no single view of inventory or demand exists. AI adoption in these environments is not simply a growth play; it is a survival imperative.
The Sequencing That Matters
For operators and investors navigating retail complexity, the practical takeaway is this: AI implementation is not a single technology decision. It is a sequenced operational transformation. The retailers achieving the highest ROI are those that started with high-value, well-defined use cases like demand forecasting and inventory optimization before expanding into more complex applications. They also invested heavily in data preparation and system integration, with research suggesting the most successful implementations allocated roughly 31% of their AI budgets to data readiness rather than algorithm development alone. The technology is only as good as the data it runs on — a lesson that has humbled more than a few early adopters who moved fast on deployment without first cleaning up the underlying data infrastructure.
The retail operators who will look back on 2026 as a turning point are not necessarily those with the largest AI budgets. They are those who were disciplined enough to identify where AI closes the gap between what their business knows and what it acts on, and then built the organizational capability to sustain it. In a margin-compressed, value-driven market, the backroom has never mattered more. The good news is that the tools to transform it have never been more accessible. The question for every retail operator today is not whether AI belongs in the backroom. It is whether the organization is ready to let it run there.
Footnotes
- 1. NVIDIA, State of AI in Retail and CPG Survey, January 2026.
- 2. Leafio AI / Industry Research, AI Demand Forecasting for Retail, 2025–2026.
- 3. PYMNTS, “Lowe’s Thinks Smarter Shelves Can Win the Pro Customer,” April 2026.
- 4. NetSolutions / Industry Research, AI in Retail Demand Forecasting, January 2026.
- 5. Invent.ai, Using the 2026 Retail Industry Outlook to Build a Stronger AI-Driven Strategy, 2026.
- 6. International Journal of Science and Advanced Technology (IJSAT), AI-Driven Demand Forecasting in Enterprise Retail Systems, 2025.
The Backroom Report: From Consumer & Retail is a recurring series from Areté Partners, bringing operator-level perspective to the trends reshaping retail and consumer businesses. Issue 2 is coming soon.