NRF Innovation Advisory Committee member Liam Buswell went to Paris this week for NRF 2026 Europe with one specific problem in mind. The retail AI decision gap. Not which platform to evaluate, not which vendor to meet. He described the challenge precisely: the quality of the analysis matters less than whether it arrives while anyone can still use it. Teams collect data, run reports, hold meetings, make choices, and implement changes. Each step is defensible. Together they add up to weeks. And weeks are often longer than the opportunity can sustain.
The 2026 Supply Chain Resilience and AI Adoption Study from Incisiv, World Retail Congress, and Anaplan put a number on that problem. Leading retailers average 71 percent full-price sell-through compared to 57 percent for the industry overall. That is a 14-point gap. The study found it is not driven by better products or more favorable markets. It is driven by how quickly those organizations act on demand signals. Ian McGarrigle, Chairman of World Retail Congress, summarized the finding directly: “The competitive divide in retail is no longer between organizations that have invested in AI and those that have not. It is between those that have turned that investment into faster, smarter decisions.”
Most retailers have more data than they can use. Most have more AI than they have operationalized. The gap that is actually costing them margin and sell-through is not the technology gap. It is the time between knowing something and doing something about it. And that gap is measurable, closable, and almost entirely ignored in most AI strategy conversations.
The Decision Cycle That Outlasted Its Usefulness
The traditional retail planning cycle was designed for a world where demand moved slowly and supply chain lead times were long. A weekly markdown decision made sense when consumer behavior was predictable across a seven-day window. A monthly assortment review made sense when changing a planogram was costly and reacting to short-term signals added little value.
Neither of those conditions holds in 2026. Social commerce, AI-driven discovery, and same-day fulfillment have compressed consumer decision cycles to hours in many categories. A product that trends on TikTok Tuesday morning can be out of stock by Tuesday evening at retailers without AI acting on that signal in real time. A competitor price change on Wednesday does not wait for Friday’s weekly review to become a disadvantage. The window between insight and actionable opportunity has shrunk. The decision cycle most retailers are running has not.
Consequently, the most expensive thing most retailers are doing right now is not a failed AI pilot. It is running a 2015 decision cycle on top of 2026 data infrastructure. The data exists. The signal is there. The time it takes to turn that signal into a decision and that decision into an action is where the margin goes.
The Data Problem Underneath the Retail AI Decision Gap
Most Data Is Accessible. Most of It Is Not Usable.
IBM’s 2026 research found that 64 percent of companies say their proprietary data is accessible to AI. However, only 49 percent of that data is actually usable. Furthermore, only 26 percent is being used to train AI models. That means the majority of data retailers have invested years collecting remains unstructured, uncleaned, or disconnected in a way that prevents AI systems from acting on it reliably. It sits in a state that requires significant human remediation before any automated decision can move forward.
Additionally, an experienced senior analyst can spend up to four hours investigating a single performance question, according to Retail Insight. Their DecisionInsight platform was designed to compress that investigation from hours to minutes. The time cost is not in the decision itself. It is in assembling the context needed to make the decision. When that assembly takes hours, the decision arrives after the window has closed.
The Organizational Structure That Fragments the Signal
The insight-to-action gap is not only a data quality problem. It is also an organizational structure problem. Companies designed enterprise analytics environments to mirror their own organizational structure. Merchandising sees its own view. Marketing gets campaign attribution. Pricing tracks benchmarks. Supply monitors inventory. Each function sees the data most relevant to its decisions. None of them sees the cross-functional causal picture that explains why a performance outcome happened.
In other words, a retailer can have clean data, connected systems, and a capable analytics team and still lose seven days explaining a margin shift. That shift required input from pricing, inventory, and promotional calendars before anyone could act on it. The problem is not that the data is wrong. It is that the organizational structure required to assemble the insight is slower than the market that produced the signal. As I described in the Year One agentic commerce analysis, the retailers who are winning are not the ones with the most sophisticated AI. They are the ones who have compressed the distance between signal and action.
What Closing the Retail AI Decision Gap Actually Requires
Moving AI From Analytical Layer to Decision Layer
The Incisiv study describes the shift precisely: move AI from analytical overlay into the decision itself. That is a different deployment model than most retailers are running. The typical retail AI deployment adds an AI layer on top of existing reporting infrastructure. Analysts still run reports. AI summarizes findings or surfaces an anomaly. Managers still hold the weekly review, with AI preparing the agenda. The decision still happens in the meeting. The AI has made preparation faster, but the cycle has not fundamentally changed.
By contrast, retailers who have closed the insight-to-action gap have deployed AI inside the decision itself, not around it. Pricing agents detect a competitive signal and adjust within minutes rather than waiting for a weekly review. Replenishment agents trigger an order when a velocity signal exceeds a threshold rather than waiting for a buyer to notice the trend on Monday. Markdown agents identify clearance timing based on real-time sell-through rather than a calendar-driven schedule. In each case, the AI is not preparing a human to make a decision. It is making the decision and escalating to a human only when the decision exceeds a governance threshold.
Governance as the Speed Enabler
The governance question is where most retailers slow down when they attempt to close the insight-to-action gap. Allowing an AI agent to make a pricing decision autonomously feels risky until the governance framework defines exactly which decisions the agent can make, which it must escalate, and what the fallback is when it escalates. Without that framework, the instinct is to require human approval for every decision. That requirement rebuilds the latency the AI was supposed to eliminate.
The retailers who have resolved this designed the governance framework before the AI deployment, not after. As I described in the eTail Boston AI adoption analysis, the retailers who get AI deployment right define clear decision ownership before the first agent goes live. Which calls belong to the AI? Which belong to the human? What happens when they disagree? That framework is what allows AI to compress decision cycles without creating governance risk. Without it, AI adds speed at the analysis layer and friction at the approval layer, and the net effect on decision cycle time is often close to zero.
The 14-Point Sell-Through Gap in Practical Terms
The Incisiv finding, that leading retailers achieve 71 percent full-price sell-through versus 57 percent for the industry, translates directly into margin. At 57 percent full-price sell-through, 43 percent of units require markdown, clearance, or write-off to move. At 71 percent, that number drops to 29 percent. For a retailer with $500 million in annual inventory investment, that difference represents hundreds of millions of dollars. The study found the gap is not driven by superior trend prediction or better buying decisions. It is driven by how quickly those retailers act once the demand signal is clear.
Moreover, the compounding effect matters. A retailer who acts on a demand signal three days faster than a competitor captures the pricing window before the competitor reacts, the inventory allocation before the size run breaks, and the promotional timing before the customer moves to a different category. Speed at the decision layer compounds across the entire margin stack in ways a point-in-time analysis does not capture.
What This Means for LatAm Retailers
The insight-to-action gap is more acute in LatAm retail environments for two reasons. First, currency volatility and supply chain variability create demand signals that move faster and with less predictability than in more stable markets. A pricing decision that could wait a week in a stable-currency environment cannot wait when the exchange rate has moved five percent and a competitor has already adjusted. Second, the organizational structure of many LatAm retail formats concentrates decision authority in fewer people, making the bottleneck at the human approval layer more pronounced.
However, this also means the payoff from closing the gap is higher. A LatAm retailer that deploys AI inside pricing, replenishment, and markdown decisions gains a larger relative advantage because the volatility that makes the cycle painful is the same volatility that makes speed more valuable. As I described in the KPMG retail AI returns analysis, the retailers getting the highest returns are the ones whose AI operates inside the decision, not alongside it.
The Honest Question About Your Own Decision Cycle
The Gap Is Not Hidden
Every retail organization knows roughly how long it takes to go from a performance signal to a corrective action. The weekly markdown meeting, the monthly assortment review, the quarterly promotional planning cycle. Those timelines are visible. What is less visible is the margin cost of each cycle that compounds across every product category every time the window closes before the decision is made.
The Question That Changes the Strategy Conversation
Specifically, one question reframes how most retailers should be evaluating their AI investments right now. For each category where AI is deployed in your organization, how long does it take from the moment the AI surfaces an insight to the moment a corrective action is taken? If the answer is days or weeks, you have added intelligence to your analysis layer without changing your decision layer. The analysis is better. The cycle is the same. The margin gap is still 14 points.
The retailers who win the next two years of AI-driven commerce are not the ones with the most sophisticated models. They are the ones whose models produce decisions that get executed while the opportunity is still open. The insight-to-action gap is not a technology problem. It is a deployment design problem. And it is the most expensive unsolved problem in retail AI right now.
If you are evaluating where your AI investments are producing decisions versus analysis, or designing the governance framework that allows AI to compress your decision cycles safely, connect with me here or reach me on LinkedIn. I am happy to walk through the framework we use across the U.S. and Latin America.
Adriana Rivas is a retail technology executive and AI strategist. She is the recipient of the Gold Stevie® Award, Thought Leader of the Year 2026, and recognized by Thinkers360 as the #7 Global Thought Leader in Retail. She is the author of How to Implement Self-Service Without Failing, with a Revised and Expanded Edition publishing September 21, 2026.