Key takeaways:
Across the retail industry, the consensus on AI is clear: it’s an absolute operational necessity. But while confidence in AI’s potential is nearly universal, a closer look at the planning landscape reveals a stark, costly divide.
The 2026 Retail Resilience & AI Adoption Study, developed by Incisiv, World Retail Congress, and Anaplan, surveyed retail executives across North America and EMEA. From this research a theme emerged: despite massive investments in data visibility and analytics, actual AI deployment lags dramatically behind. Retailers know more than ever, but they aren’t acting any faster.
This leads to the “latency tax,” the costly financial drain that occurs between the moment you see a problem and the moment you resolve it. For the average retailer, this tax costs five cents on every dollar of revenue. For a billion-dollar retailer, that is a staggering $50 million in lost value annually through markdowns, stockouts, and delayed decisions.
The gap between belief and reality?
| Planning category | % Important | % AI deployment | Point gap |
|---|---|---|---|
|
Integrated planning |
91% |
21% |
70% |
|
Assortment planning |
84% |
22% |
62% |
|
Demand forecasting |
93% |
31% |
62% |
|
Supply planning |
86% |
21% |
65% |
|
Supplier management |
79% |
17% |
62% |
|
Inventory optimization |
91% |
26% |
65% |
|
Pricing and promotion |
85% |
23% |
62% |
|
Exception management |
71% |
13% |
58% |
The research highlights a massive 60-point gap between AI’s perceived importance (over 85%) and its actual deployment (under 25%) in key retail planning functions.
When we look closely at where AI is deployed, a telling narrative forms. Retailers have concentrated their AI investments in demand forecasting, where adoption sits at 31%. This makes sense; forecasting is a well-understood capability that represents a relatively straightforward application of traditional deterministic AI.
The real struggle lies in other critical planning use cases — complex optimization tasks that forecasting AI is simply not equipped to handle.
The exception management problem and the agentic unlock
Nowhere is this adoption gap more glaring than in exception management. Despite being one of the single biggest efficiency and margin-protection drivers in the retail planner’s toolkit, AI deployment for exception management sits at a mere 13%.
Why is adoption so low where speed matters most? In practice, exception management is essentially real-time automation. Traditional automation has successfully moved the operational bottleneck from “we didn’t know” to “we knew but couldn’t act fast enough.”
When a supply disruption or demand spike occurs, systems flag the exception, but a human planner must still interpret the alert, log into multiple siloed systems, negotiate across departments, and manually execute a workaround.
AI-driven, agentic supply chain planning represents the ultimate unlock for complex, real-time automation. Building on a deterministic foundation that correctly forecasts a problem, role-based AI agents operate within predefined business guardrails to evaluate multi-variable scenarios and execute adjustments instantly. By transitioning from simple alert-based systems to system-recommended and autonomous orchestration, retailers can finally bridge the gap between planning and execution.
The AI-readiness gap: Equipping the workforce
Technology, however, is only as powerful as the people operating it. As retail planning pivots from manual data reconciliation to system oversight, roles are fundamentally transforming.
The study reveals a stark AI-readiness gap:
- 51% of retail executives expect responsibilities and tasks to be fundamentally different due to AI.
- Only 11% of the workforce has received any form of AI upskilling or training.
To capture the true value of an Agentic Enterprise, retailers must prepare their planning teams to shift from doing the analysis to managing the systems that do the analysis.
Stop paying the latency tax
The competitive edge in modern retail is increasingly rooted in how quickly and efficiently you can convert demand signals into margin. Leaders who close the latency gap achieve 71% full-price sell-through (compared to just 57% for the rest of the industry) and reduce their latency tax from five cents to just two cents on the dollar.
