Industry
Retail
Omnichannel CX and Intelligent Innovation – The Future of Retail
HIGHLIGHTS
- AI helps retailers understand demand from the point of sale, using what’s really happening in stores, not just assumptions. This moves them from reacting late to acting smart and early.
- By using sales data alongside factors such as weather, store traffic, and local events, retailers can keep the right stock in the right place, avoid empty shelves, cut waste, and protect profits.
- Retailers that use AI to autonomously manage their operations—allowing planners to focus only on exceptions—become more resilient, free up cash tied up in inventory, and respond faster to local market changes than their competitors.
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Overview
In today’s environment, well-known retail supply chains are under pressure from macroeconomic volatility, local customer behaviour, and the demands of omnichannel fulfilment. Yet many retailers continue to insist on top down forecasting, setting national targets, allocating volumes regionally, and pushing inventory through the network. Even though this approach looks sensible in a planning cycle, it doesn’t reflect operational reality.
The reality is that market leaders should consider moving away from top down models in favour of an AI driven, bottom up approach to improve efficiency, protect revenue, and increase margins. By capturing demand at a granular level (Point of Sale, by store, by hour) and leveraging artificial intelligence, retailers can transition from reactive supply chains to intelligent, autonomous demand networks.
This perspective outlines the strategic imperative for bottom up demand sensing, its cross format applications from Quick-Service Restaurant (QSR) to fashion, and a pragmatic blueprint for CXOs to implement this capability at scale.
For executive leadership, this strategic shift is not purely improving forecast accuracy alone, it is about protecting revenue, unlocking working capital, and expanding margins.
Why top-down fails
Enterprises usually are inclined to run the network top‑down to align operational execution with corporate ambition. Top‑down numbers are useful for direction or guidance, but they often fail to capture local needs.
In fact, when a national override replaces granular, store‑level signals, traceability is lost, the bullwhip effect (small demand changes cause large upstream swings) is triggered, and operational teams stop trusting the numbers. The result is twofold: high‑performing stores are starved of inventory, while underperforming stores drown in excess stock.
In a nutshell, the only operational “truth” that matters is what customers actually buy - by store, by day, and by hour. Everything else is an assumption.
Bottom‑up sensing
The most resilient supply chain strategy inverts the traditional model. It starts with actual location‑level consumption, builds the forecast at the lowest meaningful grain, and uses AI to aggregate demand upward to distribution centres, processors, and suppliers.
This shift delivers four foundational advantages:
An AI‑powered, bottom‑up approach treats location‑level demand as the source signal. In addition, it supercharges this baseline by ingesting external data such as local weather forecasts, footfall patterns, and local event schedules.
When the baseline forecast is reliable, supply chain teams trust the system, and they can manage the entire network by exception. This is where AI transforms human productivity. Exceptions dashboards flag significant outliers, so planners can perform root‑cause analysis to identify why a deviation is occurring.
Volatility is a constant in modern retail. The differentiator between leaders and followers is the cadence at which they ingest actuals, detect variance, and recalibrate. Machine learning models run continuously; recalibrating forecasts autonomously as new POS data arrives. This shifts demand sensing from a panicked periodic reaction into a constant, synchronised operational heartbeat.
The strongest enterprise operating models function like automated control systems. AI acts as the engine, setting dynamic thresholds for human intervention, running continuous digital‑twin simulations to model scenario impacts, and creating a closed feedback loop where post‑event analysis refines future algorithms. The system does not just predict; it learns and adapts autonomously.
The business impact
Across different retail sub-sectors, the business value is not only in improving supply chain planning but also in leveraging capital allocation, to place the right inventory in the right location at the right time, with minimal manual intervention.
While the foundational AI engine remains consistent, the business outcomes scale across different retail sub‑sectors:
- Grocery: A national grocer shifting to AI‑driven, store‑level sensing for fresh‑food categories can factor in local weather drops and intra‑day footfall.
Business impact: Immediate reductions in fresh shrink (often 15–20%) while improving local on‑shelf availability, directly expanding net margins. - Fashion & speciality retail: Apparel relies on managing short‑lived trend volatility. Bottom‑up signals detect when a specific Stock Keeping Unit (SKU) surges in certain regions.
Business impact: Autonomous replenishment adjustments and optimised Buy Online, Click and Collect routing, driving double‑digit improvements in full‑price sell‑through and preventing stranded stock. - Quick‑service restaurants: QSRs face intense intra‑day volatility driven by weather, traffic, and Limited‑Time Offers (LTOs).
Business impact: Consistent hourly ingredient preparation, reducing food waste, and optimising labour scheduling.
The CXO Imperative
Executives should expect sequential ROI (Return on Investment). The first improvement is a sharp reduction in stock‑outs and enhanced on‑shelf availability, immediately protecting revenue. This is followed by improvements in working‑capital efficiency through better inventory turns, and sustained margin expansion driven by reduced markdowns and waste.
For this model to succeed, leadership must commit to a cultural shift. Executives must stop applying manual, uniform overrides—such as flat, national percentage increases to inventory targets to meet financial aspirations. Manual overrides corrupt granular AI signals and erode organisational trust in the data. Leadership should use a top‑down strategy to set financial provisions, while allowing the algorithmic engine to determine local replenishment decisions.
The greatest risk of remaining on legacy top‑down systems is systemic margin erosion. In a unified commerce environment, this approach guarantees demand distortion—stranding working capital in the wrong locations while digitally native competitors capture local market share through superior availability.
From pilot to scale
While the commercial upside is significant, the journey requires overcoming common challenges—most notably poor data hygiene, legacy Enterprise Resource Planning (ERP) silos, and integration complexity. Applying AI to flawed Point of Sale (POS) data merely amplifies noise.
We recommend a value‑led implementation approach that avoids large‑scale “rip‑and‑replace” programmes:
Select a defined scope—such as fresh food or ambient category —to prove value and measure early returns.
Build foundational AI models using existing, cleansed POS and store‑level inventory data before integrating additional external sources.
Transition planners to exception‑based workflows and build confidence through transparency and early, measurable wins.
Once trusted, incorporate high‑frequency external signals (weather, events, promotions) to continuously refine accuracy.
The shift from top‑down, assumption‑based planning to granular, AI‑driven demand sensing represents a turning point for the retail industry. By anchoring supply chains in the ground truth of store‑level consumption, retailers can break the cycle of demand distortion and move towards localised, intelligent networks that understand the customer behaviours nuances.
For retail leadership, bottom‑up, AI‑driven demand sensing is no longer a technology initiative—it is a commercial imperative that determines how resilient and responsive the enterprise is. By anchoring supply chains in store‑level consumption, retailers can build intelligent networks that quickly adapt to customer behaviour and turning this into a competitive advantage.