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Nestlé reduces merchandiser time per visit by 56% using agentic retail execution

The 2026 State of the US Consumer report from McKinsey shows rising living costs have pushed confidence to its lowest level in two years, making shoppers across income brackets more selective and value‑focused. Manufacturers feel pressure as competition for shelf space intensifies, and traditional retail execution—where merchandisers visit stores, collect data and report later—fails to address…

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Key points

  • Nestlé’s agentic retail execution cut merchandiser time per visit by 56% and supervisor workload by 55%.
  • AI agents enable real‑time shelf analysis, out‑of‑stock detection, and instant corrective actions during a store visit.
  • Target invested roughly $5 billion this year in store upgrades while Walmart is spending on automation and tech upgrades.

AI agents can analyze shelf images, flag gaps, suggest actions and verify fixes during the same visit, creating an “instant closed loop”. Nestlé adopted this approach, reporting a 56% reduction in merchandiser time per visit, a 55% drop in supervisor workload, zero training time for new activities, and a shift from data collection to AI‑guided execution. The stack relies on image recognition, augmented reality and brand data. McKinsey respondents said 37% rank in‑stock reliability among the top three reasons for choosing a retailer, highlighting the sales risk of empty shelves.

Walmart’s automation spending and Target’s roughly $5 billion store‑upgrade effort may help attract customers, though the article notes it does not guarantee sales.

Full story fromUnite.AI · by Ruslan Okhrimovych, CEO, Effie AIOpen source ↗

From Reactive to Real-Time: Agentic Retail Execution in Cautious Times

Unite.AI · 21 September 2026

The 2026 State of the US Consumer report from McKinsey offered an important glimpse into current consumer spending habits. Overall, rising living costs remain a dominant concern that had led to weaker consumer confidence.

From low-income households to the most affluent, consumers across the board are adopting a much more selective approach. The cautious atmosphere means consumers are prioritizing essential items first and foremost. Premium goods are still in demand, but consumers are being much more selective about when they invest in these, spending more time comparing similar options and placing emphasis on value for money.

As consumers become more selective and value-conscious, competition among manufacturers is intensifying, and manufacturers are already feeling the pressure.

The latest data from P&G released July 2026 paints a gloomy forecast for next year. The company forecast slower revenue growth in fiscal 2027 after quarterly sales fell short of estimates and margins dropped under a “very challenging geopolitical and economic environment”.

While some of the factors driving weak consumer confidence are out of the control of manufacturers, the in-store experience is one place where action can be taken.

Although the level of consumer optimism has fallen to the lowest level in two years, it doesn’t mean that US households don’t have funds available. The K-shaped economy is narrowing, and a growing number of Americans have actually moved up the income ladder.

This indicates that there is potential to reclaim margins but consumer goods brands can’t leave this up to chance. The consumer pullback means competition is growing between manufacturers to capture the disposable income that selective shoppers are choosing to spend.

The shelf, where availability, visibility, promotion, and the final purchase decision come together, is becoming more important than ever with this fierce competition for every dollar being spent between competitor brands.

In the face of fierce competition between consumer brands, it’s quickly becoming clear that traditional retail execution is too slow and reactive to address in-store merchandising challenges that could result in a lost sale, such as out-of-stock shelves or non-compliant displays.

Agentic retail execution, however, provides a way to bring the process into real-time as competition for consumers’ disposable income gets more intense.

The Problem With Traditional Retail Execution

It’s no secret that the way supermarkets display products and direct footfall isn’t something that’s left up to chance. From the way that the aisles have been planned to the exact location of each product, retail displays are planned with meticulous attention to detail.

In this example, while the supermarket has control over high-level decisions such as the location of the bakery or fresh produce section, consumers are ultimately the key stakeholders. Premium brands will invest significantly to ensure their products stand out through eye-level product placements, end-of-aisle displays, and even host pop-up promotional experiences for seasonal events or new product lines.

Traditionally, brands manage how this investment plays out in the store through merchandising managers who attend physical locations to ensure that merchandising guidelines are being followed correctly. But this was largely a reactive process in which actions were taken retrospectively only after a brand representative visited the store, collected data and submitted a report.

In-store merchandising is normally managed through a network of merchandising and product managers who visit retail locations to check whether products are correctly displayed, identify non-compliance, review discount promotions and identify poor shelf execution or out-of-stock goods with empty shelves.

However, this is a reactive process at its core. By the time the merchandising manager has identified an issue, a report needs to be prepared and submitted before relevant actions are taken. This is far from a real-time process.

Reps have to cover a number of locations in their coverage area, which takes time to cycle through. This means major flags like non-compliance or out-of-stock shelves can take time to identify and rectify. In the meantime, the brand perception with the consumer is at risk and sales are taking a hammering.

In short, traditional retail execution is too slow and reactive to enhance performance in the new era of consumer caution. Information is collected during the visit, analyzed later, and frequently reaches supervisors or HQ after the sales opportunity has already been lost.

As consumer optimism takes a hit and shoppers demonstrate a much more cautious approach when choosing which good to purchase, the challenges with traditional retail execution come into even sharper focus for manufacturers.

How AI Agents Can Enhance Retail Execution

AI Agents can now perform virtually every non-physical component of a store visit: analyze the shelf, identify execution gaps, determine the next best actions, guide the field representative, verify the result, collect data, and report back to HQ in real time.

The field representative performs the physical shelf tasks, while the AI Agent manages the intelligence, decision-making, guidance, and validation behind the visit. This creates an Instant Closed Loop in which shelf issues are not merely reported but corrected and verified during the same visit.

AI-guided, real-time retail execution completely changes that dynamic. It’s built around the idea of an “instant closed loop”, in which shelf issues are not merely reported but corrected and verified during the same visit.

Nestlé adopted this approach and reduced merchandiser time per visit by 56%, supervisor workload by 55%, embedded new activities into field execution with zero training time, and shifted from data collection to AI-guided execution at the point of sale.

The ideal tech stack behind this process includes image recognition, augmented reality, AI-ready data from the brand or retailer and AI agents with the ability to respond to these inputs and guide execution step-by-step during store visits.

How an Instant Closed Loop Improves Customer Experience

As competition between goods manufacturers intensifies and each product battles to win over consumers’ attention.

With agentic retail execution and an instant closed-loop system, consumer goods brands can increase the likelihood that customer will choose their products when in-store.

For example, consumers are prioritizing value more than ever as a way to make their budgets stretch further. However, value doesn’t always mean the cheapest option.

While consumers are seeking promotions and lower-priced alternatives, they are also looking for better value for money, and this isn’t the same thing across the board; good value could mean anything from a product being especially durable or providing better quality than budget alternatives.

As a result, consumers are still likely to buy trusted premium brands when the price can be justified. In their quest to find value for money, shoppers are doing their research to find the best price for products in a correlative relationship that most stakeholders fail to recognize in time. Rather than simply looking for lower prices, shoppers expect brands to clearly communicate durability, quality, and functional benefits.

With an instant closed-loop system to support retail execution, merchandising teams can respond to local demographics and personalize discounts, display information and pop-up events to make sure value is demonstrated. The AI agents can identify these opportunities when the merchandising agent is in store, comparing corporate back-end data with location-specific purchase trends to identify immediate actions on in-store promotions and discounts relevant to the site.

Eliminating Out-of-Stock Failures

However, even if a product is in high demand with customers, it’s impossible to generate sales if the shelves are empty. An out-of-stock item not only means that an immediate sale is lost, but it can damage brand perception over time.

Shoppers prioritize efficiency, especially for convenience-oriented trips: 37% of McKinsey respondents ranked in-stock reliability as one of the top three reasons behind their choice of retailer. However, if their product is out of stock frequently, it reflects badly on the brand and can push the customer into the hands of the competition permanently.

With agentic retail execution, manufacturers can identify instances of out-of-stock items immediately using image recognition. With this data immediately available to head office, actions can be taken, whether that’s making sure the retailer restocks displays more regularly or identifying locations with high demand for particular products so that supply chain distribution can be adjusted to keep appropriate volumes in local stock rooms and warehouses.

Cautious Consumer Spending Ups the Ante for Retailers and Brands Alike

As weak consumer confidence leads to much more selective spending habits, brands will need to work much harder to beat out the competition.

In efforts to reverse stagnant spending projections for 2027, major retail stores are investing heavily in trying to engage with customers.

Walmart is spending money on automation, new warehouses, and tech upgrades, while Target invested roughly $5 billion this year to tidy up stores and add new, buzzy brands to shelves.

This may help to get customers through the door, but it won’t guarantee a sale. In the face of stiff competition between consumer brands, manufacturers can’t afford to rely on a reactive retail execution strategy that could leave popular products out of stock for days or leave prime sales opportunities on the table for other brands.

By adopting an instant closed-loop approach that uses AI agents, image recognition and powerful data analytics to create a real-time execution environment, every identified gap can immediately become an action. By putting the right goods in front of target consumers at the right price point, consumer goods manufacturers can boost sales of popular goods and address rising competition across the market.

This text was published by Unite.AI and written by Ruslan Okhrimovych, CEO, Effie AI. It is reproduced here with attribution so you can read it in full; the rights remain with the publisher. Read it at the source ↗

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