# Nestlé Ukraine Cuts Merchandiser Time by 56% with AI Agents

> The consumer goods company's deployment of effie.ai agents in Ukraine automates retail execution tasks to enable real-time decisions and verifications, producing measurable reductions in field and supervisory workloads across high-volume store operations.

*Published 2026-07-07 · By Diane Okafor*

AI agents are autonomous software systems that analyze real-time store data through image recognition and direct field actions to optimize retail execution without manual oversight.

## Executive Summary

Nestlé Ukraine, operating as a subsidiary of the global consumer goods company Nestlé in the competitive food and beverage sector, implemented AI agents supplied by effie.ai to automate retail execution across its distribution network. The system handles store visit tasks including analysis and verification while leaving physical product handling to human merchandisers. Primary outcomes include a 56 percent reduction in merchandiser time per visit and a 55 percent drop in supervisor workload, based on data presented by the Category Management Association.

The deployment spans roughly 45,000 store visits each month and covers about 1,500 SKUs distributed across seven categories. Additional results encompass data accuracy rising from 75 percent to 97 percent, a 60 percent cut in shelf audit time, and promotional availability increasing from 84 percent to 95 percent. These gains arise from the construction of a digital twin of retail execution that integrates image recognition, GPS positioning, timestamps, and machine-readable compliance rules.

The initiative marks a deliberate move away from visibility-focused reporting toward agent-led autonomous execution at the point of sale. Real-time decision making replaces delayed post-visit analysis, enabling immediate adjustments to shelf conditions and promotions. Maksym Oliinyk, Sales Automatization Strategy Development Manager at Nestlé Ukraine, contributed to the strategic direction of the project.

## What Challenges Did Retail Execution Face Prior to AI Agents?

Traditional retail execution in consumer packaged goods relies on manual store audits where field teams collect data on shelf presence, stock levels, and promotional compliance before submitting reports for later review. This sequential process creates delays between observation and corrective action, often resulting in missed sales from out-of-stock situations or non-compliant displays. In Ukraine's distribution environment, the volume of outlets and variability in store formats compound these timing issues, limiting the ability of teams to respond promptly to dynamic shelf conditions.

Prior systems emphasized data gathering and centralized reporting rather than on-site resolution, requiring supervisors to allocate significant time to interpreting reports and issuing follow-up instructions. The separation between collection and execution introduced errors and reduced overall accuracy, with baseline data quality recorded at 75 percent before the agent deployment. Introducing new compliance requirements or promotional rules historically demanded dedicated training sessions for merchandisers, extending implementation timelines and increasing operational overhead.

Nestlé Ukraine identified these structural limitations as barriers to efficient execution and pursued a technology-driven redefinition of the process. The focus shifted to embedding analytical and directive capabilities directly into the visit workflow, allowing decisions to occur in real time. This addressed the core inefficiency of post-visit processing while maintaining the hands-on merchandising element that requires human presence.

## How Was the effie.ai AI Agent System Deployed at Nestlé Ukraine?

The implementation established a digital twin model that replicates physical retail spaces using image recognition to capture shelf configurations during each visit. GPS data and timestamps contextualize every observation, while machine-readable requirements define compliance standards for each SKU and category. The agents then process this input to generate immediate instructions for the merchandiser, covering placement, promotion setup, and inventory verification.

Field teams receive guidance through the mobile interface while still at the location, enabling on-the-spot corrections that are subsequently verified by additional image captures. Only the physical manipulation of products remains outside the automated scope. The system supports the full monthly volume of 45,000 visits and scales across the 1,500 SKUs without requiring separate infrastructure for each category.

Integration occurred within existing field operations, allowing rapid incorporation of new activities without training periods for staff. Ruslan Okhrimovych, CEO and Founder of effie.ai, collaborated on tailoring the agent capabilities to Nestlé's specific execution needs. The phased rollout first established visibility through imaging before advancing to full agentic decision making and verification loops.

The partnership leveraged effie.ai's platform to create a closed-loop system where observations feed directly into actions and confirmations. This eliminated the previous reliance on manual report generation and review cycles. Maksym Oliinyk oversaw the alignment of the technology with Nestlé Ukraine's sales automation strategy, ensuring the deployment supported broader multiyear transformation goals.

## What Are the Specific Technical Components of the AI Agents?

Image recognition forms the foundational layer, identifying product positions, stock availability, and promotional integrity with precision sufficient to support automated evaluation. Location services via GPS and temporal data from timestamps ensure each assessment reflects the exact conditions at the moment of capture. Machine-readable rules encode category-specific and SKU-specific standards, enabling the agents to compare observed states against targets without external input.

The agents operate autonomously after initial setup, interpreting visual and contextual data to formulate and issue directives. Follow-up imaging closes the verification loop, confirming that recommended changes have been executed accurately and updating the digital twin accordingly. This architecture supports simultaneous processing of data from numerous visits, maintaining consistency at the reported scale of 45,000 monthly interactions.

Scalability derives from the modular rule set that accommodates seven categories and 1,500 SKUs with tailored logic for each. The system avoids dependency on post-visit human analysis by performing all interpretive and directive functions in the field. Such design choices enable the transition from reactive reporting to proactive, point-of-sale execution management essential for maintaining competitive shelf presence.

## Which Quantified Wins Were Achieved Through This Implementation?

The deployment delivered a 56 percent reduction in merchandiser time per visit, allowing field resources to cover more locations or focus on complex tasks. Supervisor workload declined by 55 percent, shifting emphasis from routine oversight to higher-level coordination. These time efficiencies combine with quality improvements including data accuracy advancing from 75 percent to 97 percent and shelf audit time falling by 60 percent.

Promotional availability increased from 84 percent to 95 percent, directly supporting revenue through better execution of in-store campaigns. The Category Management Association documented the core time reductions, while effie.ai reported the accuracy and audit metrics. Consumer Goods Technology covered the promotional availability gains in its coverage of the multiyear transformation.

Performance Metrics Comparison Before and After AI Agent Deployment at Nestlé UkraineMetricBefore DeploymentAfter DeploymentAttributed SourceMerchandiser time per visitStandard duration56% reductionCategory Management AssociationSupervisor workloadStandard level55% reductionCategory Management AssociationData accuracy75%97%effie.aiShelf audit timeStandard duration60% reductioneffie.aiPromotional availability84%95%Consumer Goods Technology

## What Steps Outline the AI Agent Workflow in Store Visits?

The workflow integrates AI direction with human execution to ensure decisions occur on location with immediate verification. Merchandisers follow agent-generated prompts throughout the visit, capturing necessary data and implementing adjustments under real-time guidance. This structure minimizes the time between identification of an issue and its resolution.

- Capture initial shelf images and location data using the mobile interface.
- AI agent analyzes data against requirements to identify discrepancies and needed changes.
- Provide real-time directives to the merchandiser for adjustments to products and promotions.
- Merchandiser performs hands-on placement while agent verifies through additional images.
- Confirm execution compliance and log the visit details in the digital twin system.

Each step builds on the previous to create a continuous feedback mechanism. The ordered sequence prevents the accumulation of unaddressed issues that previously required separate follow-up visits. Verification imaging ensures that all actions meet the predefined standards before the visit concludes.

The process supports embedding new activities directly into the agent logic, eliminating separate training requirements. Consistency across visits improves because the same rule set applies uniformly, reducing variability introduced by individual judgment. This contributes to the overall workload reductions observed for both merchandisers and supervisors.

## How Does This AI Deployment Impact Broader Market Stakeholders?

Other consumer goods manufacturers can reference the Nestlé Ukraine results when assessing AI agent investments for their own retail networks. The documented time savings and accuracy improvements indicate potential cost reductions and productivity gains that scale with visit volume. Companies facing similar distribution challenges may adapt the digital twin approach to their specific category requirements.

Retail partners benefit from more reliable shelf compliance and promotional execution, which can improve sell-through rates and reduce stockouts. The technology's capacity to incorporate new rules without training supports faster response to seasonal or campaign-driven changes. Workforce implications include reallocation of human effort toward physical merchandising and relationship management rather than data transcription.

The case illustrates how agentic systems can augment rather than replace field roles, preserving the value of on-site presence while automating analytical components. This model may influence procurement and implementation decisions across the sector, particularly for organizations pursuing multiyear digital transformation strategies.

## What Do Experts Say About Nestlé Ukraine's AI Agent Use?

The Category Management Association webinar positioned the deployment as one of the first large-scale examples of AI agents applied specifically to retail execution. Emphasis was placed on the zero-training-time feature that enables rapid introduction of new field requirements. Maksym Oliinyk's involvement signals internal prioritization of automation within Nestlé Ukraine's sales operations.

> Nestlé Ukraine set out to change that not by improving reporting, but by redefining execution itself.Ruslan Okhrimovych, CEO & Founder, effie.ai

Industry reporting from Consumer Goods Technology noted the partnership with effie.ai as part of a broader transformation strategy focused on visibility and efficiency. The combination of accuracy gains and workload reductions provides concrete evidence of value in operational settings.

The reported metrics from multiple independent sources reinforce the practical viability of shifting execution models toward agent autonomy. This provides decision-makers with attributable benchmarks when evaluating comparable technology adoptions in their organizations.

## What Comes Next for AI Agents in Retail Execution?

The Ukrainian results establish a foundation for potential expansion to additional markets or product portfolios within Nestlé's operations. Continued refinement of image recognition and agent logic could incorporate predictive elements to anticipate shelf issues before they occur. Integration with wider supply chain platforms may further extend the impact beyond individual store visits.

Peer enterprises are positioned to draw on the quantified outcomes when planning their own pilots or rollouts. The emphasis on measurable efficiency metrics supports business case development for AI investments in field operations. Future iterations may address additional categories or increase the proportion of tasks handled autonomously.

The overall trajectory points toward wider adoption of agentic systems in retail execution as the technology matures and more case studies emerge. Nestlé Ukraine's experience offers a reference for balancing automation with necessary human elements in physical retail environments.

Sustained monitoring of performance indicators will determine the long-term scalability and return profile of the deployment. This data-driven approach aligns with enterprise expectations for technology initiatives that deliver clear operational improvements.

## Sources

1. [Nestlé deployed AI agents achieving 56% reduction in merchandiser time per visit and 55% in supervisor workload, with zero training time for new activities.](https://www.catman.global/event/product-demo-the-first-ai-agents-for-retail-execution-how-nestle-deployed-ai-agents-to-transform-retail-execution/)
2. [The AI agents reduced supervisor workload by over 50%, decision-making time by up to 90%, with ~45,000 store visits per month and data accuracy improvements.](https://effie.ai/blog/nestle-ukraine-from-visibility-to-store-execution-by-agent/)
3. [Nestlé achieved data accuracy from 75% to 97%, 60% reduction in shelf audit time, and promotional availability from 84% to 95% through the effie.ai partnership.](https://consumergoods.com/nestle-boosts-ukraine-rex-visibility-efficiency-ai-optimizations)

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Source: https://aiintelreport.com/ai-agents/nestle-ukraine-ai-agents-retail-execution
Index: https://aiintelreport.com/llms.txt · Full text: https://aiintelreport.com/llms-full.txt