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POI State of Industry 2025: Invest in AI Now to Lead in Retail Execution

Written by Admin | April 8, 2025

The 2025 POI State of the Industry Report, conducted by the Promotion Optimization Institute, offers a comprehensive analysis of how consumer goods companies are leveraging emerging technologies to drive growth and efficiency. This year’s report focuses on the transformative impact of artificial intelligence and machine learning on retail execution strategies. 

  1. AI-Powered Applications in Retail Execution

POI finds that artificial intelligence (AI) and machine learning are increasingly woven into retail execution (RetX) strategies​. Emerging AI-driven capabilities include dynamic coverage models that optimize the type of store service (e.g. deciding when to send a rep, merchandiser, broker, or use a virtual call) based on location and needs​.

AI can also generate intelligent order and distribution recommendations, for example, suggesting optimal restock orders and even providing menu or beverage suggestions in foodservice contexts​.

Advanced tools leverage computer vision and facial recognition: image-based analytics (sometimes enhanced with augmented reality) can instantly assess shelf conditions without manual photos, while facial recognition can gauge a decision-maker’s sentiment during sales visits to refine the sales approach​.

Additional applications range from self-service retail execution (allowing store personnel to perform certain tasks in the sales call process via AI guidance) to back-office analytics that prioritize sales opportunities for field teams​

In sum, AI is helping CPG companies bring their in-store strategy to life by making each store visit smarter and more data-driven – critical given that poor retail execution can lead to up to 25% of lost sales​.

  1. AI-Driven Tools and Platforms in Action

Leading solution providers are now embedding AI into core retail execution processes to boost efficiency and sales. Key AI-powered tools highlighted by POI include:

  • Image Recognition for Shelf Intelligence: Vision AI is used to streamline planogram compliance checks and identify shelf stock gaps in real time​

This speeds up gap detection (like out-of-stocks) and ensures corrective actions faster.

  • Retail Activity Optimization: Machine learning prioritizes field tasks (store visits, audits, etc.) to maximize the value of each call, focusing reps on the highest-impact activities first​.

  • Autogenerated Orders: Execution platforms now use AI to auto-suggest orders (e.g. trigger reorders for out-of-stock or missing items) so that shelves stay stocked with high-performing products​. This reduces missed sales from empty shelves.

  • Guided Selling: Apps are providing sales reps with AI-driven, tailored recommendations for each interaction​

Reps receive guidance on which products or promotions to pitch to each store based on data, making every sales call more targeted.

According to the report, deploying these AI tools effectively “can turn each in-store call into a strategic opportunity, driving measurable improvements in both efficiency and ROI.”

  1. Impact on Omnichannel, Personalization, and Forecasting

Retail execution must succeed across physical and digital channels, and AI is a key enabler in meeting these complex demands:

  • Omnichannel Consistency: The future of execution spans “interactions with customers across all channels”, not just in-store​.

AI helps unify execution by analyzing omnichannel data to ensure a seamless, consistent experience for consumers who expect frictionless shopping both online and offline​.

For example, AI-driven analytics can coordinate promotions and inventory levels across e-commerce and stores so that the brand experience remains consistent.

  • Personalized Experiences: Personalization at scale is becoming reality in retail execution thanks to AI and rich consumer data. POI notes that leveraging first-party data unlocks the ability to deliver “highly personalized experiences” and strengthen loyalty across digital and physical touchpoints​

In practice, AI can segment consumer preferences and help field teams tailor in-store merchandising or offers to local shopper tastes. POI envisions a new wave of execution that “integrates digital insights with in-store experiences,” so that a shopper’s online behavior can inform a personalized in-store interaction​.

Connecting the brand’s digital experience with in-store engagement – for instance, through interactive content or smart kiosks – ensures stronger customer connections​.

  • Predictive Forecasting: AI’s machine learning models are improving demand forecasting and planning accuracy, which directly impacts retail execution (ensuring the right product is at the right place at the right time). The report highlights the use of predictive and prescriptive analytics to forecast trends and demand, enabling proactive execution decisions rather than reactive scrambling​

Advanced AI models can anticipate store-level sales velocities or out-of-stock risks, so supply chain and field teams can adjust execution plans in advance. This not only supports smoother omnichannel fulfillment but also informs more effective promotion and inventory strategies at the store level.

The POI 2025 State of the Industry Report (SOI) is recognized as an essential resource for manufacturers and retailers. Download the full complimentary PDF report here.