In the dynamic world of retail, generative artificial intelligence (AI) is revolutionizing the game. But can it also revolutionize how retail stores operate? In this article, we explore how generative AI can serve as a catalyst for retailers to operate their stores leaner and more cost-efficiently, while providing store associates with enhanced knowledge and decision support.
Furthermore, we believe that generative AI has the potential to fuel new growth for retailers, particularly by improving the customer shopping experience through hyper-personalized offers. Here, we delve into the impact of generative AI on store operations and the workforce, showcasing accessible and ready-to-implement use cases that can generate high impact in these early stages of technology adoption.
Two key benefits of generative AI in retail store operations
The adoption of generative AI in stores brings forth a twofold benefit: driving cost and productivity improvements and fueling new growth. Together, these benefits create a virtuous cycle that propels businesses forward.
1. AI can drive retil cost and productivity improvements
Imagine a store where 40% to 60% of human tasks are automated using AI. First-line managers (such as store managers and shift leaders) believe that 45% of their own jobs could be automated by generative AI, which aligns with various expert views. In contrast, entry-level blue-collar employees think only 36% of their jobs could be automated by AI. However, experts believe the potential for automation in these roles is much higher, in the range of 60% or more.
Generative AI acts as a catalyst for efficiency and effectiveness in store operations in the following three areas.
- Streamlining repetitive tasks of store associates. Generative AI can automate recurring tasks, such as employee labor scheduling, predictive maintenance of store equipment, routine customer inquiries (for instance returns or exchanges), or onboarding of new colleagues, freeing up employees’ time to focus on higher-value activities like customer interaction and sales opportunities.
- Enabling better and faster decision-making by augmenting (not replacing) human expertise with generative AI “copilots.” For example, generative AI can support associates in answering more complex customer questions or supporting inventory management and production planning decisions.
- Elevating the role of store management by shifting focus from task execution to validation and action-taking. For example, generative AI can support store and department managers through automated reporting analysis, summary of insights, and action planning based on multiple daily store and department performance reports, alerting compliance issues, flagging waste reduction opportunities, or detecting fraud in stores.
2. AI solutions can fuel new retail growth
New growth is driven mainly by three types of generative AI solutions.
- Solutions that support store associates in better serving customers, for example by using generative AI copilots to help answer basic or more complex customer questions (for instance, “Help me find a healthy cereal option for kids under $5.”)
- Reallocating time freed up by generative AI solutions, for example time savings from automating repetitive tasks and supporting decision-making can be invested in activities highly valued by customers.
- End-customer-directed sales and profitability-driving generative AI solutions, for example: Hyper-personalized customer outreach, such as promotions and product recommendations, leveraging data at scale for improved relevance; offering unique services like meal inspiration and planning through AI copilots; or redirecting increased productivity toward innovation, particularly in merchandising and marketing functions.
Solutions in the last category are primarily driven by leveraging generative AI in upstream functions, such as marketing and merchandizing, and are less prevalent in the hands of store operators. However, as we take an end-to-end view of the impact of generative AI on omni-channel shopping, we did not want to miss mentioning these opportunities in this article.
Our most recent research shows that consumers’ interest in user-facing AI solutions is currently limited. This is particularly driven by concerns around data privacy issues. Personalized promotions, however, are an area of high interest and where generative AI can unlock material benefits. Despite limited interest, retailers should still find value in exploring generative AI solutions and building capabilities because, at some point, customers will be interested.
How generative AI can boost productivity and empower store workers
Imagine equipping every store associate with a tool that amplifies their capabilities. That’s the promise of generative AI. Unlike traditional large language models (LLMs), generative AI is accessible for the masses, easy, and user-friendly. Implementing generative AI into stores does not require over-engineered change management to empower thousands of frontline workers.
We believe the big unlock comes from upskilling store employees to use generative AI for high-impact use cases while democratizing the usage of basic generative AI solutions across a broad set of associates. This democratizing approach to generative AI will allow stores to find many of the small — but cumulatively important — productivity improvements. The result? A noticeable boost in efficiency and growth, achieved without the usual formalities.
The harder part is integrating generative AI in complex, multi-step processes. While looking for the “big solution” is the ultimate goal for harvesting the transformative power of generative AI, a focused, intermediate effort builds critical experience and overcomes inertia.
Key generative AI use cases for store associates and customer service
Generative AI is not just a tool but a partner for store associates. It enhances their ability to serve customers more effectively and manage tasks with greater efficiency. By integrating generative AI, stories associates can streamline their workflows, improve customer interactions, and ultimately boost overall productivity.
Enhancing workforce efficiency
Knowledge assistant: A generative AI chatbot that allows store associates to ask questions about process standards, HR policies, training material, and more. This tool reduces requests to HQ and helps get answers faster, including benchmarking against competition.
Task management: Informed by generative AI and real-time computer vision tracking, this system can significantly improve task management. For example, if a picture of spoilage on the floor is detected, it triggers an immediate task list alert. This proactive approach ensures that issues are addressed promptly, enhancing overall efficiency and maintaining high standards within the store.
Predictive maintenance: Generative AI can generate maintenance recommendations by analyzing data to detect anomalies. This approach eliminates the need for manual monitoring of equipment like coolers by employees, ensuring timely and efficient maintenance actions.
Imporving customer interactions
Localized customer feedback: Generative AI analyzes unstructured feedback from customer reviews across multiple sources to provide insights into all areas of store operations and customer satisfaction, including benchmarking to competition.
Store analytics and reporting: Conversational search allows for store management to get rapid insights and suggested actions around store KPIs without analyzing long reports. For example, asking: “What were the highest shrink products in dairy last week?”
Planogram compliance: Generative AI algorithms can analyze images or video footage of store shelves to identify deviations from intended planogram layouts and alert store associates or managers.
Labor scheduling and on-demand workforce services: Optimized labor scheduling, including an “Ask Generative AI” function for self-service for associates to look up schedules, swap shifts, and more.
Store layout planning: Generative AI analyzes customer traffic patterns, product popularity, and sales data to generate optimized layouts and create visual simulations of their effectiveness.
Generative AI use cases for inventory management
Out-of-stock management: Stores can use AI and augmented-reality applications to quickly and remotely gauge what needs restocking, reducing trips between the aisles and the backroom.
Loss prevention: Generative AI can detect anomalies in sales and inventory data, analyzing video surveillance footage for suspicious behavior and predict potential theft and fraud.
Waste reduction and menu creation: Generative AI can generate menus for in-store cafes or delis based on customer preferences, seasonal offerings, and items that are at risk of being wasted.
Onboarding and training: A virtual generative AI trainer for onboarding of new associates, offering adaptive personalized learning for all employees leveraging policies, simulations, virtual reality, and assessments.
Unlocking cost efficiency and sales growth with generative AI
The incorporation of generative AI into store operations unlocks significant potential for cost efficiency and sales growth. By strategically launching high-impact generative AI solutions and democratizing generative AI across the workforce, retailers can rapidly learn from and adopt this transformative technology. The journey with generative AI is just beginning, and for those willing to embrace it, impact on store productivity, customer service, and sales growth can be generated rapidly.
