By using more intuitive, natural interfaces to knowledge and information, retailers can start addressing some of retail’s age-old challenges—like finding and retaining the best talent, getting them up to speed quickly, and simplifying store operations so associates can focus on delighting and assisting shoppers.

No matter the size of the retailer, choosing which AI technologies to prioritize and where to start can be challenging. However, there are many ways retailers are now using AI to deliver measurable value and real return on investment (ROI). Research shows that for every $1 a company invests in generative AI, the ROI is 3.7 times across industries and regions (compared to 3.5 times in 2024).¹ Top leaders using generative AI are realizing significantly higher returns, with an average ROI of $10.30—nearly three times more.¹

To build a foundation for AI success, focus on your business strategy—how AI supports your business goals. Start by identifying the business outcomes you’re aiming for and how AI can help you achieve them.

Here’s a glimpse into how you can start making gains with your AI investments today by focusing on store operations and the frontline.

The frontline is first in line with AI

As the face of retail, frontline workers play a crucial role in the shopper experience. According to recent research by McKinsey, there is a strong relationship between the employee and customer experience, as empowered employees are more likely to deliver superior customer service.² Yet many frontline workers spend too much time searching for information, and this is one of the top five reported obstacles to their productivity.³

Generative AI offers significant potential for enhancing frontline productivity and wellbeing, with evidence that most frontline workers think it could help, and they would be comfortable using AI for administrative tasks.³ Generative AI can automate routine tasks, allowing associates to engage more with customers. This shift can lead to a more stimulating work environment, which leads to higher job satisfaction and can help retailers combat ongoing challenges with employee turnover, seasonal hiring, and training.

At a more macro level, generative AI can also allow retailers to continuously learn and feed insights back into their business processes and to grow their products, services, and competitive differentiation. Retailers can do that by identifying patterns in recurring employee questions so they can get to the root cause of operational challenges and address key gaps in training and store processes.

Here are some other ways retailers are using generative AI today:

• Swedish retailer Lindex created Lindex Copilot to offer tailored support to store associates and better understand store needs. Generative AI facilitates this bidirectional learning.
• MediaMarktSaturn lets associates to have voice conversations with generative AI, accessing details for every product, service, and warranty while staying engaged with the in-store customer, maximizing conversion and increasing customer satisfaction—all while wearing an earbud.
• Store associates at gourmet chocolatier Venchi use detailed product knowledge and customer insights to address the diverse chocolate preferences of shoppers, achieving a customer satisfaction score of 4.9 out of 5.

While generative AI technologies are still relatively new, these examples offer a glimpse of what’s possible, and help retailers build an AI foundation for more powerful capabilities emerging with agentic AI.

Agents are revolutionizing retail operations

Investing in generative AI is crucial for retailers looking to reinvent customer engagement, empower store leadership and employees, and stay competitive—and now that opportunity has skyrocketed with agents.

Agents use AI to automate and execute business processes, working alongside or on behalf of a person, team, or organization. Now retailers can leverage agents to help their teams work more efficiently and effectively by giving them faster access to information so they can better support customers and be more productive.

Agents vary in levels of complexity and capabilities depending on the need. Agents can help frontline workers with a variety of time-saving tasks—from quickly surfacing real-time product information or details about store policies and procedures to support Q&A or troubleshooting. In addition to helping speed information retrieval, agents can help frontline workers with more advanced features like automated task creation or even advising and summarizing information—such as listing open tasks for a shift handover or flagging missed communications. Agents can also operate independently to dynamically plan, orchestrate other agents, and learn to improve over time. For example, an automated stock transfer agent might scan sales velocity across multiple stores and automatically transfer goods between locations if one store is oversupplied while another is understocked, minimizing manual intervention.

Find in-the-moment answers fast

One important way to get business value from agents is to help store associates find information about company policies or procedures when a customer is waiting for an answer.

SharePoint agents can help store associates find quick answers from internal company sources in seconds. Using the power of natural language, associates simply ask what they’re looking for on their tablet or mobile device and the agent responds in natural language with a link to the policy documentation for reference.

These agents go beyond information retrieval to also generate step-by-step instructions, synthesize product information, and support frontline managers to create and smart-assign shifts, and auto-validate task completion.

Agents can help associates reduce customer wait time, increase information accuracy, and possibly facilitate sales.

Simplify store processes

Complex business processes are another ongoing operational challenge and opportunity for custom agents to help improve productivity.

Custom-built agents can help retailers connect to external data sources and systems so store associates can find information such as product inventory availability in or near their store, shipping status, or how to initiate a return.

Frontline workers simply ask, “Help me initiate a return,” and the agent guides them through the process by clarifying the worker’s intent and providing them with next steps, all through a chat interface.

Custom agents are best suited to also streamline complex workflows like task management, that often involves multiple steps. Using custom agents built with Microsoft Copilot Studio, frontline workers can easily create a task and send it through a task management system that sends automatic alerts as needed, all from a single pane of glass.

Meeting you where you are on your AI journey

Microsoft offers AI solutions that you can customize to meet your unique needs and scale. There are several ways agents can be deployed, from no code to low code and pro code. Here are a couple options available today.

Microsoft 365 Copilot Chat is a new offering that adds pay-as-you-go agents to our existing free chat experience for Microsoft 365 commercial customers. Copilot Chat empowers retailers to get started on their AI journey today and includes querying the public web (such as a retailer’s website) for free. To enhance Copilot Chat, retailers can also build custom agents using Copilot Studio and SharePoint agents that enable access to retail systems such as enterprise resource planning (ERP), customer relationship management (CRM), and product information management (PIM), and to documents on SharePoint. These paid agents are available on a metered basis, so you only pay for what you use.

Store Operations Agent is a pre-built agent available on Copilot Studio enabling retailers to get started fast with a prebuilt solution that acts as an “associate” to your store associate. With this agent, retail employees can:

• Access data from LOB systems: Look up product inventory, check order status, find customer information and compare products.
• Access store policies and procedures: Quickly find answers to questions from knowledge bases such as SharePoint, websites, and across select internet portals.
• Raise incidents for quick resolution: Connect to incident management tools by using more than 1,000 connectors in Power Platform to raise incidents and alert store teams.

Using Store Operations Agent, employees at leading Nordic retailer Kappahl can quickly and securely surface product information, store policies and procedures, and more, increasing store associate productivity and upleveling the shopping experience for customers.

A new era of retail fueled by AI, powered by people

The range of potential gains with AI extends across retail operations—from people to processes to customers, helping make retail more human at every step of the way. From delighting shoppers to helping associates feel more supported and productive, AI can boost store operations efficiency, creating an environment where both shoppers and workers thrive.

Savings achieved using AI can be reinvested to create a better employee experience, fostering a work environment where employees are enthusiastic ambassadors of the brand, bringing the life of the store to customers every day.

Microsoft is the proven leader for AI transformation with the full technology stack and portfolio to help retail and consumer goods organizations power their business with AI. We can help you assess your agent environment, ideate on agent use cases, and establish success criteria for evaluating ROI so you can decide what agent is best for you.

Learn more

Learn more about how these forward-thinking companies are driving ROI with Microsoft 365 Copilot and agents—and illuminating the path ahead for every organization.

• Microsoft’s responsible AI principles
• How CMOs can personalize marketing and optimize spend with AI
• How merchants can drive velocity with AI
• Enhancing supply chain efficiency in the retail and consumer goods industry with agentic systems
• How unifying data improves shopper experiences

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¹ Generative AI delivering substantial ROI to businesses integrating the technology across operations: Microsoft-sponsored IDC report – Middle East & Africa News Center.

² How retailers can build and retain a strong frontline workforce in 2024.

³ Work Trend Index: Will AI Fix Work?

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1. AI makes retail more human—not less 

AI makes retail more human not less, creating engagement that delights consumers and empowers employees to solve problems, and deliver memorable customer experiences. At NRF, it truly hit home that “workforce” now includes both humans and their AI companions.

We learned how a beauty and wellness consumer goods brand uses digital twin assets to spin up a targeted lipstick Instagram campaign in minutes, then test and launch it—all of which would have taken days previously. In another presentation, we saw how a fashion retailer connects physical with digital, using ambient intelligence, in-store RFID signals, and AI to help associates locate garments and make recommendations for customers.  

“Can you repeat that, please?” The MediaMarktSaturn’s MyBuddy voice-navigated in-ear AI agent enables store associates to “shop” the store catalog for the best products and services while in the middle of customer conversation. This solution not only boosts customer satisfaction, it also helps associates make the most of conversion opportunities at the point of sale while delighting consumers. Do you want the expertise of a seasoned employee in minutes? Associates at Nordic retailer Kappahl can now use Store Operations Agent to gain access to product information, store policies, and procedures in mere seconds.

AI Is Already Changing Work—Microsoft Included gets at the heart of the changes brought about by AI. “The key to getting a real return on your AI investment is a human-centered approach, enabling individuals to leverage these tools in service to their work.”

2. The frontline is first in line with AI 

The frontline is first in line with AI. It’s very exciting to see how technology empowers people on the frontlines of retail, allowing them to thrive, grow, and be the best brand ambassadors to customers. At NRF 2025, we heard how a grocer employs generative AI to aid in training its 275,000 frontline associates while another automates responses to common customer inquiries and translates training materials to make its top employees available for more complex customer inquiries. A fashion retailer provides their employees with their app, which addresses queries like, “The register is slow. What should I do?”  

Curious about the hottest topics store associates are buzzing about? Swedish retailer Lindex designed Lindex Copilot to not only provide tailored guidance and support to store associates but also to learn and understand better what stores need. Generative AI is great for creating a bidirectional learning highway. Got a sweet tooth? Store associates at gourmet chocolatier Venchi are empowered by both hyper detail about the product and customer insights to meet the varied chocolate needs of shoppers, achieving customer satisfaction of 4.9 out of 5.

The best part is that savings achieved using AI can be reinvested to create a better employee experience, fostering a work environment where employees are enthusiastic ambassadors of the brand, bringing life to the store and to the customers who walk in every single day. 

3. Gen Zalpha defines the post-omnichannel world 

Gen Zalpha defines the post-omnichannel world, where authenticity, discernment, and relatability rule. Zalphas—a portmanteau of Gen Z and Alpha—use social commerce, participate in live commerce, and watch influencers via video storytelling to engage with products, brands, and creators.  

Gamified experiences and blended digital and physical worlds are critical for these consumers. Virtual shopping through gaming platforms blends digital and physical realms. You can find Zalphas creating avatars for themselves and taking their avatars shopping, buying virtual t-shirts for $13, and acquiring virtual manicured nails—in the way that everyday gamers make in-store purchases of speedier cars or accessories intended for game play. One retailer launched its real-life apparel line based on the popularity of the digital apparel collection they had created for avatar shoppers—the clothes were virtual-first. The opportunities for collaboration with technology companies, influencers, and communities are endless, as is the opportunity between virtual and real worlds.  

“Do I need winter or all-season tires?” Recognizing that up to 80% of tire purchases start with a visit to a retailer’s website with customers searching for tires, Canadian Tire’s conversational commerce helps them identify the right tires early on, growing conversion and brand loyalty. A beauty retailer shared at NRF how customers that order online and pick up in store drive the highest average order value (AOV).

“What backpack should I get for my kindergartener?” Customers using the voice ordering and text-to-shop features in the Walmart app can now interact with human-like responses as they search for items, place orders, and schedule pickups or deliveries.   

“What kind of pruning shears do I need for my climbing roses?” Gardens Alive is transforming customer service, enabling unique customer experiences by answering inquiries, tailoring recommendations, and creating seamless self- and assisted-service experiences that win customer loyalty while simplifying operations.

Retailers are working to improve how they track and connect online and offline sales to identify insights, find directly profitable use cases for AI, and reestablish attribution metrics to fully capture this interconnected online and offline relationship—also known as “show me the money.” 

4. The physical store is back 

The physical store is back, enchanting customers with memorable experiences aligned with their purpose. With stores still driving over 80% of sales, retailers are finding new ways to ensure the store expresses their brand’s values through new experiences.¹  

An outdoor retailer offers jacket repairs to emphasize the timelessness and durability of their products, a jeans shop creates the perfect fit with an in-store tailor, a toy store hosts in-store magic shows, a sports retailer debuts a community-style trying area to capitalize on the community built around sneaker culture, and a pet retailer offers self-service dog wash stations. Each of these experiences require, you know it, humans!

In addition, retailers are launching store tech innovations like virtual try-ons, digital shelves, and mobile checkout (25% of one beauty retailer’s sales). A big box retailer has built 1,700 digital twin models of store layouts. Why? To track in-store customer experiences, buying patterns, and preferences. All to optimize conversion. Do the hammers belong on the top shelf or middle shelf? Do we need eight colors of swimsuits or 12? This year at NRF, we saw an AI digital display interface that sits on top of a real-world shopping cart, displaying promotions and recommendations based on what the customer places in their basket.  

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5. Improving supply chain resilience 

Improving supply chain resilience continues to be in the spotlight. Supply chain discussions centered on using technology to optimize inventory, grow resilience, and decrease the risk and cost of disruptions. Pre-orders and just-in-time production—selling inventory before it physically exists—fundamentally rebalances inventory risk. SPAR Austria Group is using AI-enabled demand forecasting system that achieves 90 percent inventory prediction accuracy. 

Direct-to-consumer models reduce the risk of over-production, mis-forecasting, and physical risks to in-warehouse inventory (fire, flood, theft). By creating digital twins of a distribution center with different layouts and different behavior tracking, brands can pick, pack and ship products smarter and faster. One big box store uses digital twins of their warehouses to model efficiency changes to routing inside the warehouse. If an incident occurs, this approach allows automatic rerouting of warehouse robots to maintain flow and operations.

Leading Australian retailer Coles is expanding its world-leading edge computing platform to connect and manage IoT devices across Coles’ supply chain and into stores to advance Coles’ sustainability goals, improve customer experiences, reduce stock loss, and boost team member productivity and safety. British fast-fashion retailer ASOS uses AI to pull and embed knowledge of trends as they surface, enabling them to pick, curate, and share the right items at the right time with shoppers.

Geopolitical discussions are centered on onshoring manufacturing and sourcing for strategic categories. Producers have been making things wherever it was more efficient, and rebalancing supply chains and manufacturing takes time.  

6. Sustainability, ethical retailing, and circular economy 

Sustainability, ethical retailing, and circular economy remain top of mind as retailers are under pressure to meet 2030 sustainability targets and respond to rising consumer demand for responsible practices. For example, recycling expired products into biofuel and livestock feed. Brands are placing more emphasis on eliminating food waste, traceability, eco-friendly products and packaging, circular supply chain, overall waste reduction, renewable resources, and take-back or resale programs.  

A consumer goods company aligns sustainability with profitability by tracking environmental impacts across its product lifecycle assessing environmental footprints while streamlining operations.

Can we save more than 250,000 kilos of food from going to waste? Grocer Albert Heijn is using generative AI tools to help food arrive with maximum freshness, dynamically discounting prices to encourage shoppers to buy before that food is no longer fresh.

7. Customer psychographics drive true personalization 

Customer psychographics drive true personalization, whether it’s translating foot traffic data into a full snapshot of the customer journey or driving conversion in a post-cookies world.  

One of the biggest opportunities brands have is to use technology to understand their customers at their core: what makes them tick, what makes them buy, what causes them not to buy. The customer is smart, and authenticity is key to personalization. I love how a sports retail startup designs running clothes for runners, by runners, tapping into the community and speaking directly to Gen Zalpha’s desire for authenticity and connection.

“What shoes do I want for the holidays?” Saucony optimized customer acquisition during Black Friday and Cyber Monday via a custom AI model developed in partnership with Yobi, leading to a strong surge in new customers in just a matter of weeks. Are active women looking for waterproof mascara or color-correcting powder? The Estée Lauder Companies’ AI-powered custom agent connects and streamlines information for marketers, helping turn insights into action based on data that spans its 25 brands and hundreds of regions. 

Brands need to get personal in a meaningful way. Life shouldn’t be about shopping. Shopping should be about life.

Better shopping experiences through human–technology partnerships 

From AI to sustainability, NRF 2025 was a whirlwind of innovation and insight. As we move forward, the key takeaway is clear: retail is about people—both customers and employees—and the tech that makes those connections meaningful. Here’s to a future where technology and humanity work hand in hand to make the shopping experience better than ever.

Learn more

• The AI Advantage: How retailers are shaping customer experiences with data-driven insights
• Microsoft Cloud for Retail

¹ MIT Sloan School of Management, 2024.

The post 7 retail trends to watch this year from NRF 2025: Retail’s Big Show appeared first on The Microsoft Cloud Blog.

Consumer goods decision-making has become so complex that human talent alone is not sufficient. In order to scale, every data point must be digitized, analyzed, and put to work through AI and machine learning, to identify trends and patterns, and tell us what to do. Predictive analytics is at the foundation of proactive productivity, business agility, and market share growth.  

We are at the cusp of a huge shift in what it truly means to be a digital business. A digital business where you use technology to change how and why you operate, not merely leveraging it to optimize your existing processes. In the consumer goods industry, we spent more than a decade getting our data estate in order and hired data scientists en masse to help us make sense of it. Now we truly know why we were doing all the arduous work.  

AI turns data into shareholder value 

The volatility of macro events in recent years—and their accompanying challenges and disruptions—have had a serious impact on the retail industry and consumer behavior. Today’s consumers are driving the revolution of retail with new expectations in terms of experience and service. Consumer goods organizations are closely monitoring and predicting customer behavior to ensure their offerings are aligned today and tomorrow. There is so much data and it is changing so quickly that finding patterns and insights and putting them to work in a timely fashion is not possible without AI. While quick access to actionable data insights is key to understanding fast-changing consumer needs that enable better demand prediction and forecasting, the ability to insert those insights into your business processes in a timely manner is what will drive business and shareholder value.  

Digital ecosystems for greater transparency, traceability, and agility 

Consumer purchasing experiences are only as good as the retailer or brand’s ability to deliver the right product, at the right place, and at the right time so consumers can happily discover, fall in love, and purchase it repeatedly whenever and however they desire. The expectation of a seamless purchasing experience across multiple channels and shortened delivery times at little or no cost creates enormous supply chain challenges. We are not saying anything earth-shattering when we highlight once again that relying on historical models is not, and certainly will not, be enough to build necessary resilience and agility into supply chains. We must leverage AI to be predictive to proactively detect opportunities and risks across the entire value chain all the way from idea to design production, to the point of sale, and finally to the experience of the product itself. Retailers and consumer goods organizations must adopt a digital-first mindset, shifting the paradigm from a reactive way of doing business to one of long-term planning to sense, predict, and adapt to disruptions—preventing stockouts, missed sales, and avoiding overstocking. 

The complexity of forecasting demand amid market fluctuations has highlighted the need to shift from a traditional cost-driven supply chain based on siloed networks to a customer-centric supply chain of services, which allows synergies between channels and collaborative data sharing. An interconnected digital ecosystem across an end-to-end supply chain network is critical to bringing data together in one place with a holistic planning and logistic system for improved collaboration. Connected end-to-end visibility and collaboration across the supply chain network can prepare for and mitigate potential disruptions. Optimizing stock levels across all selling channels, tracking inventory from manufacturers to warehouses to transit route to point of sale, calculating shipping time for that inventory, and promising accurate delivery time to customers requires multi-tier visibility and collaboration. Compiling data in one place with updates in real-time enables the insight, control, and management needed for greater flexibility, transparency, and traceability.  

Data sharing between retailers and consumer goods vendors has not been optimal. Everyone protects their gold mine of data, and they should—data monetization is a business strategy, not a data strategy. However, retailers and consumer goods brands must find a way to work better together to both share the data and protect it so all parties can benefit. It is the ability to share information in real-time and orchestrate responses to risks and changes, in demand to ensure they are placing the inventory in the supply network at the right place and time. End-to-end visibility is a business imperative for better collaboration with suppliers for on-time fulfillment and the ability to anticipate fluctuations in consumer demand as well as bottlenecks in supply in terms of inventory and freight. Consumer goods companies and retail organizations need to find the correct balance of sharing data to improve demand planning and growth management. 

Generative AI to predict and remediate risks with actionable insights 

Supply chains have mostly been assiduously designed to be as lean as possible. That is no longer imperative. You must optimize supply chain through enabling true collaboration and using generative AI to mitigate disruptions, produce actionable insights, and orchestrate business processes to act on those insights in an automated way. Applied throughout the supply chain to improve inventory positioning, on-time delivery, accurate order fulfillment, convenient returns, and to reduce stock-outs, this orchestration will improve consumers’ experiences and help to ensure their brand loyalty.

Microsoft Dynamics 365 AI Copilot proactively alerts supply planners to risks and mitigation strategies and the best course of action: inventory restocking, inventory placement, demand shaping, and improving lead-time estimates. Predictive insights identify impacted orders, while Dynamics 365 Copilot helps act on these insights with contextualized email drafts. Now supply chain personnel can collaborate with impacted suppliers in real-time to quickly identify new estimated times of arrival and reroute purchase orders based on weather disruptions or geopolitical tensions. Dynamics 365 Copilot helps to identify reliance on suppliers in shock-prone regions leveraging external signals to predict and remediate external risks, to feed back into planning systems and improve demand forecasting accuracy. 

Know your customer  

The volatility of consumer demand, and the increasingly complex path to purchase, combined with the continuous wave of disruptions affecting supply chain logistics (commodity and component pricing) make demand forecasting incredibly challenging. With our Smart Store Analytics solution, we’re providing retailers with e-commerce-level shopper analytics for the physical space. Microsoft’s partnership with AiFi—the world’s most broadly developed computer vision-powered store operator—provides check-out free solutions and also delivers actionable insights on AiFi smart store data with predictive models that optimize store layout and product recommendations—shelf placement and inventory—but also informs marketing and trade promotions to move inventory more efficiently through the stores. AiFi powers autonomous stores at stadiums, convenience, and grocery stores using AI to enable shoppers to check out without waiting in line to pay. 

The multiple ways customers and consumers interact with brands and retailers—gathering data at each of those touchpoints, and gaining insights to improve their experience—allows brands and retailers to strengthen their relationship with consumers through collaborative data sharing using AI to provide accurate suggestions and recommendations enhancing the customer experience and deepening brand loyalty.  

Sustainability 

Consumers—more environmentally conscious than ever before—are the driving force behind a “greener” future. They want to shop from retail and consumer goods organizations that are transparent and sustainable.  

There is a growing role of data and AI in operationalizing sustainability efforts in terms of reducing costs while gaining greater resilience and efficiency in reducing environmental impacts. Using data to operationalize sustainability will reduce costs and drive efficiencies. Businesses are also choosing to extend their mission beyond shareholder value to encompass broader ecological and societal issues.¹  Investing in next-generation demand planning that leverages AI insights and machine learning capabilities helps improve forecasting accuracy. Gaining analytic agility in planning ensures that supply more precisely matches demand and increases in-store availability by reducing overall inventory levels.  

Digital is business  

AI is a game changer. At every level of your business, investing in data and AI should be the highest priority to improve net margin, free up working capital, improve customer satisfaction, anticipate changing demand to maximize revenue, manage costs and improve efficiencies to protect margins, and optimize end-to-end networks to balance inventory and service.  

So how do you decide where to start? The first step is to identify the type of data you want to collect. Remember—data monetization is not a tech strategy, it is a business strategy. The next step is to assess what technology and tools you have in place to gather that data. From there you can investigate the technology and AI options that will get the results you need. 

Learn more 

• Read our blog “Digital Imperatives for Market Tectonics in the world of Consumer Goods” and how leading with customer centricity can drive your engine for change. 
• Consumer Goods Technology’s recent article highlights how consumer goods companies are using OpenAI’s GPT-4 language model for consumer goods business intelligence discovery.
• Visit Microsoft for the consumer goods industry for more solutions. 

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¹ “Perspectives, The future of the consumer industry, Buying into Better^TM,” Deloitte, 2023.

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