It’s the day before a friend’s birthday. You need a gift under 50 dollars that arrives tomorrow. Instead of opening a dozen tabs, you ask an AI assistant. In a single conversation, it understands your context, evaluates constraints, and recommends a choice you can act on with confidence and feel good about giving.

That moment illustrates a shift underway in retail. ¹Bain & Company estimates that 30% to 45% of US consumers already use generative AI to research and compare products. Shopping is increasingly happening within AI-guided conversations, where agents move beyond listing options to interpret intent, evaluate tradeoffs, and guide decisions in real time.

For CMOs, this is not simply another channel to optimize. It represents a fundamental change in how influence is earned and how brands compete for loyalty, margin, and growth.

What is agentic commerce and how is it changing the shopper experience

For years, the front door to retail was a homepage, a search box, or a category page. Growth came from capturing keywords, driving traffic, and optimizing funnels, often measured through last click attribution.

Agentic commerce changes that. The new front door is the conversation.

Instead of navigating pages and filters, shoppers now express intent in natural language: “I need a sustainable gift under fifty dollars for a coworker who loves cooking. It has to arrive by Friday and feel premium, not generic.” The conversation itself becomes discovery, surfacing unexpected options and sparking new ideas. Through continued dialogue, those possibilities sharpen into confident decisions.

What matters is not just what is recommended, but how that recommendation is shaped using context in the moment of decision.

How AI shopping agents generate insights that drive business growth

The following video shows agentic commerce in action. It demonstrates how a single request can generate value both for the shopper and the brand.

A shopper asks for a coat for a trip to Aspen in February. The agent immediately goes to work, considering weather data, the shopper’s style preferences, available inventory, pricing, reviews, and more.

At the same time, that request also produces actionable insights for the brand. The marketer sees intent signals combined with internal retail data and broader market trends. Patterns emerge, showing rising demand for winterwear. An AI agent recommends a targeted promotion, the marketer approves it, and the offer is delivered back to the shopper.

The shopper receives curated recommendations with a timely promotion, makes a confident choice, and heads to the slopes in the right jacket.

This is agentic commerce in action. The conversation delivers value in the moment while simultaneously generating learning that informs future business decisions. This creates a feedback loop that strengthens with every interaction.

What sets this apart from previous shifts in the industry is when influence occurs. It is no longer applied only before a decision through advertising or merchandising. Influence is now shaped during the decision itself, as real-time signals flow between shopper and brand.

What makes the agentic commerce shift different

Retail has gone through major transitions before from physical stores to websites, from desktop to mobile, and from owned commerce to marketplaces. Each wave opened new channels of demand.

Agentic commerce goes further. It changes how decisions themselves are made. It introduces a decision layer between shoppers and brands. Behind every interaction sits a learning layer, where signals flow back to merchants, informing decisions that shape recommendations, promotions, assortments, and experiences.

The scale of this shift is substantial. ²McKinsey projects that by 2030, the US business-to-consumer retail market alone could see up to $1 trillion in orchestrated revenue from agentic commerce, with global projections reaching $3 trillion to $5 trillion. This is not an incremental growth. It represents a reconfiguration of how products are discovered, evaluated, and purchased.

For CMOs, strategy now centers on guiding decisions at the moment of choice and capturing the intelligence those interactions produce. Brands that earn influence in the moment and compound what they learn over time are better positioned to build lasting advantages in pricing, loyalty, and lifetime value.

How to build competitive advantage in agentic commerce

As agentic commerce scales, two imperatives are emerging, and leading brands are pursuing both.

Ensure discoverability wherever shoppers ask

AI assistants such as Microsoft Copilot are becoming common starting points for product discovery. When a shopper asks, “What’s the best running shoe for marathon training?” or “Find a sustainable laptop bag under $200,” AI agents interpret intent and surface recommendations.

To compete in these moments, brands must ensure their products, attributes, and value propositions are accurately represented in AI platforms. Success depends on being discoverable when consumers ask questions, not just when they type keywords into a search bar.

Build owned agentic experiences that capture learning

Discovery on third party platforms creates awareness. Owned conversational experiences create advantage. When brands deploy AI agents on their own properties, they capture the context behind each decision. That intelligence feeds merchandising, pricing, inventory, content strategy, and personalization. Just as importantly, brands can use these insights to enrich product catalogs with the language, attributes, and use cases that improve discoverability on third-party AI platforms, strengthening AEO and GEO performance over time.

Trust plays a critical role here. According to Bain & Company, while consumers increasingly use AI for research, they currently trust brands’ on-site agents three times more than third-party agents. That trust advantage makes owned conversational experiences more effective at driving conversion.

Why the winning move is doing both

The question for CMOs is not whether to participate in agentic commerce. The shift is already underway. The real question is whether your brand will appear only in third-party recommendation engines, or whether it will also own the intelligence that turns interactions into durable advantages in pricing, loyalty, product development, and lifetime value.

Four actions retail leaders can take now to prepare for agentic commerce

Agentic commerce is taking shape, and the path to readiness is clear.

1. Optimize for AI discoverability. Most product data was built for search filters, not AI conversations. Brands need structured attributes, descriptions that reflect real use cases and brand voice, and accurate information for pricing, availability, fulfillment, and policies. This practice, often called AI engine optimization (AEO) or generative engine optimization (GEO), helps ensure AI can accurately represent your products when shoppers ask questions across search engines and conversational platforms.
2. Launch owned conversational experiences to learn fast. Deploying AI shopping assistants on owned surfaces creates a feedback loop. Each interaction and conversion generates insight into customer intent, preferences, and friction points. Marketers can use these insights to continuously improve product catalog data, strengthening AEO and GEO performance across all platforms.
3. Design for openness and portability. Agentic commerce spans websites, apps, stores, partner channels, and third-party platforms. Product intelligence and brand logic need to travel across surfaces so brands can participate in new ecosystems without losing differentiation or starting over each time an interface evolves.
4. Govern measurement so learning compounds into growth. As AI agents mediate more of the shopper journey, brands risk losing visibility into how decisions are made. Clear expectations around signal sharing, experimentation, and insight ownership helps ensure every interaction strengthens the business.

Trust and privacy run across all four moves. Transparent recommendations, responsible data use, and alignment with brand values shape long term loyalty.

A new growth era for retail CMOs

Agentic commerce does not replace marketers or merchants. It elevates their role, bringing intelligence, context, and action closer to the moment of choice. For CMOs, this is a leadership moment. It is an opportunity to shape how customers discover and choose products, turn data into conversational intelligence, and build trust as a durable source of differentiation.

Those who define how shoppers discover, trust, and choose in an AI mediated world will define the next era of growth in retail and consumer goods.

Prepare your brand for agentic commerce

Optimize your product data and brand content so AI agents can accurately represent and recommend your products across platforms. Access our guide on AEO and GEO to advance your business for AI-driven shopping.

Retail

Accelerate retail growth with Microsoft for Retail

Find AI use cases, customer stories, and more resources on Microsoft for Retail page.

Learn more here

¹Bain & Company estimates that 30% to 45% of US consumers already use generative AI to research and compare products.

²McKinsey projects that by 2030, the US business-to-consumer retail market alone could see up to $1 trillion in orchestrated revenue from agentic commerce, with global projections reaching $3 trillion to $5 trillion

The post How agentic commerce is becoming the new front door to retail appeared first on Microsoft Industry Blogs.

Today’s business environment demands more with less. Organizations must deliver better personalization, higher volume, and increasingly complex insights while operating with greater efficiency. The gap between stakeholder expectations and what teams can realistically deliver continues to widen. 

Microsoft’s recent insights on Frontier Transformation address these challenges by embedding AI into the core of operations. Frontier Firms are organizations that treat AI as a foundational capability and are already transforming how they work. 

Explore Microsoft AI Co-Innovation Labs

Frontier Firms don’t simply automate; they adapt. By adding adaptive intelligence to existing systems, they unlock three advantages: 

• Awareness: Systems perceive conditions in real time. 
• Reasoning: They prioritize tasks based on business needs. 
• Interaction: They communicate in natural, intuitive ways. 

Early adopters see small improvements compound quickly. These include faster service, more accurate recommendations, fewer equipment surprises, and clearer insights into peak times and bottlenecks. As agentic AI matures, companies can offer guidance and assistance that feels intuitive. Employees gain more time for high-value work, and leaders gain deeper visibility into operations. 

Frontier Transformation is more than a technology upgrade. It represents a shift in operating model. Organizations that treat AI as a foundation will lead the next wave of business innovation. 

Agentic AI is reshaping customer experience 

This shift is already visible in retail, where agentic robots are transforming customer experience and improving operational performance. Customers expect fast, personalized service, yet retailers often face staffing constraints, training gaps, and unpredictable demand. 

Microsoft for retail

Learn more ↗

Industry studies show: 

• 75% of consumers are more likely to purchase when recommendations feel relevant. 
• Nearly 40% of in-store complaints relate to wait times. 
• Inventory inaccuracies account for 4–8% of lost sales. 

These challenges reflect a broader pattern across frontline-heavy industries. Customer expectations continue to rise, and employee workloads grow more complex. 

Microsoft’s Work Trend Index reinforces this dynamic. Frontline employees say AI tools that reduce repetitive tasks, surface real-time information, and streamline customer interactions have the biggest impact on satisfaction and performance. As organizations integrate adaptive intelligence into daily workflows, these benefits build on each other and help accelerate Frontier Transformation. 

Recent industry research shows that retail and consumer packaged goods organizations are generating significant business value from generative and agentic AI, with early deployments consistently delivering multi-times ROI and accelerating impact across frontline operations.

Agentic AI creates new possibilities for stores. Instead of relying on rigid automation, it blends environmental awareness, adaptive reasoning, and conversational interaction to help teams respond in real time. 

ADAM: From beverage service to customer care 

Richtech Robotics’ ADAM beverage robot illustrates how quickly agentic systems can enhance the customer experience. Richtech, based in Las Vegas, designs and commercializes autonomous robotic solutions for hospitality, retail, logistics, and manufacturing. Through a close, hands-on collaboration between Richtech’s engineering team and the Microsoft AI Co-Innovation Labs, the two companies jointly developed new adaptive intelligence for ADAM—transforming it into a conversational, context-aware assistant powered by Microsoft Azure AI. These enhancements enabled ADAM to move beyond routine beverage preparation and support richer customer interactions.

Today, ADAM: 

• Adjusts recommendations based on weather, time of day, and promotions. 
• Responds naturally to customer requests like “less sweet,” “extra ice,” or “what’s seasonal?” 
• Notifies staff about ingredient or equipment issues before problems occur. 
• Uses vision models to maintain speed and quality during busy periods. 

Retailers report smoother operations and better customer feedback. ADAM is context aware, conversational, and reliable—qualities customers consistently reward and areas where AI has historically struggled. 

While ADAM is a retail example, the pattern extends far beyond beverage automation. Across logistics, healthcare, hospitality, and manufacturing, Frontier Firms are adding ambient intelligence and agentic workflows to physical operations and seeing meaningful gains as a result. 

Unlocking retail transformation at scale 

Once retailers see how intelligence enhances a single customer interaction, the next question naturally follows: where else can this help? Building on the advancements made with ADAM, Richtech Robotics is extending these capabilities through its Agentic Store initiative. By applying vision, voice, and agentic reasoning to common in-store tasks, the initiative helps retailers address friction points that slow down the shopping experience. 

Explore building an AI workforce

Get the e-book ↗

Examples under development include: 

• Robots that guide customers to products.
• Systems that detect empty shelves or misplaced items.
• Voice-enabled, in-aisle assistance.
• Real-time adjustments based on foot traffic or local events.

This approach does not require heavy hardware investments. These workflows are software-driven and build on existing store infrastructure. It reflects how Frontier Firms drive transformation by spreading intelligence across the ecosystem rather than upgrading a single process at a time. 

Retailers gain clearer visibility into peak demand, customer behavior, product movement, and service quality without increasing manual tracking. As one store manager described it, “it feels like having a second set of eyes that never gets tired.” 

Convenient, high-quality service becomes a blueprint for store-wide intelligence. In the coming years, a clear difference will emerge between retailers that treat AI as a tool and those that treat it as a foundation. The latter will set the pace for the industry. 

Steps toward Frontier Transformation 

Agentic AI gives retailers a practical and achievable path forward. It elevates customer experience, reduces operational strain, and creates the foundation for smarter and more adaptive stores. Organizations that embrace Frontier Transformation position themselves as Frontier Firms, ready to scale faster, work more intelligently, and unlock new value through the combination of human judgment and AI-driven insight. 

The journey begins with small, strategic steps and a bold vision for what is possible. To explore the broader business impact of AI across frontline and customer-facing roles, review Microsoft’s Work Trend Index: The year the Frontier Firm is born.

Explore how organizations are transforming with AI, and learn how you can build your own generative AI proof of concept with the Microsoft AI Co-Innovation Labs.

Learn more about Frontier Firms

The post Frontier Transformation in retail: How agentic AI robots are redefining store experiences appeared first on Microsoft Industry Blogs.

At Microsoft, we see retail moving forward into a world where agents strengthen and streamline human action through automation and intelligence on tap. Cutting-edge agentic technology will enable retailers to move faster and smarter. But the emotional connection with the consumer, the trust, creativity, and empathy that define great brands, cannot be automated. Delivering authenticity at scale will rely on a workforce model that consciously centers and invigorates the collaborative human experience—one that sees agents as force multipliers for workers to deploy in pursuit of their best work. As one retail analyst put it, “Technology alone isn’t enough—real transformation requires rethinking how we work within human-plus-agentic teams.”³ Success in the next chapter in retail will accrue to the organizations who maximize the power of these human-agent teams—what we at Microsoft call a Frontier Firm. 

That’s why we believe the agentic future should be human-led, with automation embedded in key workflows and a return on intelligence that helps people move faster and smarter. Retail executives echo this vision: “AI tools can free up front-line employees to prioritize tasks that require a human touch, such as engaging with customers and designing stores.”⁴ Consumers will continue to seek out and reward authentic, down-to-earth experiences, even as they embrace AI for convenience. The retailers who win in 2026 and beyond will be those who combine the best of both worlds: the speed and precision of agents with the creativity, empathy, and expertise only humans can deliver.

How Microsoft agentic solutions are helping move retail forward

We are excited and proud to be leading the evolution of agentic retail with right-sized development options for every organization—whether they’re looking for a fully-configurable, pro-code development experience or a low-code/no-code, fully-managed environment. 

Microsoft Foundry enables professional developers to build, optimize, and govern sophisticated AI solutions that run at the edge or in the cloud. Retailers seeking deep customization benefit from the widest selection of foundation models on any cloud, including models tailored for the retail industry, open agent frameworks, vast integrations, enterprise scalability, and organization-wide observability and controls. For example, Foundry IQ helps organizations securely ground AI apps and agents on enterprise data stored in any location, while Foundry Tools allows agents to automate workflows and respond with real-time precision across more than 1,400 business systems like SAP, Salesforce, ServiceNow, Shopify, Stripe, Adobe, and Dynamics 365. This enables retailers like The ODP Corporation and Bayer to drive business results with action-oriented and context-aware agents as part of their broader application landscape. 

Organizations looking to rapidly prototype conversational commerce and worker empowerment agents will appreciate Microsoft Copilot Studio easy low-code/no-code configurability and straightforward integrations. And for retailers looking for a turnkey experience with minimal time-to-value, we offer a curated group of managed agent templates in Copilot Studio that will help these organizations deploy enterprise-ready AI agents without starting from square one.

Each template comes preconfigured with proven logic, connectors, and workflows backed by rigorous evaluation, model tuning, and quality controls. Retailers can deploy agents with confidence knowing that Microsoft security, compliance, and lifecycle governance are built in. Native integration across Microsoft 365 and Power Platform makes it easy to embed intelligence into existing applications, automate workflows, and streamline data orchestration.

Together, these capabilities reduce implementation time, lower IT overhead, and accelerate time-to-value—quickly moving organizations from pilots to real, scalable outcomes.

Today, we are introducing three agents built specifically for retail: one for product discovery, one for catalog enrichment, and one for store operations. Each is designed to help retailers tame complexity, improve data quality, and deliver higher-value customer and employee experiences.

Personalize retail journeys with the personalized shopping agent

The personalized shopping agent template serves as a digital expert associate available across retail channels. It goes beyond basic search-and-scroll by delivering guided, natural language product discovery that helps the shopper feel like a knowledgeable store associate is assisting them.

Instead of returning loosely related results, the agent asks clarifying questions, interprets nuance, and offers informed recommendations that reflect real customer needs. Retailers can tune the agent’s voice, language, and style to match their brand, ensuring every interaction reinforces the retailer’s identity and expertise.

Whether customers are searching for a fragrance that evokes a memory, planning an outfit for a themed event, or finding the right running shoes for an upcoming marathon, the agent engages in thoughtful conversation to guide decision making. Built on Microsoft Foundry’s enterprise grade AI stack—including Azure OpenAI in Foundry Models, Azure Machine Learning prompt flow, and Microsoft Fabric—the agent delivers personalized discovery at scale while remaining easy to integrate across web, mobile, and in-store experiences.

Its headless and tailless architecture allows retailers to pull from multiple systems and maintain a consistent, branded experience anywhere it is deployed. Cross-sell and upsell intelligence, financing suggestions, and even in-home service recommendations help retailers drive higher conversion and customer satisfaction.

Leading retailers are already putting this to work. Ralph Lauren’s “Ask Ralph” virtual stylist runs on this agent template, with comprehensive knowledge of the brand’s products, design philosophy, craftsmanship, and history, emulating the immersive experience of a Ralph Lauren flagship store.

Catalog enrichment: From messy product data to meaningful content

For more than three decades, retailers optimized their product catalogs for three-word search terms. But conversational search shines when it can query catalogs full of detailed, multi-dimensional product attributes. Retailers eager to surface the perfect personalized product suggestions to their customers can quickly improve the accuracy and relevance of conversational search results by augmenting their catalogs with a rich set of attributes for each product.

Modernizing and enriching product descriptions is no small task, however. Even if a retailer could resolve all the gaps and inconsistencies in their legacy product catalogs, manual data entry, inconsistent vendor information, and the scale of product refresh cycles promise ongoing operational friction and disappointing conversational search results.

The catalog enrichment agent template solves this by automatically cleaning, completing, and standardizing product information. Designed for simplicity and accessibility, the configured agent integrates directly into familiar environments like Microsoft Teams and Microsoft 365 Copilot Chat, where merchandisers can review flagged items, approve updates, and act quickly as product offerings change.

Because the agent does not rely on a fixed schema or centralized data source, it can ingest product details from images, PDFs, structured tables, or unstructured documents. It then transforms that information into clean, brand-aligned catalog entries. The agent can work independently or alongside existing systems, giving retailers a flexible, modular way to improve data quality without large-scale re-platforming.

Once data is ingested, the agent extracts key attributes, applies brand guidelines, aligns content to the retailer’s taxonomy, and generates consistent product descriptions. It can process thousands of products—flagging low-confidence entries and producing review-ready outputs—before teams even start their day.

As merchandisers provide corrections, the agent learns and improves over time. Whether operating in auto approval mode or with human review, it dramatically reduces manual workload and improves the accuracy of product data. The result is better product discovery, higher customer confidence, and more scalable catalog operations.

“With Microsoft’s catalog enrichment template forming the backbone of our personalized shopping experiences, we can turn product details into meaningful insights that help shoppers discover styles in real time, receive tailored recommendations, and explore complete looks. It’s a powerful step forward in our commitment to delivering service that’s as dynamic our brand.”  

David Torrecilla, Head of Innovation at Guess

Store operations: Turning real world signals into real time action

The store operations agent template is a prebuilt, low-code solution that converts real-world signals into timely, actionable guidance for every store. It continuously monitors external factors—weather, events, holidays, and seasonality—and triggers operational recommendations when conditions change.

Store managers can review, approve, or refine these suggestions, and the agent improves with each piece of feedback. Once approved, the agent orchestrates execution through Microsoft Teams Planner, creating and assigning tasks with clear instructions, due times, and progress tracking. This keeps teams aligned, improves task compliance, and shortens the signal-to-action cycle.

The agent includes integration stubs and templates so retailers can connect to inventory, shipping, workforce planning, and other systems. It can be tailored to specific policies, playbooks, KPIs, and regional requirements, enabling day-to-day operational precision.

Strandbags, the largest specialty luggage and accessories retailer in Australia and New Zealand, is one of the early adopters. Their teams are using the store operations agent template to act more quickly on local insights and stay ahead in a dynamic retail environment.

Murdoch’s Ranch & Home Supply is also deploying the store operations agent template in locations across six U.S. states.

 “Beyond efficiently aggregating data, the agent provides valuable insights into community events, sales trends, and actionable recommendations for improving store performance. A key aspect of this agent is its integration into our ecosystem, allowing us to continuously refine its capabilities and enhance the value it delivers.” 

Steven Potratz, Senior Learning and Development Leader at Murdoch’s

Shirley Gao, Chief Digital and Information Officer of PacSun, shared that the objective of the store operations agent template deployment underway in PacSun’s retail locations is “to embed AI at the core of store operations, equipping associates with predictive intelligence, actionable workflows, and real-time business alerts. This enhances operational visibility and accelerates data-driven decision-making at the store level. Through continuous co-creation and adaptation to our unique processes, we ensure AI delivers meaningful impact for both our business and our people.” 

Transforming retail end-to-end

Every product, every collection, and every campaign is part of a broader narrative retailers use to connect with customers. But delivering those experiences consistently requires precision, context, and agility.

Microsoft’s retail agent templates are designed to make that possible.

• The personalized shopping agent template turns product discovery into a tailored conversation.
• The catalog enrichment agent template ensures foundational data is accurate, complete, and aligned to the brand.
• The store operations agent template transforms signals into action and orchestrates execution at scale.

Together, they form a connected ecosystem that supports the full arc of retail storytelling—from data to discovery to delivery. By pairing agentic automation with human judgment, these solutions help retailers scale their brand, delight customers, and achieve greater return on intelligence (ROI).

All three agents are available today in Microsoft Copilot Studio and Microsoft Marketplace.

Retail is entering a new era—one built on intelligence, speed, and empowered teams. I’m excited to partner with you as we take the next step forward.

Join me on Monday, Jan 12, 2026 on the Big Ideas stage at NRF 2026 for Becoming a Frontier Firm: Unlocking the New ROI—Return on Intelligence as I explore the journey to an agentic future. 

Hear from Kathleen Mitford, Microsoft Corporate Vice President of Industry Marketing in conversation with Adobe and The Coca-Cola Company about the future of retail growth on the Big Ideas stage. Don’t miss Personalization at Scale: Using AI to unlock the next phase of growth on Sunday, January 11, 2026.  

Enjoy an illuminating fireside chat, The future of an icon: Ralph Lauren’s journey of heritage, innovation and partnership with Microsoft on the keynote stage on Monday, January 12, 2026. Featuring Shelley Bransten, Microsoft Corporate Vice President of Worldwide Industry Solutions and David Lauren, Chief Branding and Innovation Officer at Ralph Lauren, this session will explore how iconic brand heritage and digital creativity converge to shape the future of luxury retail. 

Interested to learn how you can jump-start your agentic future? Visit us at Booth #4503 to experience:

• AI-powered shopping journeys.
• Intelligent merchandising and catalog enrichment.
• Dynamic supply chain orchestration.
• AI-assisted workforce solutions.
• Customer stories across throughout in-booth demos and theater that showcase the ROI of AI and agents today.

Explore solutions and more

• Find out how you can start deploying agents across your organization with Microsoft Agent Factory. 
• Learn how agentic AI and Microsoft Dynamics 365 are helping organizations shape the future of retail.
• See Forrester’s Predictions 2026: Digital Commerce.
• Discover how your organization can become a Frontier Firm at a Microsoft AI Tour event near you.
• Explore key retail AI use cases, customer stories, partner solutions, and more on the Microsoft for Retail homepage.
• Follow Microsoft Cloud on LinkedIn.
• Read about how Microsoft is setting the standard for responsible AI practices.

¹ Morgan Stanley, Here Come the Shopping Bots, December 8, 2025.

² Adobe, Adobe: Cyber Monday Hits Record $14.25 Billion in Online Spending with Over $1 Billion Driven by Buy Now Pay Later, December 2, 2025.

³ Slalom, Industry Outlook, Retail Industry Trends 2026.

⁴ Snowflake, The AI Tipping Point: What Retail Leaders Need to Know for 2025, February 4, 2025.

The post Return on intelligence: The human edge in an agentic era appeared first on Microsoft Industry Blogs.

“Gartner^® predicts that 40% of agentic AI projects will be cancelled by 2027.”¹ While AI technology is transformative, adoption alone does not guarantee desired results. It’s important to have a plan that meets your organization’s unique needs, goals, and capabilities.

Studies show how finding the right strategic lever for AI is becoming table stakes for retail organizations. By 2030, personal AI shopper agents could influence over half of global consumer spending, having a massive effect on marketing strategies.² Retailers who fail to adapt, risk being left behind.

How do we resolve this paradox? The answer lies in specificity. Success depends on understanding where AI agents can drive impact in retail and consumer goods organizations, mapping innovative opportunities to the most pressing challenges, and measuring results with rigor. Starting with clear use cases tied to real business outcomes. This is how small proof points evolve into large cross-organizational impact.

The new demands of retail and consumer goods marketers

On the customer-facing side of retail and consumer goods, the pressure to deliver is intense. Chief marketing officers (CMOs), loyalty leaders, and customer experience executives are asked to orchestrate hyper-personalized campaigns while also delivering seamless support throughout the customer journey. Communications, pricing, promotions, placement (brand engagement), post-purchase care—each of these touchpoints require speed, consistency, and delight. Yet in many organizations, insights are fragmented, campaign cycles are slow, and service costs are rising.

This is where agentic AI can create a flywheel. Consider marketing campaigns. With AI analyzing consumer data for insights, generating creative content variations, and orchestrating campaigns, marketing executives can move from static plans to dynamic, always-on engagement. These same systems can feed reports to marketers managing campaign effectiveness, closing the loop between insights and agility.

With the agility offered by AI, other customer-facing roles are also enabled. Customer service leaders are empowered with insights on customers who have interacted with the brand, and frontline workers are empowered with faster time to knowledge and service.

Retailers such as Albert Heijn, featured in our new e-book, show how forward-thinking retailers are already deploying AI on the store floor, to help employees serve customers faster and more effectively.

Operations as a growth driver

If marketing and customer service comprise the front face of retail, operations and merchandising are its backbone. A delayed shipment, a stockout, a mistimed promotion aren’t operational issues; they’re revenue leaks.

AI agents reframe operations, from support to strategy. For chief operating officers (COOs) and supply chain and logistics leaders, AI agents can forecast demand, sense disruptions, and adjust supply chains before problems escalate. This goes beyond efficiency into protection of revenue, risk management, and brand trust. For merchandising executives, AI agent capabilities enable localized assortments, dynamic pricing, and promotion planning that adjusts in near real-time. What once took weeks of manual coordination can now be automated to maximize sell-through and reduce carrying costs.

The cumulative effects are profound. Agentic AI brings agility to the functions that keep retail running, turning them into engines of competitive differentiation. This example from Pets at Home illustrates how retailers are applying tools to match demand with precision, protect margins, and optimize execution across stores and channels.

Combining your insights to out-innovate at scale

Beyond day-to-day execution, the consumer goods industry faces another pressing challenge: the speed of innovation. Product lifecycles are shrinking. Consumer preferences shift quickly. Data is fragmented and siloed. For research and development (R&D) leaders, this creates inefficiencies that delay launches and increase costs.

AI agents have the potential to rewire this process. By unifying consumer insights, market trends, and operational data, they can accelerate product development cycles and empower collaboration. Manufacturing leaders gain predictive visibility into bottlenecks. Product officers can simulate demand and orchestrate workflows across teams. The net effect is faster time-to-market, lower risk of failed launches, and greater alignment between what consumers want and what companies can deliver.

Estée Lauder used AI to unify datasets and accelerate innovation. It underscores how agentic AI can serve as a catalyst for growth beyond the core of retail operations.

Learn how retail and consumer goods leaders use AI agents

Get the e-book

Where will you pilot agentic AI?

AI agents aren’t a one-size-fits-all answer, but embracing AI agents today will help future-proof your organization and empower functions across your retail and consumer goods businesses. Its greatest impact emerges when part of a broader strategy, deployed against specific challenges, and with clear measures of success. Whether enabling agentic shopping experiences or efficient operations, retail and consumer goods companies that take advantage of marketing, customer service, merchandising, operations, and R&D opportunities to embrace AI can reimagine these functions as growth drivers for the business.

¹Gartner^® Press Release, Gartner Predicts Over 40% of Agentic AI Projects Will Be Canceled by End of 2027, June 25, 2025. https://www.gartner.com/en/newsroom/press-releases/2025-06-25-gartner-predicts-over-40-percent-of-agentic-ai-projects-will-be-canceled-by-end-of-2027

GARTNER is a registered trademark and service mark and IT Symposium/Xpo is a trademark of Gartner, Inc. and/or its affiliates in the U.S. and internationally and are used herein with permission. All rights reserved. 

²Cognizant, Consumers Who Embrace AI Could Drive $4.4 Trillion in Spending Over Five Years, 2025. 

The post How retail and consumer goods leaders empower their workforces with AI agents appeared first on Microsoft Industry Blogs.

It’s also transforming the way we shop. Increasingly, we’re embracing AI shopping tools that more easily help us find products. But that’s just the start of what conversational commerce can do. Just like consumers want in store, online they’re seeking recommendations that reflect their sense of personal style.

Enter Ask Ralph, a new AI-powered styling companion that not only helps with product discovery but also inspires consumers with Ralph Lauren’s unique and iconic take on style.

Ask Ralph: A style companion powered by AI

Ask Ralph is a conversational AI shopping experience built on Azure OpenAI and available in the Ralph Lauren app in the US. You can interact with Ask Ralph just like you would a stylist in a Ralph Lauren store by asking simple, conversational questions or using prompts to find the perfect look for any occasion.

Whether you’re refreshing your wardrobe for fall or wondering what to wear to a concert in the park, Ask Ralph responds with curated, fully stylized, visually displayed, and shoppable outfits from across the Polo Ralph Lauren brand, tailored to your unique prompts.

The delight of conversational commerce

Ask Ralph is part of a broader movement—one where AI doesn’t just assist, it inspires.

Using natural language, Ask Ralph interprets open-ended prompts, asks clarifying questions, and delivers beautifully visualized outfit recommendations that are tailored to your query—all based on Ralph Lauren’s real-time available inventory.

Built for the future, grounded in legacy

For nearly 60 years, Ralph Lauren has been a pioneer in creating transportive and cinematic retail experiences. Twenty-five years ago, Microsoft and Ralph Lauren teamed up to launch one of fashion’s first e-commerce platforms, setting an industry standard—and now, together, we are again redefining the shopping experience with Ask Ralph.

As Naveen Seshadri, Ralph Lauren’s Chief Digital Officer, shared in a recent interview, “At Ralph Lauren, our focus is always on the consumer. We harness innovative technologies to create an elevated, personalized experience that draws customers into Ralph’s iconic world at every interaction. The launch of Ask Ralph is a continuation of that commitment.”

To hear more from Naveen on the vision behind Ask Ralph, watch the Ralph Lauren customer video.

Agentic AI: The new frontier

Ask Ralph is powered by Azure’s agentic AI capabilities—intelligent systems that plan, reason, and act. These agents are transforming retail by enabling immersive, personalized experiences at scale.

“At Ralph Lauren, our focus is always on the consumer. We harness innovative technologies to create an elevated, personalized experience that draws customers into Ralph’s iconic world at every interaction. The launch of Ask Ralph is a continuation of that commitment.”

—Naveen Seshadri, Chief Digital Officer at Ralph Lauren

Confidence, creativity, connection

At its heart, Ask Ralph is about inspiration. It’s about helping people find new ways to express their personal style.

This is just the beginning for Ask Ralph, which will continue to evolve with new features and offerings to offer an even more personalized experience, as well as expand across markets, platforms, and additional Ralph Lauren brands.

Azure AI solutions

Create the future with Azure AI Foundry

Get started

Ready to transform the shopping experience with AI?

With Azure AI, retailers have the power to build immersive, intelligent shopping experiences that scale, adapt, and inspire. Whether you’re looking to personalize customer journeys, optimize inventory, or empower your workforce, Microsoft’s AI platform is ready to help you innovate with confidence.

Join us for an AI.deation workshop to explore how agentic AI can elevate your business—from concept to production. Let’s co-create the future of retail, one conversation at a time.

Learn more

• Read the press release on Microsoft News.  
• Learn more about Ask Ralph and download the app on the Ralph Lauren site.
• Read the full Ralph Lauren customer story.

The post Ask Ralph: Where style meets AI—a new era of conversational commerce  appeared first on Microsoft Industry Blogs.

For consumer packaged goods (CPGs), delivering personalized content across channels requires multiple iterations of product images, constant reshoots, tweaks, packaging design adjustments, and localization by region. This can be all-consuming for creatives, who are rebuilding or recreating imagery constantly to meet the moment.

Imagine if brands could leverage AI digital twins to create and integrate high-quality, personalized product content at scale—simply, cost-effectively, and in a fraction of the time. AI and 3D digital twins make it possible, proving AI investments deliver on reduced time and speed to innovation.

In a recent post, we discussed how AI isn’t just a tool—it’s the foundation for building competitive advantage. Let’s walk through three strategic areas where digital twins offer exceptional outcomes for marketing teams looking to deliver more.

1. Starting with product imagery

According to EMARKETER, content creation will be the top budget priority AI use case for chief marketing officers worldwide. Why? Producing product images today requires brands to spend a massive chunk of their budget to constantly reshoot and edit images. With digital twins, brands have the flexibility and scalability at low cost to create thousands of variants on a single product image, including labels, packaging, and language formats—all within a single file.

AI empowers not only productivity but creativity. Digital twins are hyper-realistic, enabling content managers to easily and endlessly modify or expand on a concept using a 3D product model with a few clicks. Creatives can reallocate time spent in operational “to do’s” to storytelling, strategy, and delivery by channel. Brands can even showcase products in both static and dynamic formats because AI models aren’t limited to one dimension.

Net-net: Digital twins for product images, videos, and interactive experiences simplify content workflows and allow you to:

• Generate endless product images or videos using a single digital twin.
• Refresh imagery for markets or seasons without reshoots.
• Reduce repetitive labor for creatives while shortening production timelines.
• Test creative concepts instantly without adding costs.
• Update visuals across brands seamlessly.

Making it real: Nestlé reduces associated time and cost by 70% with scaling digital twins

Recently, Nestlé—the world’s largest food and beverage company—collaborated with Microsoft, Accenture Song, and NVIDIA to build and launch a new AI-powered in-house service to create high-quality product content at scale.

With its new digital twin content supply chain powered by NVIDIA Omniverse on Microsoft Azure and using Microsoft AI solutions, content creators across Nestlé’s 45 content studios around the world can deliver high-quality creative assets at scale for e-commerce and marketing communications. Nestlé’s Integrated Marketing Services (IMS)—250 marketing experts in seven hubs—are working on scaling the digital twins and driving content localization.

Nestlé already has a baseline of 4,000 3D digital products, mainly for global brands, with the ambition to convert a total of 10,000 products into digital twins in the next two years across global and local brands.

Proving the value of AI investments in digital twins:

• 70% reduced time and cost associated with scaling digital twins.
• Faster content production for several brands, including Purina, Nescafé Dolce Gusto, and Nespresso.
• Better ability to position iconic brands in a fast-moving digital environment.
• Seamless updates for seasonal campaigns or channel-specific formats.

For Nestlé, these technologies are proving to be catalysts for creative ingenuity, revolutionizing creative workflows in design, supercharging content creation, and enabling nuanced personalization—positioning Nestlé at the forefront of marketing.

Learn more from this video about conversations Chief Marketing Officers had with Microsoft at the recent Cannes Lions Festival of Creativity event:

2. Digital twins enable game-changing one-to-one consumer experiences

Digital twins are generating realistic virtual experiences that not only enhance the shopper journey but also hyper-personalize each touchpoint to create memorable brand moments. AI has enabled interoperability between datasets to unlock online configurators, virtual reality product trials and visualizations, and in-store displays.

Net-net: Embedding AI in user experiences is allowing consumer and retail goods companies to enable:

• Try-ons for beauty products and fashion.
• Configurators for custom merchandise.
• Interactive, 360-degree product views.

3. Next level: Media and creative, together at last

The era of AI ushers in a world of “intelligence on tap.”

Imagine if AI-powered digital product twins merge product imagery and consumer insights to create visuals targeted to specific audience segments or even individual customers.

A combination of insights and digital twin content creation empowers marketers to optimize for better impact and even map future trends. The value of building digital twins goes beyond endless product image creation. CPG brands are now leveraging AI to connect real-time campaign insights to their content studios as a primary use case to prove value. Agents are being built to perform audience simulations, test images, content, and even segmentation strategy to drive higher return on ad spend (ROAS) or even predict impact.

Net-net: Use AI to connect media insights and content to:

• Simulate, refine, and test marketing scenarios and consumer responses.
• Increase campaign effectiveness with real-time, iterative feedback.
• Test and optimize personalized marketing strategies at scale.
• Model customer segments and predict campaign outcomes.

AI as a tool to amplify human creativity

As AI continues to evolve traditional processes and enhance productivity, marketers know human creativity remains a critical resource. With digital simulations and AI together, you can reallocate your valuable resources to more strategic, creative tasks; reduce costs and risk; and help your marketing teams optimize spend and focus on your number one KPI: growth.

Learn more

• Read the blogs:
  • How CMOs can personalize marketing and optimize spend with AI
  • More human-centered retail with AI
• Learn more about Nestlé’s end-to-end content supply chain powered by digital twins.
• Read the e-book: Transformative retail: Harness AI innovations to stay ahead.
• Explore Microsoft’s responsible AI principles.

Microsoft Cloud for Retail

Connect your customers, your people, and your data

Explore solutions

The post The next wave of AI for content creation includes digital twins appeared first on Microsoft Industry Blogs.

But, as with every technology, working out where to start is fraught with difficulties. “When all you have is a hammer, everything looks like a nail”—or so the expression goes—but when it comes to business challenges, not every problem warrants an agentic AI approach.

You may have determined candidate areas for agentic AI using a similar approach to that which we described when discussing rapidly ideating on value in a previous blog. However, how do you know if it really warrants an agentic approach, and then, once you’re confident that it does, how do you determine the value it will bring for your organization?

This blog aims to provide guidance on how to address these areas to empower you to make informed decisions and unlock the full potential of agentic AI.

Business and technical criteria

Based on our experience working with retail and consumer goods companies across the globe, there are some common trends that can be considered as criteria for determining if a specific process—or part of a process—is a good use case for agentic AI.

These aren’t considered to be “hard and fast” criteria that must be adhered to—they are merely guidelines.

• Volume. A process with high volumes or number of interactions. For example, a consumer goods company receives many more orders than an aircraft manufacturer, therefore, it’s likely to be far more applicable to apply agentic AI to an order intake process in a consumer goods company. That doesn’t mean that agentic AI cannot help an aircraft manufacturer with this process. It means that the specific process element where it’s applied would be different. For example, in placing an order for an aircraft, multiple detailed configuration documents may be needed, and agentic AI may have a valuable role ensuring those documents are correct.
  
• Interaction. A process that interacts with multiple systems. For example, updates, reads from, or consolidates data between different systems. Processes where users must review, or consolidate, content from multiple systems are prime candidates for the application of agentic AI. Sometimes referred to as “swivel-chair integration,” these types of processes are both tedious and fraught with error.
  
• Human. A process where a high level of human interaction is required. Perhaps involving seeking, reading, considering, and reasoning over multiple pieces of information, documents, or systems. This is typically work that’s mundane and repetitive. Agentic AI can assess and highlight gaps, differences, or anomalies. It can make recommendations to be evaluated by a human and as such, is designed to work alongside or augment the human by reducing the amount of mundane, repetitive activity. The human element is critical here—AI allows the human to focus on exceptions, strategic analysis, and complex decisions while supporting innovation.
  
• Errors. Processes that are error prone—which often occurs with repetitive, mundane human operations. More importantly, one where any errors or issues during the process execution cause adverse downstream consequences such as delayed deliveries, lost sales, compensation claims, or handling by a human that incurs cost or time. This can be a key area of concern and focus.
  

There is an additional requirement, albeit one that must be considered when architecting a solution. This relates to data availability.

It’s critical to ensure that the data required for the agentic AI application is available and accessible without causing challenges elsewhere. It’s common that agentic systems need to refer to data to aid decision-making. For example, it may be necessary to look something up on a customer or supplier master record in a transactional system. Where many of these are required in a very short time, it may be that the agentic solution causes performance issues in the transactional system. Architecturally, this challenge can be avoided by extracting this data into a data lake or other data store to act as a reference location.

Retail Thought Leadership Study

The AI Advantage: How retailers are shaping customer experiences with data-driven insights

Get the report

Defining value

Advancements position agentic AI as a cornerstone for creating a more resilient, efficient, sustainable, and autonomous supply chain. When it comes to evaluating the business value of any technology investment, one of the first points to consider is determining the specific drivers of value. In addition, understanding how you’ll measure this is equally important.

From the work we have done relating to agentic AI, value typically falls into three areas:

1. Productivity. You can think of this as “agentic liberated time.” This reflects reducing the non-value-added time associated with human interaction in a process or process step using the “liberated time” for value-added activities. Scoping these additional activities is critical to delivering value from agentic AI. As an example, one retailer was seeking to free up time for their supply chain planners to spend more time with individual suppliers planning future promotional inventories. AI agents can streamline communications with suppliers, monitor contract compliance, and resolve disputes efficiently.
   
2. Process efficiency. This relates to the elapsed time that a process takes. AI agents automate repetitive tasks and optimize operations leading to higher process efficiency levels and lower costs. This in turn has follow-on benefits—for example, reducing the time spent between receiving and processing a customer order translates to improved customer responsiveness.
   
3. Quality. This can often be seen as cliché. However, in this instance, the focus is the reduction of errors or issues. Specifically, those that have a negative consequence downstream within the organization or supply chain. For example, promising inventory that does not exist will adversely impact customer satisfaction scores and may well result in future lost sales.
   

Measurement is key

For each of these value driver areas it’s important to establish the metrics or KPIs that this is likely to impact in your specific case. The graphic above gives some examples, but this is where the value of agentic AI really comes into force.

For the productivity value driver, liberated time can be used to identify additional revenue generating opportunities, which can enhance your revenue per employee KPI. For process efficiency, reducing lost sales can be a relevant metric if, for example, you’re automating your customer order process.

Quality, however, is where it becomes interesting. Determining the downstream negative consequences of a delayed or misinformed decision can be difficult, but it’s worthwhile. One approach to consider is to use Microsoft Copilot to help ideate on this, asking for suggestions as to what the negative downstream consequences of errors in a particular process might be. This may not yield the exact answer for your business, but practice has shown that it usually inspires a new thought or perspective that relates to your business.

Microsoft Cloud for Retail

Connect your customers, your people, and your data.

Discover solutions

Moving on value

Selecting the right use cases for agentic AI requires a thorough understanding of both the criteria for implementation and the drivers of value. By focusing on high-volume, error-prone processes that require significant human effort and interaction with multiple systems, organizations can identify the most promising areas for AI application.

Additionally, defining and measuring the value of AI investments through productivity, process efficiency, and quality improvements will ensure that organizations can unlock the full potential of agentic AI. With these guidelines, organizations can make informed decisions and navigate the complexities of AI use case selection, ultimately driving innovation and efficiency.

Learn more about agentic AI

• Microsoft’s responsible AI principles
• Retail Ready: Agentic AI built for the future of retail—ready now
• The future of logistics: How generative AI and agentic AI is creating a new era of efficiency and innovation

The post Helping retailers and consumer goods organizations identify the most valuable agentic AI use cases appeared first on Microsoft Industry Blogs.

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

Microsoft Cloud for Retail

Connect your customers and your data

Explore solutions

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?

The post More human-centered retail with AI appeared first on Microsoft Industry Blogs.

Agility helps retail and consumer goods supply chains:

• Quickly switch suppliers, develop more flexible sourcing strategies, and mitigate disruptions from potential tariffs¹
• Adapt product offerings and pricing strategies to combat the lingering effects of inflation
• Adopt more real-time demand forecasting tools and flexible warehousing solutions to keep up with shopping patterns
• Augment human labor with automation to improve productivity and address labor shortages

Retail and consumer goods organizations that develop greater agility will catapult themselves forward by using insights from their supply chains as a critical enabler.

Nonetheless, many retailers’ supply chains struggle with agility because warehouse data is often still on-premises—and that’s holding them back from the latest technologies. Because data is central to all business processes, it’s data that either fuels or inhibits supply chain growth. Reliance on on-premises data and legacy systems likely inhibits supply chain growth because it:

• Causes latency that slows decision-making since leaders lack access to real-time data and often rely on outdated snapshots of old data
• Prevents visibility and collaboration since data is often fragmented and siloed
• Limits scale because systems can’t efficiently process increased data volumes and fluctuating demand
• Impedes flexibility when systems can’t adapt quickly to shifting market conditions and demand
• Impairs adoption of new technologies and processes when existing platforms aren’t adaptable

The warehouse is the ideal starting place for increased digitalization because investments made at the warehouse create value that extends to other parts of the supply chain and enterprise.

Digitalizing the warehouse enables operational excellence and innovation through:

• Data-driven decision-making through real-time insights that help managers make more informed decisions and get teams unified around the same information so retailers can get ahead of demand.
• Reduced operating costs related to warehousing operations through enhanced efficiencies gained by automation and robotics—and improved warehouse throughput through layout optimization, labor efficiencies, and automation. This includes reduced time and labor required for tasks such as picking, packing, and shipping.
• Seamless integration throughout supply chain systems, such as enterprise resource planning (ERP) and warehouse management systems. It also sets the stage for other powerful capabilities, such as intelligent stores.
• More scalability, making it easier for retailers to handle seasonal demand fluctuations or rapid growth without disrupting operations.

Agility helps supply chain leaders drive operational excellence and innovation. Nothing enables that level of agility like the cloud. Here are three compelling reasons to start digitizing your warehouse today with Microsoft and its partner ecosystem.

1. Help warehouse managers drive operational excellence with agentic AI

The role of the warehouse manager is pivotal in the supply chain ecosystem, yet warehouse managers are overloaded with information from multiple sources, making it hard to parse what’s relevant and useful.

Blue Yonder’s warehouse manager AI agent offers an easy-to-digest, interactive report designed to help warehouse managers stay up to date with the most important data and information. The agent delivers those key insights when they’re needed, helping ensure operational excellence every day.

Instead of sifting through hundreds of charts and dashboards, pages and pages of report analysis, or piecing together fragments of information from their teams, warehouse managers get a simplified view of what’s happening, what caused the issue, and what to do about it.

It’s like having a personal analyst working alongside the warehouse manager who knows all about their role, their company, and warehouse. That partnership helps the manager move much more quickly from information overwhelm to clear, decisive action.

Blue Yonder expects more developments coming soon, including more data highlights, summaries, and suggested actions, as well as an expanding list of tasks the agents can perform with human guidance.

2. Optimize warehouse design, planning, and operations with simulation

Today’s customers expect retailers to have what they want and deliver it fast to their store or home. Warehouses are critical nodes in the supply chain where optimizations can improve growth and profitability. From receiving shipments to sorting, picking, and packaging, every step of warehouse operations is being modernized with AI that analyzes changes in the physical world.

Simulating facility designs and layouts, processes, and discrete events in fulfillment and distribution centers helps retail and consumer goods enterprises make more informed and faster decisions without the need to physically install systems to evaluate use cases. Simulation also lets enterprises create and use synthetic data to orchestrate between manual labor and automation systems applying AI, machine learning, robotics, sensor technology, management systems, cloud platforms, and data analytics. How can warehouses achieve operational excellence at every step of the orchestration?

NVIDIA Omniverse is a platform for developing and deploying physical AI and simulation applications for industrial digitalization. Developers use Universal Scene Description (OpenUSD) to build solutions on a platform that enables warehouse scale, digital twins, and simulations to optimize layouts and achieve operational efficiencies. These digital twins also serve as virtual training grounds for autonomous systems and robotic fleets that increasingly operate inside these facilities.

Today, leading retailers and consumer goods companies use applications and solutions built on NVIDIA Omniverse to design and simulate greenfield and brownfield warehouses from scratch, establishing an optimal layout and process flow all in a physically accurate digital space. They can evaluate technologies like robotic shelving systems, robotic grid-based storage, or vertical lift modules (VLM) for high-density storage.

Solutions built on Omniverse let retailers integrate data from different enterprise and industrial systems to create, test, and measure design, process, and operational twins before spending precious capital or stepping foot in the building. For greenfield sites, this means a fully optimized virtual version of the entire design before construction begins. For brownfield sites, retailers can seamlessly integrate new automation technologies with existing systems, ensuring the entire warehouse achieves its operational benchmarks and performs as one cohesive unit.

Applications developed with the Omniverse platform also allow supply chain leaders to understand the impact of discreet events that impact efficiency so they can make decisions that improve key performance metrics like warehouse throughput without the risk of costly physical trials.

In the fast-paced world of commerce, time to value is everything. But platform technologies are never the end-all, be-all. That’s why collaborating with the right partners and experts is crucial for retail and consumer goods enterprises. By bringing together integration partners like Accenture to simplify the development and implementation of end-to-end advanced automation and robotics solutions and services, Microsoft’s powerful cloud solutions, and NVIDIA’s cutting-edge accelerated computing, AI, and simulation platforms, retailers can accelerate warehouse transformation and realize value faster than ever.

3. Boost productivity and collaboration with robotics-enabled automation and intelligent orchestration

Warehouse managers have traditionally relied on manual processes and human labor to keep their operations running smoothly. But labor shortages and rising operational costs are making it increasingly difficult to maintain efficiency and productivity. Additionally, the complexity of managing inventory and ensuring timely order fulfillment often leads to bottlenecks and errors.

Advancements in robotics can help supply chains augment staffing, improve employee safety, and drive warehouse productivity. New capabilities are emerging every day and startups are the ones embracing these new capabilities.

Intelligent orchestration and sortation with Unbox Robotics

The last mile can be a significant chunk of the cost in getting the supply chain right. Unbox Robotics is one of hundreds of startups Microsoft works with to deliver retail supply chain solutions. Unbox Robotics can help automate the last mile process by using robots and swarm intelligence that mimics what a swarm of bees or ants do by carrying goods from one place to another. These robots pick items, sort them, and put them in one lot lightning fast so they can easily be picked up and delivered. And because robots can work around the clock, Unbox Robotics can help retailers offset labor challenges with “always on” reliability.

Smart redistributions with YDISTRI—a new era in inventory optimization

Even the best demand forecasting systems can’t fully prevent real-time overstock and understock issues. YDISTRI doesn’t compete with these systems—it complements them by providing an AI-based reactive inventory redistribution solution. For example, in a supermarket chain, YDISTRI analyzes sales patterns, local demand, and product turnover to identify overstocked items—such as specialty foods or seasonal goods—and moves them to stores where they will sell faster at full price, reducing markdowns and waste.

By weighing transfer costs against the risk of discounts or write-offs, YDISTRI helps retailers maximize revenue from existing stock, improving inventory efficiency without relying on heavy markdowns.

Bend the curve on innovation by digitalizing your warehouse in 2025

Improving agility gives retailers the ability to future-proof their business, flex and scale their operations, and be more responsive and adaptive to consumer demands. Supply chain leaders can achieve operational excellence and catapult themselves forward with generative AI, digital twins, and robotics.

Microsoft partners with Blue Yonder, an organization that provides complete solutions across the entire supply chain, and with hundreds of today’s most innovative startups to complement a retailer’s existing technologies. Start using your supply chain as a business enabler by digitalizing your warehouse in 2025 and gain more agility for years to come.

Microsoft Cloud for Retail

Explore solutions

Learn more

• Microsoft’s responsible AI principles

• Enhancing supply chain efficiency in the retail and consumer goods industry with agentic systems

• Delivering your supply chain copilot: Prioritizing areas of ROI

• Delivering your supply chain copilot: Getting started on ideation

¹ “Tariffs: What Retailers Need to Know,” Bain & Company, January 2025.

The post AI-powered retail: 3 reasons to start digitalizing your warehouse in 2025 appeared first on Microsoft Industry Blogs.

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.  

The AI Advantage

Retail thought leadership study

Get the report

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 Microsoft Industry Blogs.