Revenue growth management (RGM) has never been more essential—or more difficult to execute well.

For years, many consumer goods companies could rely on a relatively stable set of playbooks: predictable shopper behavior, consistent channel economics, and promotional mechanics that reliably delivered results in a more stable environment. Consumers are increasingly price-aware and deal-oriented. Digital platforms make comparison shopping effortless, and agentic commerce accelerates the journey from intent to purchase, while the margin-volume equation continues to shift. In short: what used to be good enough in pricing, promotions, assortment, and trade investment is now a structural risk.¹

At the same time, the broader fast-moving consumer goods (FMCG) model is under pressure. Industry incumbents are navigating slower demand, a reshaping of channels, erosion of traditional scale advantages, and the relentless rise of digitally enabled business models.² The stakes are clear: RGM is no longer a specialized capability sitting inside sales or finance. It is becoming the connective tissue between growth strategy and execution.

However, many RGM organizations continue to operate with fragmented systems, inconsistent definitions, and analytics that struggle to keep pace with change. In a recent discussion I had with leaders from Asper.AI, Chief Product Officer Soudip Roy Chowdhury and RGM Business Unit Lead Vibhor Mishra, we went straight at this reality—and what it will actually take for Agentic AI to deliver outcomes in RGM, rather than headlines.

Why RGM has to change (and why the timing is urgent)

Boston Consulting Group (BCG) recently argued that, amid economic uncertainty, consumer companies must shift their RGM bias from higher profits and productivity to higher volume and market share—and that winners will master three challenges: winning shopper missions, cross-functional orchestration, and rebuilding infrastructure on AI-enabled tools.³

That framing resonates because it reflects what I see in the field: shoppers are changing faster than conventional processes can interpret, and traditional analytic cycles are too slow for today’s volatility. The opportunity is real—but only if we confront the operational reality inside many organizations:

• Trade and spend decisions managed through disconnected tools (sometimes Excel and email).
• Siloed dashboards built by business-unit fringes because there is no shared platform.
• Inconsistent key performance indicator (KPI) definitions across teams, markets, and retail customers.
• A shortage of scalable decision support for complex trade-offs (price versus volume, promo ROI versus. brand equity, distribution versus mix).

If we don’t fix these foundational issues, agentic narratives risk turning into overly optimistic technology narratives—where the technology story races ahead of the business systems required to benefit from it.

Agentic AI, demystified: It’s not just better search

One of the most helpful parts of my conversation with Soudip Roy Chowdhury was his crisp distinction between vanilla retrieval and what truly makes an agentic system useful in RGM.

As he described it, the differentiator is grounding beyond data—combining domain knowledge with organizational knowledge and role-based interpretation. That means capturing not only what the metric is, but how different people in the organization use it to make decisions.

Asper.AI grounds RGM insights on domain, organizational, and role knowledge.  Therefore the insights retrieved for a CFO is different from a Head of RGM, because their KPIs are very different.  This approach increases the utility of an agentic system than just vanilla retrieval systems.

Soudip Roy Chowdhury, Chief Product Officer, Asper.AI

This matters enormously in RGM because success is not about producing “an answer.” It’s about navigating trade-offs across levers—pricing, promotions, assortment, trade terms—while reconciling the differing objectives of sales, marketing, finance, and category teams.

In the discussion, Soudip Roy Chowdhury explained how role-specific grounding can live in a knowledge base (for example, a domain ontology in the form of a graph for knowledge organization and reasoning) that maps KPI meaning, data sources, and how business entities relate—enabling agents to respond with nuance rather than generic output.

The RGM foundation: From System of Record to System of Intelligence to Agentic AI

Then came a moment I loved—because it turned a complex topic into an executive-ready mental model.

Vibhor Mishra described the prerequisites for an RGM assistant as two foundational layers:

1. System of Record: The authoritative source of spend decisions, financial data, and account-level profit and loss truth.
2. System of Intelligence: The ability to bring data together, standardize mappings/assumptions, and operationalize analytics and models (elasticity, forecasting, simulation).

This is the reality check: Agentic AI cannot compensate for missing financial truth, fragmented trade data, or absent governance. It can accelerate and augment—but it cannot conjure decision-quality inputs out of thin air.

At the same time, Vibhor Mishra offered an important nuance: organizations don’t have to finish the foundation journey before starting agentic work. The two can move in parallel, with agentic value expanding as maturity improves.

From dashboards to orchestration: Why central governance matters

We also discussed the dashboard sprawl many consumer packaged goods (CPG) companies face today. Vibhor Mishra nailed one of the root causes: siloed dashboards often exist because a centralized platform doesn’t—so teams build what they need locally, using their own assumptions and definitions.

And that’s where agentic AI can become a forcing function—not by replacing dashboards overnight, but by creating a new layer above them: an orchestrator that can interpret signals, run scenarios, and recommend actions across levers.

But we were aligned on a key warning: if KPI definitions and return on investment (ROI) logic aren’t governed centrally, then agentic experiences will reproduce the same fragmentation—just faster. Vibhor Mishra emphasized that enterprise design choices (what must be standardized versus configurable) are as important as the technology itself.

The hidden value of agents: Speed to insight (not autonomy)

Perhaps the most provocative point in our discussion was the productivity shift.

Soudip Roy Chowdhury described how a decision request that typically takes a large team of analysts a week or two—to consolidate data, run analysis, iterate, and prepare a leadership-ready view—can become near-instant in an agentic model for information extraction and synthesis (not automated action).

This is where I think many leaders misjudge the adoption path. The near-term breakthrough isn’t “autonomous revenue management.” It’s radically faster cycles of decision intelligence—enabling business users to explore scenarios, pressure-test assumptions, and then bring analysts in to critique and deepen, rather than to assemble.

Human judgment remains central. Agents should recommend, suggest, and collaborate—not override.

Agentic AI is not magic, and it is not meant to replace the hard work of real Revenue Growth Management. What it actually does is cut through the noise so teams can focus on the judgment calls that matter. When you move from scattered dashboards to true decision intelligence, you do not get hype. You get clarity, speed and better choices

Marco Casalaina, Microsoft VP Product Core AI

Where Microsoft innovation fits: Horizontal platforms meet domain depth

A question I care deeply about—and asked directly—is how domain players stay aligned as Microsoft accelerates investments in AI platforms and agents.

Soudip Roy Chowdhury described a co-evolution dynamic: Microsoft provides horizontal capabilities, while domain solutions pressure-test them in real enterprise contexts—sending product feedback, such as benchmarking agent performance, and collaborating with Microsoft teams using tools like Microsoft Foundry and opensource components such as LangChain.

This is how modern enterprise innovation scales: platform, partner, and practitioner. Microsoft’s agentic investments can provide the secure foundation—identity, access, orchestration patterns, and governed data experiences—while domain partners bring the deep RGM decision journeys, ontologies, and workflow embedding required for adoption.

A practical takeaway: A readiness lens leaders can actually use

If you’re a CPG leader evaluating agentic RGM, here’s the simplest way I’d frame it:

1. Confirm your system of record: Do you have account-level financial truth for trade and spend? Can you allocate funding cleanly across retailers and levers?
2. Strengthen your system of intelligence: Can you standardize definitions, map data reliably, and operationalize models and simulations?
3. Deploy agentic experiences where speed creates advantage: Start where faster insight loops deliver measurable wins: scenario exploration, cross-lever interpretation, anomaly detection, and recommendation support—with humans firmly in the loop.
4. Add a deliberation layer that turns insights into action: Once data-driven hypotheses are formed, the agent convenes the right collaborators to pressure-test assumptions, build consensus, route decisions into the operational workflow, and continuously monitor outcomes—creating a living learning system that blends human and digital labor to execute complex work with end-to-end traceability.

This is how we move agentic AI in RGM from hype to durable value: decision intelligence grounded in business reality.

Explore solutions and more

• Explore how Microsoft AI for Retail helps consumer goods organizations modernize pricing, promotions, and decision intelligence.
• Learn how Microsoft embeds governance and accountability into AI systems through its Responsible AI practices.

¹ McKinsey: Harnessing revenue growth management for sustainable success

² BCG: Fast-Moving Consumer Goods (FMCG)

³ BCG: Driving Volume-Led Growth in Consumer Markets

The post Agentic AI in revenue growth management: From hype to decision intelligence appeared first on The Microsoft Cloud Blog.

“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

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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 The Microsoft Cloud Blog.

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.

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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 The Microsoft Cloud Blog.

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.

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

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.

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

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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.

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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 The Microsoft Cloud Blog.

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.

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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 The Microsoft Cloud Blog.

Retailers collect vast amounts of data from multiple sources—inventory and staffing, product development, sales, marketing, and more. By unifying this data, retailers can better understand customer preferences, anticipate their needs, and provide memorable shopping experiences that build loyalty. Meanwhile, consumer goods (CG) companies can better monitor manufacturing equipment to reduce downtime, monitor supply chains, anticipate new product trends, and better meet customer needs. It also effectively boosts revenue and balances costs by providing business leaders with insights that drive better decision-making and resource management.

Data challenges holding organizations back

Gaining a unified view of data comes with several key challenges. Fragmented data is a common cross-industry challenge for both retailers and CG companies. Retailers pull omnichannel data from various sources, including e-commerce sites, in-store sales, social media, supply chain systems, and customer service interactions. For consumer goods companies, data comes from research and development (R&D), marketing, sales, industrial equipment (including sustainability data), and supply chain management tools. All of this data is scattered across many sources and comes in a variety of formats, making integration a complex and time-consuming task.

The result? Disconnected insights that prevent business leaders from making timely, informed decisions.

Without a unified data source, retailers struggle to understand customer preferences, predict shopping trends, or manage inventory accurately, while CG companies face machine downtime, supply chain disruptions, and extended product lifecycle management cycles. This lack of cohesion hinders business growth, as it’s harder to provide personalized offers or stock the right products. It also affects profit margins, as data silos lead to inefficiencies and redundancies that could be eliminated.

On top of that, fragmented data can weaken customer loyalty when the shopping experience becomes inconsistent and lacks personalization. It also makes it harder for customer-facing employees at all levels to access, manage, and store information accurately, raising security and compliance concerns.

In retail, consider a furniture store as an example. A shopper browses the website, showing interest in specific items and adding a few to their cart. Later, they visit the physical store, hoping to see those items in person. However, the store associate has no record of the shopper’s online activity and can’t offer personalized recommendations. Frustrated by the lack of connectivity between the online and in-store experiences, the shopper leaves without purchasing, impacting revenue and customer loyalty.

In consumer goods, a company operating large factories might struggle to track real-time performance and maintenance needs without connected data on equipment. When a machine breaks down, production halts, causing costly delays. By integrating real-time data into a unified system, the company could better anticipate issues, schedule preventative maintenance, reduce downtime, and improve efficiency and profitability.

These challenges can significantly hinder growth for retailers, CG companies, and those in both categories. For retailers, the disconnect between online and in-store experiences can lead to missed sales opportunities, customer frustration, and diminished brand loyalty. For CG companies, the inability to accurately forecast demand, track sustainability data, and gain actionable insights creates inefficiencies that hurt profitability, reputation, and competitiveness. Ultimately, the lack of a unified data strategy stifles growth by preventing companies from making informed decisions, optimizing operations, and delivering seamless customer experiences.

Using data to create seamless, connected customer experiences

Fragmented operational data significantly impacts the customer experience, and retailers and CG companies need a comprehensive data estate to remain competitive and meet growing expectations.

A unified platform for data helps consolidate all relevant data into a single source of truth, providing a 360-degree view of the business and its customers. This robust data foundation enables businesses to integrate AI and other advanced technologies to be better equipped to unlock insights, enhance personalization, and optimize the customer journey.

A comprehensive data view also allows retailers to anticipate better and meet customer needs. Returning to the furniture store scenario, imagine if the shopper’s online purchasing history was available to the in-store associate. When the shopper arrives, the associate can seamlessly guide them to their preferred items in the store and even offer a relevant promotion.

In the CG scenario, having a single source of truth for data would make it easier to predict maintenance needs for equipment, reducing costly downtime and ensuring production stays on track to meet demand. In both scenarios, bringing data together helps create a more seamless, responsive experience that drives customer satisfaction, operational efficiency, and overall business performance.

Activating the power of data across your retail organization

The value of data unification goes far beyond the retail stores and the factory floors. A single, unified data platform also simplifies data access and management across the organization. Whether employees are in brick-and-mortar locations, in headquarters, or working remotely, they can securely access relevant insights, enabling better decisions at every level and enhancing operational efficiency.

The advantages of data unification extend beyond front-line operations, providing significant benefits for both leadership and IT teams.

Empowering leaders and executives with insights

Unified data platforms equip C-suite executives with real-time insights into customer behavior, purchasing trends, and inventory movement. These tools enable leaders to:

• Make strategic, data-driven decisions that drive revenue growth.
• Identify high-performing products and emerging market demands.
• Pinpoint new revenue streams, such as personalized service offerings or targeted loyalty programs.
• Allocate resources effectively, focusing on impactful areas like expanding popular product lines or enhancing store layouts based on foot traffic data.

Unlocking advanced capabilities for IT teams

A consolidated data foundation for IT teams opens doors to innovative technologies that enhance customer experiences. With comprehensive data at their disposal, IT teams can:

• Implement AI-powered solutions like intelligent product recommendations and predictive restocking alerts.
• Develop sophisticated digital tools like web-based concierge services to offer real-time personalized assistance.
• Ensure seamless, efficient customer interactions that strengthen satisfaction and loyalty.

By harnessing the full power of your data, your organization can empower all employees to make more data-driven decisions, enhance operational efficiency, and improve customer experiences.

Transform a strong data estate into innovation

In today’s shopping landscape, you most likely have all the data needed to serve your customers better than ever before. You can turn that data into clear and actionable insights with a robust strategy and the right technology solutions. A unified data platform lets you harness the full potential of your information, helping you streamline operations, improve customer experiences, and drive growth.

For a deeper dive into how unified data can transform your business, check out our comprehensive e-book. To learn more about how Microsoft solutions help businesses drive efficiency and growth, visit Microsoft Cloud for Retail and learn more about Microsoft for consumer goods.

Register for a no-cost Microsoft Fabric trial to organize and unify your data and begin unlocking its true potential.

Optimize Shopper Experiences with a Strong Data Estate

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Now that we’ve explored priority areas of return on investment (ROI) that a supply chain copilot can provide in the context of our supply chain, let’s delve into data considerations and how to get started on ideation for your supply chain copilot. The complex real-time decisions and agility required of today’s supply chains can seem daunting when thinking about transformation, but actually is well-suited to application of AI to selected use cases as you get started. 

Technology requirements  

Irrespective of the target areas for a supply chain copilot, the foundational technology capability requirements are the same. These can be thought of three distinct layers that ensure scalability and maximum extensibility into the future. The three layers are laid out below:  

1. Data platform:
   Data is the fuel that powers AI, consequently it must be the foundation of any application of AI such as a supply chain copilot. Data is likely to exist in multiple places, leveraging different storage approaches. Some could be in distributed databases such as Azure Cosmos DB. Other sources might include SQL databases such as Azure SQL. Irrespective of such sources, the need to unify across multiple locations is essential to make sense of the data. This need to govern, model, and consolidate multiple data sources creates a strong case for a single data platform like Microsoft Fabric—offering unified storage, experience, and governance—Fabric was designed to be the data platform for the era of AI.
2. Analytics and AI:
   Some might say that ‘this is where the magic happens’, and when it comes to making sense of the data they would be correct. The analytics and AI layer is where predictions and detailed—yet actionable—insights are generated. As an example, Azure AI Studio empowers you to simplify the creation of AI-powered solutions, giving access to multiple AI techniques and approaches—including generative AI with Azure Open AI, but also incorporating tools such as AI vision or AI Speech.
3. Application layer:
   While it is easy to think of the application layer as the layer with which users interact with your solution, this is only part of the story. Process orchestration and automation is something that can add significant value to a copilot. For example, when a supply chain copilot recommends actions to a supply chain planner—for example requesting expedition of a shipment—it would add so much more value if the copilot were able to execute the recommendation on behalf of the planner.  This is where Azure Functions, which are event driven, can play their part. Typically, this would require interaction with other execution systems through an API—resulting in the need for careful API management over the lifecycle of the different solutions.  

Your own supply chain copilot: Design and delivery  

Experienced supply chain and technology practitioners know very well that what may look amazing in a conceptual demonstration or video can be very difficult to deliver in the real world.  

Issues such as data sources, system integration, technology choices, and overall architecture can make the prospect of delivering your own copilots feel overwhelming.   

Microsoft teams have seen this and pioneered approaches to help you establish a way forward and deliver results quickly. This is broken down into three steps:  

Rapidly ideating on value  

This is primarily a workshop-driven approach to identify and validate specific challenges or opportunities where AI can deliver value for the customer. From a supply chain perspective, this would focus on the elements that you feel offer the largest potential benefit or cause the largest business pain. It is broken into three parts:   

• Inspiration: Here, a high-level education regarding the capabilities of AI, generative AI, and copilots are covered alongside inspirational examples of where customers are using this technology to deliver value.
• Opportunity generation: In this stage, opportunities within your supply chain organization are examined.
• Prioritization: This is the evaluation stage where opportunity areas are prioritized using a simple four-quadrant matrix with ‘Effort’ on the x-axis and ‘Impact’ on the y-axis. This provides a simple yet effective visualization approach to defining priority focus areas.  

The output of this is to be clear on the ”solution route,” or most appropriate combination of technologies to be applied, to deliver what is required. As an example, Microsoft Copilot Studio, Microsoft Azure AI Studio, and Microsoft Power Automate, when combined together, form a very powerful combination to support copilot delivery especially where there are requirements around information extraction, knowledge mining, and process orchestration.   

There is no “one-size-fits-all” so other approaches may be recommended. This may include leveraging partner offerings—Blue Yonder for example have mature capabilities in the form of its control tower solution which may be appropriate.   

Another key outcome of this stage is to understand which of four AI opportunities is being targeted: enrich employee experiences, reinvent customer engagement, reshape business process, or bend the curve on innovation. This aids identification of the key value drivers that you seek to influence, key performance indicators (KPIs) you seek to influence, and provides a segue into the next stage.  

Envisioning  

Again, this is a workshop driven approach—sometimes spanning several sessions—including all relevant customer stakeholders, to determine details of the solution to be delivered.   

This will include establishing a detailed view of architectural elements, interaction and integration points, value potential, and data requirements. A perspective on the business process impact will further enhance the detail behind the value case. This would leverage benchmarks from existing research and established copilots to create a view tailored to your business.  

An additional key output to complement the value case is a view of the investment required and the way in which the delivery can be structured to maximize return on investment and deliver using an Agile delivery approach.  

Proof of concept, minimum viable product, and beyond  

Following envisioning, there is a choice between moving to deliver a proof of concept (POC) or a minimum viable product (MVP).  

A POC demonstrates that an idea or use case is feasible through the delivery of a specific set of capabilities. It is used to illustrate and prove a concept and is usually self-contained in that it does not connect to live data or other systems. Consequently, it is of limited use beyond demonstrative purposes.  

By contrast, an MVP is deployed into production and integrated to existing systems so offers immediate value to the business and end-user while requiring limited effort to deliver. It can therefore become a foundation for further development and enhancement by adding capabilities based on prioritization using Agile development principles.

Integrate AI across your supply chain  

AI transformation, and specifically copilots, present a remarkable opportunity for you to innovate and compete with renewed vigor. By leveraging AI, businesses can enhance efficiency, mitigate risks, and uncover hidden opportunities.   

The integration of AI into supply chain management through a supply chain copilot, for instance, allows for real-time visibility, optimized data management, and seamless interoperation across multiple elements. This shift from reactive to proactive operations enables organizations to consistently deliver the right products at the right time, while balancing inventory, waste, and transportation costs. Moreover, the use of generative AI offers new possibilities for content and insight generation, further empowering supply chain practitioners. As technology continues to evolve, embracing AI transformation will be crucial for organizations to stay ahead in an increasingly complex and dynamic world.  

Learn more  

•  Microsoft Copilot Studio  
• Azure AI Studio   
• Microsoft Cloud for Retail website  
• Microsoft for Consumer Goods website  
• Supply chain AI for the new era of value realization  
• Unlock the full potential of your next-generation supply chain with Microsoft and Blue Yonder  
• Empowering responsible AI practices  
• Copilot in Microsoft Dynamics 365 Supply Chain Management   

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By holistically tracking, analyzing, and sharing information for each facet of their value chain, retail and CG companies can meet this moment while also addressing regulatory requirements—by turning challenges around ESG data infrastructure and organizational culture into an opportunity to build business resilience.  

Starting by understanding your current ESG data management can be a helpful first step: Assess your ESG data readiness.  

New opportunities require holistic, accessible ESG data  

Taking ESG data out of silos and into a unified, accessible system can help retail and CG companies identify and act on opportunities to advance sustainability goals. Eckes-Granini, Europe’s largest fruit juice producer, embraces this strategy by using Microsoft solutions to increase supply chain transparency. By adopting this objective, data-driven supplier management technology, now almost 70% of Eckes-Granini’s juice ingredients come from sustainable sources—putting the company on track to achieving their goal of using 100% sustainable ingredients by 2030.  

Key emerging areas where ESG data insights can help retail and CG companies drive sustainability and efficiencies include: 

• Circularity: Recycling, recommerce, and reusing or repurposing materials help companies reduce reliance on net-new or single-use materials, to help reduce waste and carbon emissions.
• Sustainable material sourcing: Integrating recycled and low carbon materials into operations and final packaged products helps companies minimize overall environmental impact.
• Increased transparency: Companies can improve brand recognition and loyalty by providing greater ESG transparency—which can also help identify new products or lines of business.
• Evolved supply chain: Data and AI-powered technologies can help streamline supply chain, reduce emissions, and minimize waste, while pinpointing ways to boost efficiency and decarbonization.

Today’s roadblocks and tomorrow’s benefits  

As discussed in Driving Business Value with ESG Data Readiness, creating a robust ESG data estate can strengthen business resilience, by enabling teams to make informed decisions as markets shift and opportunities evolve.  

For retail and CG companies, this capability supports decision-making as they explore sustainability improvements, from upgrading equipment to increase energy efficiency, to investing in new low-carbon or recycled materials. It can also help companies advance ESG data tracking in supplier regions where nascent reporting standards can impede transparency, consistency, or granularity of ESG information. 

Retail and CG companies can also collaborate across the value chain to share ESG data that helps unlock shared efficiencies, innovation, and greater trust. For example, by using Microsoft solutions to leverage integrated data analysis, sustainability-driven Radish—a food-delivery startup in Montreal—shares data insights with its restaurant partners to offset supply challenges, reduce food waste, and even access government assistance grants.   

Data that delivers more than reporting compliance  

Leveraging ESG data can also help retail and CG companies explore options such as AI-powered waste reduction, product recommerce services, or innovative, eco-friendly packaging and products. German cosmetics company Beiersdorf took this approach by using Microsoft solutions to build a simulation tool to assess scope 3 emissions for products and packaging, transitioning the company from emissions guesswork to objective insights.  

The power of ESG data insights is also boosting sustainability and improving decision-making for global bakery giant Gruppo Bimbo. The company adopted Microsoft Cloud for Sustainability to centralize emissions data across its operations—helping the company advance toward its sustainability targets for 2025, 2030, and 2050. And the Netherlands’ leading supermarket chain, Albert Hejin, developed an AI-powered solution in partnership with Microsoft to reduce food waste by dynamically adjusting prices on near-expiration items.  

Make ESG data work for you

To help unlock the value of ESG data, retailers and CG companies can benefit from setting up strong data collection and management systems:  

• Bridge the data gap: By investing in technologies that integrate ESG data across operations, companies can collect accurate and actionable information from all aspects of the value chain.
• Build trust through governance: Ensuring data accuracy is essential for both regulatory compliance and strategic decision-making, requiring strong governance and control practices around ESG data.
• Embed ESG into the company culture: Training and engagement programs can help integrate sustainability into a company’s daily operations—from executive leadership to frontline workers. And by enriching data with value-added insights from other departments, combined with natural language querying, more teams are empowered to make sustainability-informed business decisions.  

Following these steps—and the framework in the Leader’s Guide to Sustainable Business Transformation—can help you use ESG data to drive long-term success beyond reporting requirements, according to your unique business priorities. For example, industry-leading businesses like electronics retailer Kotsovolos-Dixons used Microsoft Solutions to create digital twins of its stores, reducing waste and boosting operational efficiency by 50%. Additionally, one of the United Kingdom’s largest food sellers, Co-op Group, adopted our hybrid cloud services to reduce its datacenter footprint to save £400,000 annually. 

Sustainability creates success

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To stay competitive and in compliance, sustainability has become a necessity for the retail and CG industries. But starting with small steps works: ESG data can serve as the foundation for transformation, and help you advance no matter where you are in your sustainability journey.  

We’re ready to partner with you, to help you use AI-powered data technology and Internet of Things (IoT) to begin to accelerate your progress. The growing set of capabilities in Microsoft Cloud for Sustainability are designed to help companies leverage ESG insights to report on and reduce environmental impacts while driving growth well into the future.  

To gain a view of your ESG data across key areas, as well as personalized guidance on how to drive sustainability progress and add business value, complete our readiness assessment.

Explore ESG readiness for other industries

• Manufacturing: Data is driving a more sustainable industrial transformation
• Financial services: ESG data readiness drives value for financial services firms

¹ McKinsey & Company, How to prepare for a sustainable future along the value chain, January 20, 2022.

² PWC, Integrated ESG Data in Retail: Why and How.

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But what if your organization is currently still in pilot mode? The challenge with pilots is that they don’t drive consequential change in the bottom line, and it’s a struggle to democratize learnings. With three in five chief marketing officers (CMOs) driving funding behind investment for generative AI,² there’s pressure to prove return on investment (ROI) and value from AI investment amidst numerous pilots.

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As we transition from hype to reality, brands are shifting from basic generative AI applications like content and tagline generation and to evaluating comprehensive business processes that leverage generative AI to accelerate timelines, unlock more value, and drive increased growth. Marketers are leading the way in leveraging AI as a powerful co-creator at scale.

Let’s evaluate age-old challenges and lengthy processes for marketers.

First, identify key areas where AI creates significant impact in a 6 to 18 month period:

• Increased investment in a multitude of omnichannel marketing solutions and partners has led to fragmented customer profiles and data. Siloed analytics, reports, and data make it difficult to create a unified view of the customers, hindering segmentation efforts to personalize each interaction with customers
• Isolated business analysis tools and cross-media performance and recommendation data across partners and platforms make it difficult to develop real-time or predictive media planning strategies. Marketers struggle optimizing spending and maximizing return on ad spend (ROAS).
• Convoluted naming conventions, metadata tagging, and static reporting from disconnected create “tech debt.” This debt makes it difficult to spot patterns in data such as best keywords, segments, content, and channels.

Powerful personalization at scale with AI

How can AI create more personalized touchpoints across a shopper journey?

Common “personalization” mishaps that decrease loyalty: A customer browses running shoes online but buys a pair in-store, later they receive a generic email offering discounts on the shoes they just purchased and unrelated accessories.

The future process of personalization accelerated with AI: The customers’ needs are anticipated before they even ask. A customer now browses for running shoes online, purchases them in-store, and receives a personalized upsell promotion to “complete the look” with complimentary products. The brand then sends a promotion the following year to upgrade the shoes to a new pair.

Making AI-powered personalization “real”

1. Collaboration is the heartbeat of innovation: Personalization at scale should be a joint priority for business and technical stakeholders. Together, executives collaborate over a single “source of truth” for data and ensure a dynamic flywheel of data is in place, updating customer signals and operational data (supply chain, promotions, product, point of sale [POS]) in real time. Consistent updating and scrubbing of the data sources ensure conversational agents used by marketers, like Microsoft Copilot, are reasoning over accurate, quality data and keeps data secure.
2. Simultaneously, marketers apply “AI as a co-creator strategy” to the end to end (E2E) process of creating, planning, executing, and analyzing campaigns adopting, training, and utilizing conversational agents.

But, how?

Collaboration between IT and the CMO: Preparing the data estate to accelerate personalization at scale, stakeholders can leverage Microsoft Fabric, a unified data platform with compatibility across multiple cloud platforms, allows marketers to access and analyze up-to-date data directly within the governance boundary. Fabric offers intelligent data analytics as a service, allowing brands to build custom reports in Power BI without having to export data, ensuring greater security.  Marketers can spend less time consolidating reports from multiple groups, partners, and internal resources and instead simply ask questions of their data.

Create a comprehensive view of the customer and their journey with a customer data platform like Dynamics 365 Customer Insights, connected to Fabric, offering better segmentation, insight generation, and campaign activation tools for data-driven optimization driving ROAS, growth, and elevated customer experiences. With both Fabric and Dynamics 365 Customer insights, marketers leverage advanced analytics and AI capabilities to gain deeper insights.

Marketers can then leverage Microsoft 365 Copilot as an AI “co-creator” to enhance productivity and collaboration across marketing and agencies, reimagining the entire content creation and activation process, from creative brief development to real-time brainstorming with agencies.

Embrace the future of marketing with AI

As brands struggle to make the promise of generative AI and its benefits “real,” cross-collaboration across both data and marketing stakeholders becomes more critical than ever before. By overcoming the challenges of disparate data, marketers create more effective campaigns that drive better ROI. The future of marketing about more than leveraging generative AI as a content creator, but a co-creator grounded quality, accurate, and up to date in company data and supercharged by large language models (LLMs). These E2E strategies will turn marketing strategies from reactive to predictive. Ready to begin the journey to personalization at scale?  Learn more about how Microsoft can help.

Get in touch with a Microsoft representative at any time for more information on the ways Microsoft can help your retail business achieve more with insightful, intuitive AI tools. We are eager to help you innovate and achieve your goals.

Learn more

• Microsoft’s responsible AI principles
• Generative AI will revolutionize Revenue Growth Management
• “What’s my generative AI strategy?” Microsoft is helping consumer goods brand marketers embrace the era of AI
• Generative AI transforms business processes

¹Sitecore Launches Sitecore Stream, Delivering on Vision for Industry’s First Intelligent Digital Experience Platform.

²How CMOs are shaping their GenAI Future, BCG 2024.

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