AI transformation offers you a phenomenal chance to innovate and compete with new vigor—offering previously unimaginable opportunities. It is a term you are likely to hear more over the coming years, and Microsoft aims to place a copilot on every desk, every device and across every role in support of Microsoft’s mission to empower every person and every organization on the planet to achieve more.

As part of this, Microsoft has identified four areas of opportunity for organizations to drive their AI transformation¹:

• Enrich employee experiences.
• Reinvent customer engagement.
• Reshape business processes.
• Bend the curve on innovation.

The value of AI transformation and copilots

While it may feel instinctive that the value of AI transformation lies in its ability to save time, this is only part of the story. Early studies are already showing significant value from AI transformation being derived from not only reducing costs, but also increasing revenue and reducing risk through improved quality of decision making.

Highlights from key studies include benefits of:

• Delivered 25% increase revenue through enhanced efficiency.²
• Increased customer satisfaction by 12%.³
• Increased revenue growth by 4% through improved strategy and engagement.⁴
• Reduced costs of 10%.⁵
• Completed tasks 25% faster.⁶
• Reduced total expenditure by 0.7%.⁷
• Reduced risk through a 40% improvement in quality of decisions.⁸

The supply chain context

In an era of rapid global change, macroeconomic shifts, and geopolitical disruptions, the global supply chain faces unprecedented challenges. Simultaneously, technology is undergoing a transformation fueled by data and AI. These powerful tools and capabilities empower organizations to enhance efficiency, mitigate risk, and discover hidden opportunities.

As the world becomes increasingly complex, leading organizations are gravitating towards technology to accelerate supply chain optimization with greater speed and precision to shift the paradigm from a reactive mode of operating to one that is proactively getting ahead.

It is a foundational concept that supply chain excellence is achieved by consistently and efficiently getting the right products to the right place, in the right quantities, at the right time and at the desired quality, the first time. Doing this while respecting constraints and balancing inventory, waste, and transportation costs is what makes the work of a supply chain practitioner so difficult.

Integral to this challenge is optimized data management, real-time visibility combined with integration and interoperation across supply chain elements—such as production, logistics, procurement, partners, and customer service.

Yet so often, organizations struggle with siloed business processes, communications challenges, disconnected systems, complex planning workflows, transportation disruption, warehouse capacity issues and multiple other challenges leading to high inventory, increased costs, waste, and a lack of overall business resilience.

For a supply chain practitioner there are simply too many information sources to assimilate and consider when making better-informed decisions in real time. The practitioner can get started with a copilot to overcome fragmented data and integrate it into usable insights. Read about how Altana began overcoming fragmented knowledge—establishing a uniform understanding of the data/knowledge gap combining enterprise resource planning (ERP) systems, factory data, enriched with market and external risk factors.

The application of AI across the supply chain

With all the focus on generative AI, it can be easy to perceive that generative AI is the answer to all your problems. This would be incorrect—as ever there are no silver bullets. AI and generative AI are distinct, yet complementary technologies used for supply chain optimization that provide the analytical horsepower to process vast amounts of data that can deliver significant impact.

Non-generative AI techniques can be used for multiple different tasks in a supply chain context, for example:

• Clustering: Route planning for customer shipments and Warehouse slotting optimization.
• Classification: Inventory management approaches (for example, fresh, frozen) and resource allocation.
• Rules and heuristics: Inventory planning and distribution planning.
• Optimization: Inventory optimization, and route optimization and network design.
• Regression: Demand forecasting and supplier performance analysis.

Likewise, generative AI offers some incredible opportunities across the supply chain, which can be broadly placed into three groups:

• Content generation: For example, summarizing multiple contracts and agreements associated with a given supplier.
• Insight generation: For example summarizing multiple sources of external data to provide a perspective of events that could influence your demand forecast.
• User Interaction: Provision of a universal interface with which supply chain practitioners interact and spans multiple systems and allows for both understanding and interaction with systems that control the supply chain.

The control tower concept

You can think of your supply chain function as a central brain orchestrating data and physical movements across your organization. This is critical work, influencing all the key metrics that drive business performance.

The concept of a supply chain control tower appeared a few years ago as a centralized system providing real-time visibility and insights across the entire supply chain. It leverages a unified data platform to deliver next-generation supply chain capabilities, beginning with end-to-end visibility and performance management.

The concept looks to incorporate data from various sources to help you monitor, manage, and optimize your supply chain operations, enabling better decision-making and more rapid responses to disruptions.

Adding AI into this mix offers tantalizing possibilities—the ability to dramatically reduce the quantity of direct decision-making that supply chain practitioners need to be directly engaged in.

Enrich employee experiences

Generative AI is fundamentally changing how we, as individuals, relate to, and benefit from technology. While both generative AI and traditional AI contribute to supply chain optimization, generative AI emphasizes employee productivity and can work with a broader set of data, revolutionizing the types of insights you can glean with better explainability. The gamechanger here is the ability to use a conversational “agent” or copilot to navigate any task and turn data into knowledge through a conversational user interface using natural language. A copilot can enhance supply chain teams by providing real-time insights, automating routine tasks and workflows, and facilitating collaboration. For instance, it can analyze data to identify bottlenecks, suggest optimal routes for shipments, and streamline inventory management. It provides the ability to move beyond static dashboard reporting by extracting actionable insights to empower users.

A copilot for supply chain can help empower teams during their workday by converting predictive insights into specific actions while powering collaboration within a connected ecosystem.

This means organizations are better able to manage the cascading impact of their supply chain with more transparent and collaborative data sharing. Visibility improves because, where once it was restricted by the network it is now enhanced through a wider global context.

Internal data is augmented with real-time connections to partners and external signals—like geopolitical tensions, logistics challenges, and commercial factors like promotional activity or weather events. Data is continuously available and interoperable across the supply chain, giving users simultaneous access to current information, with the ability to pass on insights into the wider organization. Microsoft Teams and Microsoft 365 become engines in the connected ecosystem for greater connectivity and collaboration—empowering team members who may not be using supply chain systems—like a store manager or sales representatives—to be consumers of supply chain insights and information. This improves access to insights that are actionable at the optimal point in the value chain.

Copilots can dramatically improve productivity while accelerating decision-making. For example, take this common scenario where Hillary—an inventory analyst—needs to understand why projected cost and freight (CFR) of a key product has dropped and determine what to do to reduce impact on customer service level agreement (SLA).

Instead of compiling spreadsheets from different data sources and spending hours doing manual analysis, Hillary uses a combination of copilots and a CFR prediction algorithm to quickly identify the root cause, assess alternatives, and share the recommended approach with her manager.

Next steps to apply generative AI across your supply chain

We’ve explored some strategies for applying AI and generative AI across your supply chain, and how a supply chain copilot can support supply chain practitioners. Stay tuned for part two, where we delve into data considerations and how to get started on AI ideation for your organization.

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

¹Embracing AI Transformation: How customers and partners are driving pragmatic innovation to achieve business outcomes with the Microsoft Cloud, Official Microsoft Blog.

²How Netlogic Computer Consulting is Boosting its Sales Performance with Microsoft Copilot for Sales, Tech Community.

³Microsoft: Copilot for Service Boosts Customer Satisfaction by 12 Percent, CX Today.

⁴What Can Copilot’s Earliest Users Teach Us About Generative AI at Work?, WorkLab.

⁵Is Microsoft Copilot Worth the Investment?, Varonis.

⁶Navigating the Jagged Technological Frontier.

⁷Is Microsoft Copilot Worth the Investment?, Varonis.

⁸Navigating the Jagged Technological Frontier.

The post Delivering your supply chain copilot: Prioritizing areas of ROI appeared first on Microsoft Industry Blogs.

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.

The post Harnessing AI to supercharge personalized marketing at scale appeared first on Microsoft Industry Blogs.

Maximizing AI: Category management and more

Buying habits shift quickly in today’s consumer-driven world. For retailers, especially grocers, providing customers with affordable, fresh, and convenient options while navigating the impacts of inflation and supply chain disruption is critical. Meeting these expectations requires creating and maintaining a supply chain centered around customer demand—no easy task when supply chain functions are siloed, data is disparate, and needs change from day to day.

Together, Blue Yonder and Microsoft are unlocking a new era of value for retailers with AI. With AI-powered solutions, retailers can empower their teams to make decisions based on access to real-time data and intelligent insights. AI has allowed us to reimagine planning, making it possible for retailers to operate more effectively by transforming category management into an agile, responsive, and ongoing process that is tightly synchronized with the broader supply chain.

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AI-powered category management makes it simple to keep the end consumer the focal point of your supply chain functions, helping retailers quickly achieve several critical capabilities:

• Address demand across every channel
• Plan at the hyperlocal level
• Optimize for demand in real time
• Factor in space and labor parameters
• Monitor and adjust instantly
• Identify and respond to opportunities and concerns quickly
• Enable continuous learning with constant space and assortment performance feedback
• Share updated demand forecasts across the supply chain

Enabling AI in this way facilitates a constantly improving demand forecast as the AI model builds iteratively on the data provided, allowing planners across the entire value chain to make better decisions for the business. It’s clear that, properly integrated, AI is not just a technological advancement but rather a strategic tool that can lead to improved customer experiences, operational efficiencies, and ultimately, financial growth and scale for retailers.

Blue Yonder and Microsoft teams recently collaborated to present a webinar titled “Supercharge Your Category Management Process with AI Assistance.” In this presentation, we introduced category managers to the many ways AI-powered assortment can help streamline category management and empower faster, smarter decision-making.

But category management is just one piece of the modern supply chain puzzle. In this blog post, we’ll discuss some of the major connecting points between category management and the overarching supply chain and how understanding the interplay between components can help you begin to realize the art of the possible with supply chain AI.

To that end, we’re looking at three major considerations for making the most of category management within a broader, AI-powered supply chain.

1. Synchronizing with the overall supply chain

One crucial thing to consider is the extent to which your category management process must be synchronized with the broader supply chain to enable an agile, responsive, iterative process. This requires thinking about how you get the initial data, and then how you operationalize it — how you put the data to work. Everything should be framed in terms of the end consumer as the focal point, making sure that you address demand across all channels. Doing so normalizes the physical and the digital channels, enabling hyperlocal planning at the individual store level.

It used to be that whatever the practice was, you would cluster stores and talk about stores that had similar formats, planning similarly for all store locations based on one generalized model. Now, with the integration of AI-powered insights and analytics, we’re getting into hyperlocal store planning, where you can really reflect not only the local community shoppers who are making the trip into brick-and-mortar locations, but also support the way that buyers want to shop online, normalizing those two experiences.

But this also requires acute awareness around demand planning, as you have to essentially make sure that demand planning is optimized in real time. This is why the correlation with the supply chain is so important: because you’re reflecting the latest trends, but you’re also working around the space and labor parameters in the store and optimizing in real time to make sure that demand planning is updated accordingly. This ability to execute on constantly changing data across workstreams—to monitor and adjust on the fly—is key to achieving the agility piece that’s so necessary for responding with flexibility to market demands and driving better margins for the business.

2. Enabling collaborative data sharing

Data sharing sits squarely at the intersection between retail consumer goods and category management. In an AI-supported category management process, you have category captains managing entire shelves of a category and gleaning invaluable insights in the process about the performance of products on the shelves, both physical and digital. These insights inform and support their retail partnerships in ways that weren’t possible until very recently.

Cross-capability data sharing allows you to identify the problems and root causes, understand them quickly, take action, and then implement that continuous learning. With interoperability, you can leverage that AI-powered continuous learning component around space and assortment performance, feeding that data back into the forecasting engine to generate an updated view of demand that can be shared across the supply chain so that the demand forecast is constantly improving, allowing planners across the entire value chain to make better decisions.

But a plan is only as good as the ability to execute it, so we move on to thinking about the execution piece and how to optimize that with store-level compliance.

3. Pulling in the store as a node in the supply chain

Syncing this concept of category management with the supply chain is critical for high-impact results because this is where operationalizing your data becomes real. It’s important to understand that integrated architecture is not an orchestrated ecosystem. In order to have a holistic view of the business, synchronization has to take place. You’re reducing the latency to have better data synchronization across various supply chain functions; you’re enabling the collaboration both with store associates but also with brands and retailers, empowering adaptive decision-making by connecting the planning and execution functions.

What’s pivotal to realize here is a theme that we’ll see become more prominent over time: the store is now a huge data source that needs to be integrated with the rest of the supply chain. As we see customer experience playing an increasingly pivotal role in the supply chain, we see a greater need to incorporate store-specific data. It’s no longer that we’re just optimizing store operations off to the side—the store and its operations are now part of the supply chain itself.

Many organizations seek to address concerns around siloed technology, and yet, the retail store often continues to be an overlooked component. Many retailers have warehouse management systems that are connected to their transportation management solutions (TMS), but very rarely do they also connect the stores as being a node in the supply chain for real inventory visibility. So, when we think about optimizing across the different channels with e-commerce and fulfillment, structuring warehouses and the fulfillment network, it becomes more relevant to connect the data across these functions.

Powering a connected supply chain with Microsoft and Blue Yonder

Integrated AI across the supply chain has incredible potential to enhance business performance and reduce volatility with predictive intelligence. Together, Microsoft and Blue Yonder are making it easier for retailers to get ahead with technologies that empower agility, transformation, and innovative operations at scale.

Bringing together the best of supply chain technology and cloud platform capabilities, Blue Yonder and Microsoft are at the forefront of a cognitive revolution of supply chain innovation. Blue Yonder’s Luminate® Cognitive Platform lays the foundation for a truly intelligent autonomous supply chain with predictive and generative AI capabilities that are industry-specific. It’s built on Microsoft Azure, which is a game changer in the cloud platform space, ensuring data is unified for centralized and accessible insights. Our partnership enables supply chain innovation by connecting information across the value chain for better collaboration, scalability, security, and compliance.

Sainsbury’s: Results that speak for themselves

Sainsbury’s is a trusted UK brand, loved by millions of consumers and operating more than 2,000 store locations across its Sainsbury’s and Argos brands. A longtime user of Blue Yonder’s warehouse management, Sainsbury’s sought to implement new AI-powered solutions in 2023 to improve forecasting and replenishment capabilities and increase sustainability.

Blue Yonder has helped Sainsbury’s to tackle several significant goals:

• Realizing improvements in inventory stockholding and availability key performance indicators (KPIs) with machine learning (ML) forecasting and multi-echelon replenishment
• Transforming Sainsbury’s architecture and business processes to become easier to understand, scalable, resilient, and nimble, as well as able to support any future business changes quickly
• Reducing the current number of key systems to eliminate redundant functionality, reduce technology risk, and improve the user experience for colleagues, suppliers, and business-to-business (B2B) customers
• Offering a more automated, simplified user experience and standardized workflows to increase user productivity

Our partnership with Sainsbury’s has already resulted in significant savings for the organization as part of its ongoing plan to future-proof the business. Sainsbury’s leadership confirmed in April 2024 that the company is unlocking significant savings and have already improved ambient availability, using real-time forecasting to optimize sales, waste, and stock equation.

Implementing Blue Yonder’s solutions built on the resilient, scalable Microsoft Azure cloud platform, Sainsbury’s has elevated its ability to monitor and respond to changing customer needs with new capabilities allowing prediction and prevention of potential supply chain disruptions. Blue Yonder has helped Sainsbury’s take advantage of ML-based forecasting and ordering capabilities to help stores better manage fresh and perishable products, while also achieving visibility, orchestration, and collaboration across the end-to-end supply chain, using automation to make better business decisions.

Explore solutions from Microsoft and Blue Yonder

• Learn more about Blue Yonder category management and Microsoft Azure
• Learn more about Blue Yonder Luminate Cognitive Platform
• Learn more about Blue Yonder Orchestrator
• Explore Microsoft Cloud for Retail

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At the heart of this transformation is our global ecosystem of partners and developers who build industry solutions on the Microsoft Cloud. Organizations are only beginning to grasp the full extent of the possibilities that lie ahead to innovate and transform with AI. Microsoft and our partners are investing in industry-specific capabilities to help customers adopt revolutionary AI solutions faster like copilot templates to create AI assistants for high value scenarios and industry data solutions that provide a data and analytics foundation to ensure your data is AI ready. 

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The age of copilots

Copilots, your everyday AI companion that assists with complex cognitive tasks, helps streamline operations, and enhances decision making, are becoming integral to almost every aspect of work and creativity. Powered by advancements in machine learning and natural language processing, copilots are impacting nearly every industry and business function, such as healthcare, manufacturing, financial services, and agriculture.  

For example in healthcare, Stanford Medicine is deploying Nuance DAX™ Copilot, an AI assistant that automates clinical documentation such as visit summaries and care instructions, to reduce heavy administrative workloads that lead to physician burnout and expand access to personalized, high-quality care. In a preliminary survey of Stanford Health Care clinicians using DAX Copilot, 78% reported that it expedited clinical notetaking.  

In manufacturing, Volvo Group is using Microsoft Azure AI to streamline invoice and document processing in their customer service and finance departments, which had to process critical internal and customer-facing paperwork in various forms from emails to PDFs and written bills. By extracting data from images, like photographs and stamps, and translating documents to and from multiple languages, Volvo Group is saving more than 850 manual hours per month. 

These success stories are just the beginning. We are also collaborating with industry partners to bring the power of copilots to their workforce and their customers, including LSEG (London Stock Exchange Group) in financial services, Bayer in agriculture, and Siemens in manufacturing.  

LSEG (London Stock Exchange Group), one of the world’s leading providers of financial markets infrastructure with more than 45,000 customers in over 170 countries, is using AI to improve productivity with interoperable, secure, and compliant solutions that help optimize strategies and enable faster data-driven decisions in financial services. By integrating Microsoft Copilot capabilities into existing workflows, LSEG is working with Microsoft to build a new, interoperable solution to streamline meeting preparation for financial services professionals like investment bankers, built directly into Microsoft Teams. This solution makes it easier to discover data, summarize documents, and support investment bankers, making their customer interactions more efficient and informed. 

Additionally, LSEG is creating custom chatbots and copilots within its flagship LSEG Workspace platform that makes it easier to switch to Teams through seamless, end-to-end workflows, enabling interoperability with custom application environments to provide quick answers to financial queries. Capitalizing on AI, LSEG’s solutions will transform the financial services industry by improving productivity and accelerating value creation for customers. 

Bayer, a global enterprise with core competencies in the life science fields of healthcare and agriculture, is piloting a unique generative AI solution for agriculture, trained by Bayer agronomists using proprietary Bayer product data. The solution aims to assist Bayer team members by providing quick and accurate answers and support to agronomy, farm management, and Bayer agricultural product-related questions. The system responds to natural language queries, generating concise, relevant information within seconds, a significant improvement over the traditional time-consuming process of information gathering and synthesizing. AI is quickly becoming an indispensable technology for the agriculture industry, with the potential to serve agronomists and benefit farmers globally. The collaboration with Microsoft as a leading technology partner is enabling exploration of ways to integrate this technology into Bayer’s digital offerings, with broad opportunities for collaboration with other agricultural offerings and partners. 

Siemens is a leading technology company focused on industry, infrastructure, transport, and healthcare. Siemens’ Industrial Copilot is a generative AI-powered assistant designed to enhance human-machine collaboration and boost productivity in manufacturing, exemplifying the transformative solutions and sustainable growth potential of our global partner ecosystem. Siemens Industrial Copilot allows users to rapidly generate, optimize, and debug complex automation code, and significantly shorten simulation times from weeks to minutes. It ingests automation and process simulation information from Siemens’ open digital business platform, Siemens Xcelerator, and is enhanced with Microsoft Azure OpenAI Service.  

To find out more about solutions specific to your industry, please visit Microsoft AppSource.  

New copilot templates for retail and sustainability

In our ongoing effort to foster innovation, we are excited to introduce more ways to help you jumpstart your copilot journey. At Microsoft Build 2024, we announced new copilot templates for retail and sustainability. 

1. Store operations help retail frontline workers improve customer service and productivity by using natural language to query store operating procedures, processes, and policies on topics such as product returns. 

2. Sustainability insights enable users to easily obtain insights, facts, and data around their own company’s sustainability goals and progress. 

Available through Copilot Studio, these templates provide ready-made dialogs, intents, entities, prompts, and actions that can be easily customized and extended according to the user’s needs. 

Additionally, we will be adding industry-specific prompts to the Microsoft Copilot Lab. These new prompts will help customers quickly get started using Copilot for sector, job, and role-specific scenarios and can be customized with domain specific details, best practices, and industry context to reduce trial and error and ensure high-quality output. 

Fuel AI with industry data solutions in Microsoft Fabric

An AI-ready data estate is critical to the success of AI. Analyzing vast amounts of data, often unstructured or semi-structured, poses a significant challenge for any organization. Our industry data solutions built on top of the Microsoft Fabric platform provides a one-stop-shop for data integration, data engineering, real-time analytics, data science, and business intelligence without compromising the privacy and security of your data. Data solutions in Microsoft Fabric provide a robust platform for customers and developers to manage all their data in one place, leverage a suite of analytics experiences that work together seamlessly, and apply AI to help make data driven decisions that address challenges unique to their industry. Some of these data solutions available in preview include:  

Healthcare data solutions in Microsoft Fabric (preview) 

This tailored solution enables healthcare organizations to break down data silos and harmonize their disparate healthcare data in a single unified store where analytics and AI workloads can operate at-scale. Leveraging the native capabilities of the platform, healthcare organizations can create connected experiences at each point of care, empower their workforce, and unlock value from clinical and operational data.  

Retail data solutions in Microsoft Fabric (preview) 

For retailers to truly deliver personalized experiences through generative AI for their customers, the first step is to break down data silos within their organizations and get a holistic understanding of their data estate. A unified data platform is the key to unlocking deeper, actionable insights that give retailers the ability to drive more meaningful experiences for customers. Achieving data compatibility is also a key step to getting value from AI investments, ultimately allowing retailers to optimize store operations, enhance store associates’ performance and productivity, and uncover insights for product upselling and shelf optimization. Partners like Sitecore are already connecting and building on top of the platform to further help retailers overcome data incompatibility and unlock new capabilities in Microsoft Cloud for Retail. 

Sustainability data solutions in Microsoft Fabric (preview)  

The solution allows organizations to centralize and transform disparate data into standardized environmental, social, and governance (ESG) data lakes. It also enables the collection and processing of subscription and resource level emissions data. Users can connect to their data in Microsoft Cloud for Sustainability solutions to create custom environmental notebooks and build insights to better understand their carbon, water, and waste emissions. These capabilities are provided through prebuilt and preconfigured Fabric resources, that can be easily configured for your sustainability needs. 

Resources to drive innovation

To get started and learn more about our complete set of data and AI industry solutions, visit Microsoft Industry Clouds or sign-in to your account on Microsoft Cloud Solution center. 

Learn more at the Copilot learning hub, where you can discover how Microsoft Copilot can help you in your specific industry.  

Learn more about these announcements at Microsoft Build 2024

• Microsoft Build opening keynote with Satya Nadella, Kevin Scott, and Rajesh Jha
• Next generation AI for developers with the Microsoft Cloud
• Enabling ISVs to transform industries in the age of copilots
• Launch AI applications and get to market faster with marketplace
• Transforming the most valuable industry workflows with Copilot
• Shaping next-gen development: the future of Copilot in Power Platform
• Microsoft Fabric: What’s new and what’s next
• Build your own copilot with Microsoft Copilot Studio
• Grow your business with Copilot extensions and marketplace

¹Number of employees worldwide from 1991 to 2024, Statista.

The post Accelerating business transformation with industry AI and copilots appeared first on Microsoft Industry Blogs.

During this wave, we’ve invested heavily in the development of copilot templates to enhance capabilities and integration across various industries. These customizable templates offer improved operational efficiency, enhanced customer engagement, and seamless integration with existing technology, all while supporting a diverse, global customer base. Copilots are valuable assets for Microsoft Industry Clouds customers, helping to drive customer and partner success. Microsoft’s partner ecosystem extends our offerings, with systems integrators and independent software vendors enabling factory data ingestion from different systems and building custom UI experiences for the copilot templates on Microsoft Azure AI.

Here’s a look at what’s been delivered since the release plans announcement in January 2024.

Microsoft Cloud for Manufacturing

Microsoft Cloud for Manufacturing is introducing new solutions in preview to optimize factory operations. These include manufacturing data solutions in Microsoft Fabric and a copilot template for factory operations on Azure AI. These solutions enable manufacturers to ingest and unify data from diverse sources, standardize and enrich data for seamless interoperability, and utilize custom copilots for querying data through conversational interfaces. Fabric allows users to maximize the value of factory data and uncover operational insights for production optimization by unifying information and operation technology data into an open and secure data platform. The copilot template for factory operations on Azure AI enhances responsiveness and streamlines communication across teams and roles.

Azure Data Manager for Agriculture

This release of Azure Data Manager for Agriculture includes new copilot templates that can empower organizations to build agriculture copilots with Microsoft Azure OpenAI Service. These templates provide a powerful tool for organizations to use generative AI and data to optimize their operations and engage better with their customers. Customers are bringing generative AI to life for farmers. ITC, a multi-industry enterprise, has created, Krishi Mitra, an AI copilot, developed using Microsoft copilot templates. With this application, ITC seeks to empower farmers by providing them with timely and relevant information that can boost productivity, increase profitability, and enhance climate resilience.

Copilot templates can support use cases based on tillage, planting, crop protection, harvesting, and other types of farm operations. Users can submit queries such as “show me active fields” or “what is the average yield for my field?”. These use cases can help input providers to plan equipment, seeds, applications, and related services and engage better with the farmer.

Using data from Azure Data Manager for Agriculture and other sources, copilots can provide insights on topics like disease risks, yield forecasts, labor needs, crop protection, weather impacts, and harvest windows. Enabling seamless retrieval of data and allowing for plugins, embedded data structures, and subprocesses to be selected as part of the query flow allows organizations to extend their copilot use cases to many roles and scenarios along the agriculture value chain.

Microsoft Cloud for Sovereignty

Microsoft Cloud for Sovereignty is a solution that helps public sector organizations use the public cloud and advanced technologies while helping meet security, sovereignty, and regulatory requirements. The latest release of Microsoft Cloud for Sovereignty introduces updates and new features to streamline the configuration and deployment of sovereign environments. Guardrails and codified architectures reduce complexity and make the process of building sovereign environments more predictable and repeatable. New preview tools include assessment, policy compiler, and drift detection analysis tools, as well as a new Azure service that allow users to create and deploy Sovereign Landing Zones (SLZs) within the Microsoft Azure Portal. Guidance includes sample reference architectures for using large language models (LLMs) and Azure OpenAI Service with SLZ, as well as guidance on workload migrations and Microsoft Power Platform and Microsoft Dataverse configurations.

Microsoft Cloud for Sustainability

In February 2024, Microsoft Cloud for Sustainability announced new data solutions and generative AI advancements in Fabric, providing new levels of speed and efficiency in processing data to help drive faster progress toward sustainability goals. These new features include sustainability data solutions in Fabric and natural language queries with Microsoft Copilot in Microsoft Sustainability Manager, among other AI-powered features now available in preview.

In March 2024, new features were added to Sustainability Manager, including the ability to create calculation models with Copilot using natural language input, a dedicated energy data model to help track energy usage, activity to emissions traceability to link underlying source activity data to emissions records, and the ability to create a Microsoft Power Query template to streamline and accelerate data import.

Additional release updates to Cloud for Sustainability include enhancements to environmental, social and governance (ESG) insights with what-if analysis to help organizations build the relationship between forecasting and reduction goal planning. Users can link forecasts to existing goals to track actual progress alongside the projected ones. In addition, forecasts with the same historical data can be layered onto a single view, allowing for faster analysis of optimal reduction opportunities.

Another new feature is the ability to import and calculate with product carbon footprint data. This feature allows you to use product carbon footprint data to calculate and understand value chain emissions in Sustainability Manager more easily. Organizations can determine the greenhouse gas emissions that are associated with a product family and more easily import and manage this data within Sustainability Manager.

Azure Data Manager for Energy

Azure Data Manager for Energy is expanding geo availability, adding the Australia east region. This additional region is enabled for both the standard and developer tiers of Azure Data Manager for Energy. Users can now select “Australia east” as a preferred region when creating an Azure Data Manager for Energy resource using the Azure portal.

External data sources (EDS) (preview) allow data from external data sources aligned with the OSDU® Technical Standard to be shared with an Azure Data Manager for Energy resource. EDS is designed to pull specified data (metadata) from OSDU-compliant data sources through scheduled jobs while leaving associated dataset files (such as LAS and SEG-Y) stored at the external source for retrieval on demand.

Microsoft Cloud for Retail

Microsoft Cloud for Retail now includes new features in its retail data solutions architecture, an industry-specific workload for unifying, enriching, and modeling industry data on Fabric. Retailers can take advantage of the available list of connectors, application templates, and business intelligence capabilities, which can be easily configured. Retail data solutions offer application templates tailored for retail-specific scenarios, accelerating time to market. These templates serve as customizable and extendable starter kits, allowing retailers to adapt them to their unique requirements. Additionally, application templates and connectors from specialized partners are available. These capabilities enable the seamless use of data to produce unique insights that can’t be achieved in isolation.

One of the new features is the copilot capabilities in Fabric, which bring new ways to transform and analyze data, generate insights, and create visualizations and reports in Fabric and Microsoft Power BI. Another new feature is the Sitecore OrderCloud data connector, which can be used to bring commerce data from Sitecore OrderCloud (preview) into Fabric in real time. The connector performs transformation and orchestration on top of the data from Sitecore OrderCloud to map it to the retail industry data model, reducing engineering effort and accelerating time to insights.

Microsoft Cloud for Healthcare

The 2024 release wave 1 also brings new features and innovations to Microsoft Cloud for Healthcare. One of the new features is the ability to improve clinical and operational insights by ensuring health data is accessible across provider, payor, and pharma; and facilitating clinical, operational, and performance analytics using healthcare data solutions in Fabric (preview).

Some other new features in the 2024 release wave 1 for Microsoft Cloud for Healthcare include support for additional data storage needs, support for availability zones for Microsoft Azure Health Data Services, FHIRLink Power Platform connector, and the ability to use the digital imaging and communications in medicine (DICOM) service with Azure Data Lake integration.

Microsoft Cloud for Nonprofit

Microsoft Tech for Social Impact is proud to announce the April 2024 release for Fundraising and Engagement. This release brings significant enhancements, mainly to nonprofit gift processors, including valuable enhancements to Fundraising and Engagement Azure services and new Stripe API (payment intents) integration. Customers who rely on Stripe for their payment processing can now benefit from the latest Stripe APIs, addressing the requests of current customers and the requirements of future customers. It is highly recommended that customers upgrade and use the new Stripe API when creating a payment processor associated to a configuration profile. For more details, read more here.   

Learn more about Microsoft Industry Clouds

• Official Microsoft Cloud for Manufacturing documentation
• Azure Data Manager for Agriculture Preview
• Microsoft Azure Data Manager for Agriculture Preview
• Official Microsoft Cloud for Sovereignty documentation
• Microsoft Cloud for Sustainability documentation
• Microsoft Azure Data Manager for Energy
• Official Microsoft Cloud for Retail documentation
• Official Microsoft Cloud for Healthcare documentation
• Microsoft Cloud for Nonprofit documentation

Microsoft Industry Clouds

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In just over a year, it has gone through what you might call the ‘shiny toy’ stage where teams play with it to try and work out what it can do for them. From this, lessons have been learned and applied.  Some of the lessons Microsoft teams have learned have been highlighted in previous blog posts.

Microsoft’s customer teams have undertaken many customer workshops, each focused on identifying the areas that have the greatest opportunity for benefit. 

McKinsey suggests that for retail and consumer goods businesses, the value potential is somewhere in the region of 1 to 2% of the total industry revenue. As for the ‘low hanging fruit’, about “75% of the value that generative AI use cases could deliver falls across four areas: Customer operations, marketing and sales, software engineering, and research and development (R&D).”¹ But in practical terms what does this look like if you are a retail or consumer goods company? 

From the work Microsoft has undertaken there are three broad groups of use cases that offer the greatest value: 

1. Content and product marketing. 
2. Internal knowledge management. 
3. Customer conversational experience. 

You may wish to explore each of these areas with a view to understanding what others are doing and considering examination of something similar in your organization. 

Microsoft Azure OpenAI Service

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Content and product marketing

At the heart of generative AI is the ability to create new content, so it stands to reason that this would be an area of high potential.   

Content marketing traditionally involves a series of iterative loops involving multiple parties—perhaps a brand or product owner and a creative group—be this a copywriter or a creative agency that produces images. 

This approach has several challenges. Firstly, due to the iterative nature between different parties it can take several days or weeks of iterations due to the lags between each create—review—revise cycle. For visual content—with the use of external agencies, complex backgrounds, complex picture, and editing—this can become expensive.   

These two reasons mean that scaling the creation of content becomes very difficult. If you have a very wide range of products or a wide range of customer groups for which you would like to customize the message, it is simply impossible to achieve this with a traditional approach. 

Generative AI changes this. 

One of the first case studies regarding the use of generative AI that Microsoft highlighted was the creation of product related marketing content at a vast scale. Carmax—a used car retailer—wanted to provide a consistent set of product information for all the different makes and model of cars that they sell. Generative AI was used to generate text for car comparisons allowing viewing of specifications, features, highlights, and summary reviews. Carmax estimated that to build this would have required eleven years of effort—which dramatically illustrates how generative AI can address the scaling challenge when a retailer has a wide range of products. Learn more about the Carmax case study alongside example content and a short video. 

Marketers aspire to segment their customers into smaller and smaller groups to make messaging as personal as possible. Customer data platforms such as Microsoft Dynamics 365 Customer Insights allow creation of segments based on customer attributes from multiple sources. Websites and social media platform allow specific messages to be targeted at these groups but the challenge of having the capacity and time to create the relevant content remains a constraint. 

This is where generative AI can be used to fill the gap. A number of organizations are utilizing an innovative approach of aligning keywords to their products and then using generative AI to suggest a series of advertisements, or social media headlines associated with specific consumer profiles.  Following review by a copywriter, to ensure brand alignment and an appropriate tone, these headlines are then approved for use. This approach can enhance overall creativity as well as enabling more granular targeting.

Internal knowledge management

“If HP knew what HP knows, we’d be three times more productive.” This is a quote attributed to Lewis Platt who was Chief Executive Officer of HP between 1993 and 1999 and is well known amongst knowledge management professionals.² 

It is no secret that organizations create and retain a lot of knowledge. The larger the organization the more knowledge. But more knowledge can often add to the problem—understanding what is available can be very difficult. As Lewis Platt suggested, organizations do not know what knowledge they have. Knowledge becomes siloed across the different systems that permeate the organization and pulling it together for specific purposes becomes very difficult.  

Traditional search might be able to help you find something specific within your organization by referring to a particular document. It will even guide you to the source document where the information can be found. But what if you want information from across multiple documents? Or you want the information formatted in a particular way, like providing information in a tabular format? 

Again, this is where generative AI changes things. 

Microsoft Copilot for Microsoft 365 can work across Microsoft 365 applications—Microsoft Word, PowerPoint, Outlook, Excel, and others—to analyze, provide insight, and pull together information allowing you to access and manage all your content in one place. 

While this approach allows you to look across documents you and your colleagues are using today, organizations are also seeking to unlock data in documents going back many years. Examples include understanding recipes and ingredients previously experimented with; attaining insight into previously run marketing programs or attaining perspectives on previous supplier negotiations in preparation for upcoming discussions. These are all use cases where the knowledge is spread across disparate locations and systems. 

Already, several organizations have used generative AI to help improve the employee experience. Heineken, for example, has used Azure OpenAI Service and its built-in ChatGPT capabilities to build chatbots for employees, while also using other Azure AI Services to bring innovation to existing business processes.  

Customer conversational experience

Solving a problem for your customer is a major way to differentiate your business from that of the competition. 

A few years ago, when bots emerged, they offered the opportunity to allow a customer to get help without the need for a human. But the challenge was always that bots were limited by the topics and actions that your bot was configured for. In-short, they did not feel human enough. 

Consumers often want help, advice, or inspiration with their purchases but without visiting a store this can be tricky. These ‘human-like’ interactions are so important that stores have invested heavily to save store associate time—freeing them to help customers.   

Online this becomes difficult. But what if you could replicate a human expert who can help, advise, and inspire? One which could be available 24 hours a day to all your customers online?  

This is where generative AI can power and dramatically enhance your Customer conversational experience. 

In January 2024, Microsoft launched (in public preview) a copilot template on Azure OpenAI Service to build more individualized shopping experiences across existing web sites and applications. With this capability, retailers can build advisor type experiences for their customers who can engage in helpful and natural conversations and be guided to precisely the product they need. Help, advice, and inspiration all in one place.   

Illustrating how this approach can differentiate, Carrefour launched their Hopla bot to help with what many consider a difficult domestic task—menu planning. After selecting the store where you want to do your shopping you can ask Hopla for a meal idea, based on your family size and budget.  When you are happy with the suggestion the ingredients are displayed, considering assortment and availability at your chosen store. From there you can even add the products to your basket and transact for delivery or pick-up. 

Carrefour built this using Azure OpenAI Service to access OpenAI’s GPT-4 technology. The solution respects confidentiality and compliance—leveraging Microsoft Azure data security, reliability, and confidentiality features, to ensure compliance with general data protection regulation (GDPR).³ 

Hopla is a great example of how AI can enhance customer experience and convenience, while also boosting sales and loyalty for retailers. By using OpenAI’s GPT-4 technology, Carrefour was able to create a bot that can generate natural and relevant meal suggestions based on user preferences and store availability.⁴ 

When they announced the launch, Carrefour said that customers will be “able to use this natural-language AI to help them with their daily shopping. They will find it on the site’s home page and will be able to ask it for help in choosing products for their basket, based on their budget, food constraints they may have or menu ideas.”³ 

This is a great example of how AI can help retailers differentiate themselves in a competitive market and offer personalized solutions that meet customer needs. 

Generative AI provides more possibilities than can be addressed in a series of blogs. Understanding what others have done can help guide your thinking and approach. The level of creativity increases daily, and we will all watch the space with anticipation of the most impactful use cases for retail and consumer goods companies. 

Transform your business with AI solutions from Microsoft

Visit the Microsoft Cloud for Retail website to learn more about how AI and generative AI capabilities are helping retailers and consumer goods organizations transform their businesses. Learn about Microsoft’s commitment to making sure AI systems are developed responsibly and in ways that warrant people’s trust.

¹The economic potential of generative AI: The next productivity frontier, McKinsey.

²New technologies to take knowledge management in procurement to the next level, CPOstrategy.

³Carrefour integrates OpenAI technologies and launches a generative AI-powered shopping experience, Carrefour Group.

⁴Hopla,Carrefour.fr.

The post Threefold revolution: The influence of generative AI on retail and consumer goods appeared first on Microsoft Industry Blogs.

In a world where market complexity and disruptions are common, retail organizations must learn to navigate and be ready to adapt to new challenges. Retailers seek to get ahead of supply chain disruptions, embrace workforce transformation, and address economic uncertainties—all in the context of a “new era of AI” that has emerged with generative AI, where technology serves a pivotal role in future-proofing businesses.

Microsoft Cloud for Retail accelerates business growth by providing retail-specific capabilities across the Microsoft Cloud portfolio to seamlessly connect your customers, people, and data. Together with Blue Yonder, Microsoft’s generative AI-powered scenarios are enabling retail organizations to create agile, resilient, and sustainable supply chains by connecting data across their ecosystems to identify issues and optimize performance. Microsoft and Blue Yonder have been at the forefront of a cognitive revolution of supply chain innovation, laying the foundation for a truly intelligent, autonomous supply chain, with a predictive and generative AI copilot, delivering faster and better decision making.

Retail and consumer goods companies are turning to AI, including generative AI, predictive AI, machine learning, and automation, to respond faster and in an agile and scalable way to a myriad of problems. These emerging technologies reimagine the user experience, unlock productivity, and drive greater efficiencies in a way that was previously not possible. Harnessing AI, machine learning, and generative AI in this way affords greater visibility and insights into the next best actions. When integrated into retail planning and execution workflows, generative AI can transform the way teams respond to evolving market dynamics, continuously adjusting decision-making based on demand and supply signals, while considering exponentially more scenarios in a fraction of the time it would take the team to compile.

Microsoft Cloud for Retail

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Gain efficiency and profitable growth with the power of AI and machine learning for retailers

In the evolving landscape of supply chain management, the integration of AI is becoming a popular strategy for enhancing efficiency and innovation. While the journey of implementing AI and machine learning technologies can be challenging, with some initiatives possibly not fully achieving their expected outcomes, this doesn’t detract from the potential value AI brings to the table. The effectiveness of AI doesn’t solely rely on its application to existing processes but rather on a transformative approach towards how these technologies are embedded within the organizational fabric. Embracing AI is about more than just technological adoption; it’s about reshaping the foundational elements of workflows and processes to truly leverage the power of predictive insights, automation, and data-driven recommendations.

Traditional, linear supply chains, business units and even data currently exist in silos, perpetuated by the inherently disjointed nature of how supply chains were originally constructed: every function contained within its respective walls. These silos can lead to slower decisions and hinder collaboration throughout the organization. Each functional team lacks visibility beyond their sphere of influence, and often doesn’t understand the impact of their actions on other business areas. When AI and machine learning are applied in this environment, quick decisions can conflict, and optimized key performance indicators (KPIs) can cancel each other out. It is therefore imperative that organizations centralize their data, standardized into a single data model that is ready for AI and machine learning consumption, and connect their workflows to reap the full benefits of AI and machine learning. Once connected, businesses can leverage AI to orchestrate the entire supply chain, allowing visibility and collaboration between all functional teams, all driving toward common goals: to fuel profitable growth and delight the end customer.

Unlocking the power of AI

There’s a lot of hype around generative AI right now, and for good reason. Its inherent creativity, speed, and automation have been viewed as a potential way to replace labor, but we would caution that view is an incomplete view of what this emerging technology can do and how it can be applied. Rather than seeing the technology as competing with human workers, generative AI offers an unprecedented opportunity to enhance industry expertise and experience, unlocking levels of productivity.  

Imagine: instead of having to search multiple systems to find the answer to your question, you have a dedicated assistant that can access the far reaches of your company’s system-wide knowledge, and in a moment return any answer to any question—in simple, everyday terms. Think about the impact this would have on your team’s productivity, quality of performance, and overall satisfaction. Or how this could dramatically accelerate the onboarding of new team members. The opportunities are endless, and as more use cases are discovered, generative AI can and should be used to supplement or redeploy the efforts of human workers in ways that empower and enable teams, not supplant the workforce. While these technologies cut down on manual processes, saving time and effort while providing robust recommendations and thorough data, the human touch will always be necessary to some degree. 

Adapting to new information and updates is vital to controlling your supply chain and making the best choices, and AI holds the key to greatly improved shopper experiences. By using automation and AI, stores can stay ahead of customer demand and keep their shelves stocked with the right product at the right time, and at the right price. Applying predictive analytics to internal and external data can help identify potential disruptions before they happen, empowering teams to proactively respond before it impacts end customers. And bringing holistic data from your warehouse, logistics network, staffing plans and more together with market conditions, seasonality, weather, and traffic patterns can provide a 360-degree view of how shoppers think, act, and what they will experience as consumers.  

By partnering with Blue Yonder and their strategic services team, an iconic fashion retailer will be able to align sales demand forecasting and replenishment by using Cognitive Merchandise Financial Planning throughout their supply chain to improve agility and efficiency. After introducing Cognitive Merchandise Financial Planning, the retailer will reduce time spent on set up and maintenance tasks like adjusting to trends, seasonality, and more. Because the solution dynamically adjusts to the latest marketing needs, there will be improved decision-making speed and automated accuracy, resulting in better performance and greater collaboration across teams while using fewer resources and improving global inventory control.  

Microsoft and Blue Yonder taking retail planning to new heights with AI and machine learning

Blue Yonder and Microsoft are transforming the way supply chains are run. The Blue Yonder Luminate Cognitive Platform, which runs on Microsoft Azure, is embedded with AI and machine learning and serves as the foundation for all systems and applications. Retailers can spin up unconstrained computing power to run hundreds of simulations in a matter of minutes, versus in a few hours—or days. Blue Yonder’s solutions also run on a single source of truth, eliminating batch so teams don’t have to sacrifice accuracy for speed. As a result, retailers are collapsing the time horizon between planning and execution to nearly zero, while working synchronously across their supply chain. Integrated generative AI serves as a force multiplier for productivity so teams can do more important things more frequently and drive continuous optimization. To enable this transformation, Blue Yonder recently announced the launch of two next-generation planning solutions for retail, Cognitive Demand Planning and Cognitive Merchandise Financial Planning, as well as its generative AI solution, Blue Yonder Orchestrator.

The Blue Yonder Cognitive Demand Planning solution utilizes patented Blue Yonder algorithms and machine learning models to forecast, shape, and sense demand while collecting inputs from all key stakeholders to produce an optimized plan. These capabilities reduce the effort required to simulate drivers in real-time while managing more complex scenarios, resulting in faster and more accurate results. By seamlessly bringing together AI and machine learning driven capabilities, Cognitive Demand Planning empowers teams to respond faster to problems while building supply chain resilience and managing more complex scenarios, providing a leg up for demand planners to deliver higher plan accuracy and more relevant AI-driven insights.

Traditional merchandise financial planning can be a manual and reactive process. Cognitive Merchandise Financial Planning from Blue Yonder solves these problems and more. Blue Yonder can take your manual process and transform them into long-range planning and workflows that span across stores, e-commerce, wholesale, and more. This planning solution adjusts to fit your needs, with planning processes that can be configured based on your priorities and business objectives. To more accurately predict upcoming challenges, AI-enabled ‘what if’ scenarios allow a multitude of roles in the company to make quality decisions based on all available data, enabling a true omnichannel planning process. Cognitive Merchandise Financial Planning also makes it easier to analyze data after the fact—from large aggregations to slicing down to granular data—or continuous learning and optimization.

Integrated within the Luminate Cognitive Platform, Blue Yonder Orchestrator brings together the power of generative AI, the natural language capabilities of large language models (LLMs) and the depth of Blue Yonder’s supply chain expertise to unlock the full value of data. This unique solution delivers dynamic decision-making and orchestration by allowing users to query in everyday language then providing recommendations and insights in an easy-to-use format. This approach accelerates the learning curve for new employees while increasing overall productivity by serving up insights and guided recommendations without needing to navigate multiple software applications. Because Blue Yonder Orchestrator is embedded within the Luminate Cognitive Platform, which runs on Azure, it inherently benefits from robust security measures, auditing, reliability, and cost control. With Blue Yonder Orchestrator, companies can also establish guardrails based on user permissions, protecting data access without inhibiting performance. No matter what your focus is, using generative AI can bring you closer to your goals.

Getting started with AI and machine learning

At the end of the day, these emerging technologies have been proven to help accelerate better business outcomes when the right foundation is in place. Supply chains are an excellent place to incorporate predictive AI, generative AI, and automation due to the number of moving parts and the massive amounts of data generated. AI can unlock the complete value of data in near real-time for better insights to increase efficiency of decision-making. For example, while a person can only create so many scenarios in a day, AI’s unconstrained computing powers enables it to process hundreds of scenarios in minutes. These data-driven insights allow teams to focus their time on making value-added, high-impact decisions rather than on manual data entry and analysis.

AI, machine learning, and automation are great tools that can act as a force multiplier for supply chain efficiency and profitability. While new solutions are cropping up every day, it’s imperative to look for business applications that run on a centralized, cloud-based platform, a single database, have connected workflows, and have AI and machine learning embedded throughout. With this foundation, retailers will be empowered to break down existing silos, foster both inter-and intra-enterprise collaboration, and drive their businesses towards a future of greater resilience and sustainability.

Explore solutions from Microsoft and Blue Yonder

• Learn more about Cognitive Demand Planning from Blue Yonder.
• Learn more about Cognitive Financial Merchandise Planning from Blue Yonder.
• Learn more about Blue Yonder Orchestrator.
• Build your own copilot and generative AI applications with Azure OpenAI Service.
• Microsoft introduces new capabilities that help retailers bring AI to the shopper journey and enhance store associate experiences.
• New era of value realization is here—put your data to work with AI.

The post Unlock the full potential of your next-generation supply chain with Microsoft and Blue Yonder appeared first on Microsoft Industry Blogs.

Sitecore, a leading provider of end-to-end composable digital experience software, is partnering with Microsoft to support retailers as they transform their business. The Sitecore OrderCloud connector (preview) is uniquely positioned as the first partner-developed commerce platform integration in Microsoft Fabric, which can help retailers speed up their ability to deliver AI-enabled digital experiences.

Unifying data for actionable AI insights

For retailers to truly deliver personalized experiences through generative AI for their customers, the first step is to break data silos within their organizations and get a holistic understanding of their data estate. A unified data platform is the key to unlocking deeper insights and gives retailers the ability to drive more meaningful experiences for customers. Retailers can open the door to next-generation retail data solutions and more actionable insights with a unified data estate. Achieving data compatibility is also the first step to getting value from AI investments. With retail data solutions in Microsoft Fabric, we are giving customers a single place for all their data analytics.

Retailers can build an AI-ready data estate with retail data solutions in Microsoft Fabric by:

• Unifying retail data from disparate systems: Simplify retail data integration and use the same copy of data without needing to move it from its original source.
• Creating new shopping experiences: Accelerate time to market with application templates purpose-built for the most common retail use cases and powered by AI.
• Reimagining retail with next-generation AI: Use custom models to improve shopper recommendations, optimize product placement, store layout, and more.

Partners like Sitecore connect to and build on top of the platform to further help retailers overcome data incompatibility and unlock new capabilities in Microsoft Cloud for Retail. Sitecore offers an ecosystem of diverse solutions designed to enable retail customers to power commerce and orchestrate content intelligently. The Sitecore content-to-commerce portfolio is built on Microsoft Azure, using the cloud platform’s ecosystem of scalable storage, automation, and application deployment across the globe. Sitecore’s OrderCloud connectivity in Microsoft Fabric gives customers instant access to AI-enabled commerce, as well as data and analytics tooling and more, to empower leaders to enhance and scale the delivery of these channel-less commerce experiences customers expect.

Unlocking the power of AI to enhance digital experiences

The Sitecore OrderCloud connector in Microsoft Fabric helps retailers unify product, customer profile, and order data seamlessly across digital channels on a common industry data model—making it easier to take advantage of AI-powered enhancements, analytics tooling, and commerce and business administration capabilities. This ultimately helps brands deliver the unified, channel-less experience that meets today’s shopper expectations.

“We can’t forget what AI stands for—Artificial Intelligence. To effectively employ AI in your retail business, you need to equip AI with your customer data, product information, and business transactions to start. This is what Sitecore and Microsoft built together for retailers: Every customer, every product, every transaction, every channel and touchpoint—all of your data available in Microsoft Fabric in real-time. This enables retailers to adopt AI at disruptive rates, delivering transactable, AI-powered experiences, such as shopper copilots or conversational commerce.” 

Jake Hookom, Vice President of Product, Sitecore OrderCloud

Traditionally, commerce platforms only served foundational needs like launching a storefront, automating operations, and reducing friction. The focus has shifted, catering to customer demand for seamless, authentic shopping experiences that do more than just sell a product. Customers want personalized experiences that both assist and delight. Sitecore OrderCloud helps retailers use cloud-native and composable architecture that easily evolves, scales, and helps harness the power of application programming interfaces (APIs) to support a full tech stack. Adopting a channel-less approach for customer experience is crucial, and AI is indispensable for efficiently delivering interconnected, personalized digital experiences—making it a strategic necessity for brand success. Together, Microsoft and Sitecore are dedicated to delivering AI-powered enhancements that will lead to: 

• Improved consumer engagement: By automating asset creation for advertising and campaigns, innovating with smart, user-generated content, and managing brand presence and representation across channels. 
• Intelligent marketing and store operations: By improving customer experience through more in-depth analysis of customer calls and complaints through automated summaries, and by enhancing employee experience by automating report generation and workforce scheduling for store managers. 
• Streamlined back-office management: By improving response time and accuracy of internal communications, IT and HR helpdesk tickets, and procurement matters. 
• Automated innovation: Automating everything from product descriptions to marketing emails, onboarding training, and enablement of employees. 

Discover retail solutions 

• Learn more about Sitecore OrderCloud connector today.
• If you are a partner and would like to start working with Microsoft Cloud for Retail, please fill out this form for more information.  

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The post Unleashing the power of AI and unified data with Microsoft Cloud for Retail and Sitecore appeared first on Microsoft Industry Blogs.

• Microsoft Cloud for Retail
• Azure Data Manager for Agriculture
• Microsoft Cloud for Financial Services
• Microsoft Cloud for Sustainability
• Microsoft Cloud for Healthcare
• Microsoft Cloud for Nonprofit
• Microsoft Cloud for Sovereignty

During this wave, we continue to invest in generative AI and copilot solutions—these solutions include customer and partner feedback across industries. Our global partner ecosystem builds and extends our first-party offerings, enabling high-value scenarios for our customers. Read on for a summary of each industry’s release plan and learn about supporting resources. 

Microsoft Industry Clouds

Discover new release plans for capabilities, products, and services

Explore updates

Microsoft Cloud for Retail  

The power of generative AI is transforming the way retailers are managing their businesses, engaging customers, and how they empower frontline workers to work more effectively. Microsoft Cloud for Retail 2024 release wave 1 includes additional capabilities for our retail customers along with considerable improvements to existing in-market solutions.  

The copilot template for personalized shopping on Azure OpenAI Service (preview) enables retailers to build tailored shopping experiences that allow consumers to shop using natural language. Using a retailer’s current systems and data, this copilot template can be embedded into existing experiences—such as a website or app—making it easier for shoppers to find and purchase the products they want. With the copilot template, retailers can offer customers personalized expert advice as well as guide the shopper to find unexpected items and learn more about the retailer. 

Copilot template for store operations on Azure OpenAI Service (preview) has been updated to drive more synergy and integration with modern work frontline worker applications and address customer feedback and requests. This AI-powered copilot template offers store associates on-demand access to store operational policies and tools to learn from customer behavior. Using key data points, employees can quickly and easily make adjustments that improve shopping experiences and drive revenue. 

Lastly, with Retail data solutions in Microsoft Fabric, retailers can plan, architect, and design data solutions for retail data governance, reporting, business intelligence, and advanced analytics. With Sitecore OrderCloud connector, retailers can bring commerce data from the connector into a standardize format and draw actionable insights. And with frequently bought together functionality, organizations can leverage insights and analytics to improve product upselling, shelf optimization, and drive operational efficiencies. 

Azure Data Manager for Agriculture 

The agriculture industry is at the heart of human civilization, and as the world’s population increases, so do the demands on farmers across the globe. Azure Data Manager for Agriculture empowers customers and partners to innovate using high-quality data that is no longer siloed, providing industry-specific data connectors and capabilities to unify farm data from disparate sources, enabling organizations to leverage high quality datasets and accelerate the development of digital agriculture solutions. With new large language model (LLM) APIs, others can develop copilots that turn data into insights on yield, labor needs, harvest windows and more—bringing generative AI to life in agriculture. The 2024 release wave 1 will provide key enhancements for analytics scenarios to align with leading industry standards. Enhanced ability for data set curation, compatibility with industry leading analytics services, more powerful geospatial scenario support, and repeatable data transformation workflows all work together to provide rich new analytics and AI capabilities. 

Microsoft Cloud for Financial Services  

Built on a foundation of intelligence, security, and compliance, Microsoft Cloud for Financial Services provides a powerful and flexible platform that helps unlock business value and deepen customer relationships. Financial services organizations can rely on the broad capabilities of the Microsoft Cloud as well as industry-specific solutions from our global partners to achieve impactful business outcomes quickly. Microsoft Cloud for Financial Services optimizes internal and external business processes through integrated collaboration and omnichannel communications capabilities. It enhances the customer experience through comprehensive customer insights and personalized, intelligence-driven interactions and helps accelerate products to market, removing data silos to turn insights into action, while defending against financial crime and supporting compliance needs. The 2024 release wave 1 plans include release of Meeting Prep for Financial Services—easing the burden of data discovery and curation for investment bankers in managing customer meeting preparations.  

Microsoft Cloud for Sustainability  

Microsoft Cloud for Sustainability empowers customers and global partners to reach their sustainability goals by providing a digital structure to help enable environmental, social, and governance (ESG) data intelligence, deliver sustainable information technology (IT) systems, improve ESG performance, unlock operational efficiencies, and unlock sustainable growth. We provide a digital structure to help enable ESG data intelligence, deliver sustainable IT systems, improve ESG performance, and unlock sustainable growth. This wave 1 release reflects our commitment to expanding the depth and breadth of our offering with new and enhanced features in Microsoft Sustainability Manager, including large language model (LLM) based Q&A and qualitative summary for ESG reporting. LLMs provide efficiencies by comprehending intricate queries, extracting relevant data, and offering meaningful, actionable information. Product carbon footprint data exchange, discovery, customization and testing of custom models and factor libraries, and AI-based suggestive labelling on waste data will help organizations measure their carbon or waste to take steps on report and reduce their environmental impact. 

Microsoft Cloud for Healthcare 

Microsoft Cloud for Healthcare provides capabilities to manage health data at scale and makes it easier for healthcare organizations to improve the patient experience, coordinate care, and drive operational efficiency. For the next release wave, our investments are in continuing to improve our data model, supporting additional fast healthcare interoperability resources (FHIR) in Virtual health data tables, and updates to healthcare data solutions in Microsoft Fabric.  

Microsoft Cloud for Nonprofit 

Microsoft Cloud for Nonprofit Community Training became generally available globally in December 2023. Community Training, an Azure-powered platform, enables organizations to build equity in skilling and deliver training to communities of any size, anywhere in the world through its mobile-optimized and customizable, white-label style platform. Community Training can be used by nonprofits and community programs to empower facilitators, train communities and volunteers, and to deliver education, health, or volunteer services in the field utilizing organizational-created content, with learner tracking and assessments, all while supporting low-bandwidth functionality for offline learning. This solution is part of Microsoft Cloud for Nonprofit, in our “deliver programs in time and at scale” narrative and will be available in both a nonprofit and a commercial version for other sectors. Additionally, Microsoft released new functionality in Microsoft Cloud for Nonprofit, specifically an AI-powered model called Likelihood, to donate built into Fundraising and Engagement. The AI model utilizes an organization’s donor information to support a nonprofit’s understanding of who is most likely to give to programmatic initiatives based on an array of donor characteristics—including past donations, event interactions, and more. Utilizing the model can support the development of donor segments, helping nonprofits target interactions based on their needs and donor giving patterns currently and into the future. Finally, we will be releasing the French localized version of Fundraising and Engagement allowing for greater functionality and global use. 

Microsoft Cloud for Sovereignty 

Microsoft Cloud for Sovereignty is now generally available across all Azure regions, we’re also announcing new capabilities moving into preview. These solutions underscore our continued investment in a rapid pace of innovation to advance sovereignty in the hyperscale cloud: 

• Drift analysis capabilities: Ongoing administration and maintenance can potentially introduce changes that don’t comply with policies, resulting in the deployment beginning to drift out of compliance over time. The new drift analysis tool inspects your deployment and generates a list of non-compliant settings, as well as a severity rating, making it easier to identify any discrepancies to remediate and verify the compliance of specific environments.  
• Transparency logs: Gives eligible customers visibility into the instances where Microsoft engineers have accessed customer resources through just-in-time (JIT) access, most commonly in response to a customer support request. With this update, customers can now request access to the preview feature through the Azure portal.  
• New configuration tools in the Azure portal: Allow customers to create a new sovereign landing zone in two simple steps using a guided experience. 

Learn more 

• Stay up-to-date on the latest release plans on Microsoft Learn.
• Follow the Microsoft Industry Blog for industry-specific news.
• Read more about what’s upcoming on our Microsoft Industry Clouds documentation page.

Note: Some of the functionalities described in this release plan have not been released. Delivery timelines may change, and projected functionality may not be released (see Microsoft policy). 

For a list of the countries or regions where Dynamics 365 business applications are available, go to the International availability guide. For more information about geographic areas and datacenters (regions), go to the Dynamics 365 and Microsoft Power Platform availability page. 

The post Explore the latest release plans for Microsoft Industry Clouds appeared first on Microsoft Industry Blogs.

The importance of data in AI-driven retail fuels the algorithms, insights, and personalized experiences that are increasingly integral to success of the modern retailer. The ability to collect, harmonize, and derive meaningful insights from vast amounts of data empowers retailers to make informed decisions, create personalized experiences, and stay competitive. Understanding that data is key to success, we’ve also released retail data solutions in Microsoft Fabric—helping retailers to standardize and enrich raw data with connectors to engage customers more effectively. Read on to learn how you and your employees can maximize benefits from your data.

Microsoft Cloud for Retail

Become a resilient retailer and drive sustained profitability and growth

Discover solutions

Optimize shopper experiences with personalization  

Generative AI models have vastly improved the experience of chatting with virtual assistants—providing expert advice and bringing ease and convenience to online shoppers. The copilot template for personalized shopping on Azure OpenAI Service (preview) enables retailers to build tailored shopping experiences that allow consumers to shop using natural language. Online shopping is now akin to consulting with a specialist in a store. Personalized shopping taps into the retailer’s vast expertise and knowledge base to apply across shopping situations with the convenience of anytime, anywhere availability. Using a retailer’s current systems and data, this copilot template can be embedded into existing experiences, such as a website or app, making it easier for shoppers to find and purchase the products they want. With the prebuilt copilot template, retailers can offer customers personalized expert advice as well as guide the shopper to find unexpected items and learn more about the retailer. For example, a shopper going camping for the first time asking for clothing recommendations will get suggestions for clothing as well as complementary gear with personalized advice based on the customer’s history and preferences, such as color, style, and design and cross referenced with publicly available information such as destination, weather, and time of year as it relates to the customer’s trip.

The copilot template is designed to be easy for any retailer to use. Its open architecture lets each organization leverage existing investments in commerce, inventory, and personalization solutions to support a wide range of buying environments, including e-commerce sites and mobile apps. Designed to have interactive discussions with shoppers, the copilot template can be adapted to each seller’s brand and values, responds to shopper questions, and facilitates decision-making to create a personalized experience that feels natural and easygoing. 

Increase productivity and efficiency with copilot template for store operations   

Whether associates are long-term employees or seasonal workers, empowering the workforce and optimizing their performance is critical to customer satisfaction and retailer success. With the new generative AI-powered copilot template for store operations on Azure OpenAI Service (preview), retailers can build technology that allows frontline workers to quickly and easily access the information needed for their work day, increasing productivity and efficiency with text and voice commands.  

Retail frontline workers can easily get answers to questions on store operating procedures like how to handle refunds or set up product displays. Using natural language, associates can ask questions about the product catalog to support shoppers, review HR policies and benefits, or complete surveys. Copilot template for store operations enables workers to independently manage issues such as addressing damaged items. For example, with a few prompts, an associate can ask copilot template for store operations for the company’s standard operating procedure for replacing damaged products. In addition to getting the right steps to handle the process, the associate can also create a task to replace products and send an alert through Microsoft Teams to the store manager. Copilot template for store operations reduces the need for pen and paper, data in multiple systems, or the retail frontline tracking down the store manager for every problem that arises.  

Likewise, the copilot template for store operations helps store managers optimize the work experience for their associates and deliver improved customer experiences. They can ask Microsoft Teams for a summary of key performance indicators (KPIs)—such as sales, inventory, and customer feedback. Using copilot template for store operations, a manager can quickly create tasks and assign associates to address. Copilot template for store operations provides retailers insights and information in the normal flow of work, helping managers and associates increase productivity and responsiveness. Finally, the copilot template, when used in combination with Microsoft Power Platform and Microsoft Dataverse capabilities empowers customers to seamlessly connect data across multiple line-of-business systems to generate AI-driven recommendations and actionable solutions that will help store associates make the right decisions.

Gain actionable insights fast with retail data solutions in Microsoft Fabric

Microsoft Fabric is a complete analytics platform built for the era of AI. It enables the transformation of data into predictive insights, to ensure better business outcomes and reduce costly data replication and movement to support data analysts and data scientists. With these solutions in place, organizations can also automate duplicative tasks resulting in data efficiency and resource management.   The self-serve ability enables organizations to equip everyone in the company with their own access to these powerful analytics. Underscoring all of this is a commitment to help keep data secure and protected in a single source while meeting stringent compliance requirements.  

Retail data solutions in Microsoft Fabric are a set of industry-specific capabilities that enable customers to accelerate time to insight generation by unifying, enriching, and modelling industry data in Microsoft Fabric​. Organizations can plan, architect, and design data solutions for data governance, reporting, business intelligence, and advanced analytics. Using data definition, formats, and storage enhancement, a standardized data model is created to help organizations derive actionable insights from large volumes of retailer data. Retail data solutions in Microsoft Fabric provide seamless integration across different systems and applications. By establishing a foundation for naming, data classification, and access controls, retailers can be assured effective governance of their data. 

Sitecore OrderCloud connector enhances your digital capabilities 

Sitecore OrderCloud connector, one of the key capabilities of retail data solutions, gives you a quick and reliable platform to get commerce data onto Microsoft retail industry standard schema. The connector performs transformation and orchestration on top of the data from Sitecore OrderCloud to align with the data model. The connector works across three key data sets:   

1. Products: All the sellable products available in Sitecore OrderCloud. 
2. Customers: End customers who are shopping from your e-commerce portals. 
3. Orders: Customer generated online sales orders.  

The key role of the connector is to get data into the retail data solutions schema, giving users the benefit of access to solutions that are built on top of retail data solutions. Microsoft Power BI reports on sales, products, and customer data solutions can be enabled on top of Sitecore OrderCloud data out of box. 

The connector ensures that no personal customer information is transferred when using Dataverse and Microsoft Power Platform capabilities such as Power BI reports and other related solutions. The Power BI reports provide actionable insights on top of data. Each report provides a specific view of data across sales, customers, and products that can help make impactful merchandising, marketing, and supply chain decisions.  

“Sitecore OrderCloud delivers limitless commerce capabilities and opportunities to consolidate commerce channels into a single managed marketplace. The out-of-the-box integration with Microsoft Fabric unlocks the ability to be a forward-looking AI retail organization. Retail organizations can benefit from stateful services, reliable messaging, orchestration, monitoring, and security, while continuing to focus on their roadmap and strategic business objectives.” 

Steven Davis, Vice President of Engineering Commerce, Sitecore 

Make strategic product decisions with frequently bought together

Lastly, with this release, retailers can make informed decisions about product placement and promotions when using the frequently bought together application model for Smart store analytics. This model provides insights and recommendations and applies data science to unearth deeper insights into store performance. Retailers can forecast foot traffic, group products that are frequently bought together and understand products substitutions to optimize store operations and increase customer satisfaction.

Frequently bought together analytics enables retailers to: 

• Find the top revenue products and boost cross-sales by putting identified products closer. 
• Check the lowest revenue products and decide which ones to drop from the product catalogue. 
• Measure how past marketing actions, like new shelf layout or promotion performance, affected the sales of each item in a product group by comparing before and after sales revenues when purchased together. 

Learn more

• Join us at NRF 2024 from January 13 to 16, 2024 in New York City, New York to learn more about Microsoft Cloud for Retail’s cutting-edge AI technologies and how they can optimize your business.
• Join Shelley Bransten at the Microsoft Retail Digital Forum on February 6, 2024 for the How AI Unlocks Value for Retail session to learn about the research and gain insights into what is driving AI transformation among leading retailers.
• To get started, you can sign up here or learn more about our latest capabilities on the Microsoft Cloud for Retail homepage. 

The post Microsoft Cloud for Retail unlocks a new level of productivity with AI appeared first on Microsoft Industry Blogs.