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.

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. 

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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.

It also changed the conversations Microsoft customer-facing teams were having as organizations scrambled to exploit the technology. There are very few customer conversations and major projects where some form of generative AI does not have a part to play. 

Microsoft teams have worked hard to advise customers on the best way forward and there have been lessons learned from all sides. Conversations are now starting to change, however. They are moving away from being primarily focused on ‘What can we do with generative AI?’ toward ‘How should we approach generative AI?’. 

There are some lessons that have been learned in customer interactions that are now being applied to projects to maximize the return on investment for our customers. Each of these four areas you may wish to give consideration to as you explore possibilities within your organization. 

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1. Be clear on the problem 

There can be a temptation to see generative AI as if it were a ‘new toy’ waiting to be played with. It is critical however to be focused on the problem you are trying to solve and to use this to ‘paint the vision’ of what you want to achieve. 

An approach that can work well is to identify several candidate initiatives or use cases where generative AI may be able to help. Examining these more closely could reveal that there are other ways to solve some of them. There may also be some that you can cluster together because they could warrant similar approaches to address their needs. 

Prioritizing based on likely business impact versus the effort required is an excellent way to determine which use case, or use cases, you should start with. This is a technique Microsoft Industry Architects use with customers engaging with both technical and business teams at the same time. Placing each use case on a simple four-quadrant matrix with ‘Effort’ on the x-axis and ‘Impact’ on the y-axis gives a simple yet effective visualization approach to setting priorities. 

When considering impact, consider things like speed or time saving, improved decision making, and cost savings but also consider how long it will take to reap the benefits. When considering effort, think about the time and money you would need to invest to achieve the desired outcome while being conscious of the different systems and personas that would be involved. 

Once you have determined your target use case or use cases, you must be clear about what ‘good’ looks like. This means determining the metrics and values by which you will measure your success. Using quantitative terms can deliver real focus. Your metric could be based on time, effort, or money—but try to make it as measurable and meaningful as possible. 

2. Work inside-out 

As a customer-centric organization, it might feel counter-intuitive to focus internally first. With new innovative technologies like generative AI, this is a great way to try out the technology and your approaches on a ‘friendly’ audience—your own internal teams—before applying to your external customers. 

This also acts as the foundation for a roadmap of projects and initiatives all centered around generative AI. You can think of this roadmap in three phases—which you could align to a ‘Crawl-Walk-Run’ approach. 

Phase 1: Crawl

Use cases inside the organization

This focuses on ‘human in the loop’ reviews of content that is generated. In a retail and consumer goods environment example use cases might include: 

• Summarization and analysis—call center and customer interaction summarization. 
• Categorization—knowledge management and internal communications. 
• Content generation—creating product images for your e-commerce platform or creating product descriptions based on multiple inputs. 

Phase 2: Walk

Use cases that directly interact with employees and customers

In this phase, you may have human supervision of multiple use cases, each providing information to help the employee or customer, human, make better-informed decisions. Retail and consumer goods examples might include: 

• Summarization and analysis—summarizing product reviews, product descriptions, and specifications. A great example of this is how CarMax is creating their used car listings—saving 11 years of content generation effort. 
• Categorization—analyzing social media trends, sentiment analysis, or making product design recommendations. 
• Content generation—personalized user experience or marketing campaign content generation. 

Phase 3: Run

Increased automation 

This final phase focuses on new product offerings or automation that you might provide as direct interaction with your customers. Examples in a retail or consumer goods setting could include: 

• Summarization and analysis—call-center automation and the contextual automation of customer-facing business processes. 
• Categorization—automating the creation of product descriptions for your commerce platform considering consumer feedback. 
• Content generation—automated creation of personalized marketing and brand content. 

3. Be business (problem) led 

Prioritizing use cases based on effort versus impact is likely to ensure you are maximizing your focus on a problem that can deliver real value for your business. ‘Painting the vision’ of what you are trying to achieve remains key here. 

Ongoing input from business teams remains key—like all initiatives where technology plays a major part, the most successful initiatives tend to be where technology and their business counterparts work most closely—all focused on the specific business problem. 

In any technology-focused initiative, people, processes, and technology all need to be considered together and generative AI is no different. Consequently, it is important to not ignore specific elements. For example, as part of your work focused on each use case you should ensure you: 

• Determine the business process associated with your use case. Understand the triggers that will initiate it along with the points of interaction with different systems and personas. 
• Establish clarity on the personas that interact with the process but also understand who will be impacted when your use case is successfully delivered and used in the business environment. 
• Establish how personas who need to be involved will interact with the process—establish when and how they will need to be notified of interaction or approval they need to undertake. This is especially important when considering the responsible AI principle of accountability. 
• Look carefully at your use case from a responsible AI perspective. Using tools like the Microsoft Responsible Impact Assessment could accelerate things for you here. 
• Change management should be examined carefully. Understanding the personas impacted is essential to aid this. Given how new generative AI is and the large amount of information in the media there are sensitivities that may require careful handling. Again, careful application and consideration of the Microsoft responsible AI principles of fairness, reliability and safety, privacy and security, inclusiveness, transparency, and accountability will help you in this regard. 

4. Equip yourself (to follow fast) 

It is good to be excited about the potential of generative AI—according to McKinsey the productivity it could add to the global economy equates to $2.6 trillion to $4.4 trillion annually.² These numbers are significant given that in 2021 the GDP of the United Kingdom was $3.1 trillion. Consequently, it is important to be able to innovate using this technology.   

To do this, you need to build skills inside your own organization to be able to deliver use cases quickly. This means nurturing your own teams and in-house capabilities. 

It could be very tempting to adopt a ‘fast follower’ approach where if you see a competitor launch a generative AI-powered solution that aids their customers you rapidly follow with something similar—or perhaps even better. 

There is nothing wrong with this as a strategy, but it needs the in-house skills and experience to have to be nurtured over time to have the capability to deliver against this. Delivering use cases you have prioritized and innovated against is one of the best ways to nurture the in-house skills you need. 

By reaching 100 million users in 64 days, ChatGPT became the fastest-growing consumer application in history.³ This potentially sets the scene for generative AI to become the fastest-growing technology category ever seen. 

While the overall space in terms of possibilities and benefits is likely to evolve rapidly, looking carefully at these four areas will help your organization learn while effectively harnessing the power of generative AI to deliver value for your business. 

Learn more

• Microsoft Cloud for Retail
• 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 
• How next-generation AI and digital tools transform the retail frontline 
• Microsoft’s responsible AI principles 
• CarMax puts customers first with car research tools powered by Azure OpenAI Service 
• Welcoming the generative AI era with Microsoft Azure 
• Generative AI transforms business processes 

¹ The Guardian, ChatGPT reaches 100 million users two months after launch, February 2023.

² McKinsey Digital, The economic potential of generative AI: The next productivity frontier, June 2023.

³ Reuters, ChatGPT sets record for fastest-growing user base—analyst note, February 2023.

The post From discussion to deployment: 4 key lessons in generative AI appeared first on Microsoft Industry Blogs.

As rising costs, supply chain issues, changing consumer behavior, and other challenges continue to mount, retailers need more intelligent cloud solutions and technology to build resilience and achieve more. To build this new level of agility and adaptability, it’s imperative to find new ways to make data-driven decisions through cloud, AI, and mixed reality solutions. This integration of physical and virtual assets to make data-driven decisions is one of the key capabilities of the industrial metaverse.

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Harnessing technology to meet customers where they are

Consumer behavior has shifted considerably over the last few years, with new patterns of work emerging that put people, communications, and heightened efficiency at the core of what is expected in the retail environment. Seeing and meeting these new opportunities, Microsoft has made significant investments in improving existing technologies, and pioneering new technologies to help our retail organizations better meet the evolving needs of their customers. Retail organizations that are already on this digital transformation journey can protect and extend their investments as they move toward their goals.

One way we are addressing these changing needs is through investments in emerging technologies, such as the industrial and commercial metaverse. In fact, we believe that in the not-so-distant future, the metaverse will play a key role in providing new ways of connecting, working, and delivering services for businesses and retail consumers alike. Our Work Trend Index¹ data shows that 50 percent of Gen Z and Millennials envision doing some of their work in the metaverse in the next two years.

As a platform-based company, our approach is to ensure that the software experiences we deliver can benefit users on all their favorite devices. This is especially true for our customers and partners in the retail industry, where we are making great strides to help them update and even revolutionize the way they train their teams, and how they interact with their customers.

By harnessing new and innovative technologies, like those we discussed at Microsoft Ignite 2022, retail organizations can leverage new approaches to drive results to meet customer expectations and achieve long-term success.

Microsoft and the metaverse 

In Judson Althoff’s keynote at Microsoft Ignite 2022, he highlights many of the ways that companies can leverage these emerging technologies to gain a competitive advantage in the weeks, months, and years ahead.

Indeed, while metaverse for consumer, commercial, and industrial applications are still in their early stages of development and applicability, we are already seeing considerable promise for their deployment on a broad scale.    

One area that shows significant promise is the potential to use metaverse to train employees by creating engaging environments for them to learn, practice, and retain the skills they need. Moreover, with the robust tools, resources, and technology that Microsoft offers, paired with the inherent possibilities of the metaverse, retail organizations can create plans and deployments that push broad organizational learning.

As is the case with all new technologies, the metaverse is not without potential risks, and companies must learn how to manage and operate in a metaverse environment safely. One way to do that is by experimenting internally with your own teams before you create a customer-facing metaverse experience.  

One example of an organization that has created an employee-focused metaverse is Accenture. Called the Nth Floor, Accenture’s enterprise metaverse empowers its geographically distributed workforce to meet, collaborate, and learn in immersive environments. These virtual environments are familiar to employees as they are brought to life via digital twins of its physical offices. Within the Nth Floor, Accenture created a virtual campus called One Accenture Park to help new employees personally connect with the culture and build professional relationships.

In 2022, 150,000 new hires are working from the metaverse on their first day at Accenture. Of course, the metaverse is still in its early days. In fact, according to a Gartner®² report, it will be 10 years before its mainstream adoption. Aside from the need for organizations to learn how to operate and manage, two other things are clear—the requirements in terms of security and computing power will be significant—making the cloud the perfect technology to power metaverse usage. 

A bold new future

Microsoft is working with many retailers on their metaverse future—building accelerators and helping them to plan to maximize return on investment. As we step boldly into this new future, Microsoft is committed to providing our customers and partners the tools and resources they need to take advantage of the many amazing opportunities the metaverse holds.

Learn more

To learn more about Microsoft and the metaverse:

• Explore Microsoft’s pioneering technology, Microsoft Mesh, which enables presence and shared experiences from anywhere—on any device—through mixed-reality applications.
• Microsoft and Meta partner to deliver immersive experiences for the future of work and play—the Official Microsoft Blog.

Every year, Microsoft Ignite is our opportunity to showcase the new and exciting innovations that we’ve developed to better equip our customers and partners to succeed. With the speed of tech and cloud innovation, Microsoft Ignite is the ideal platform to learn about how we are helping organizations unleash value quickly, build for the future, and exceed expectations. 

At Microsoft Ignite 2022, we highlighted new and beneficial ways that Microsoft technology is revolutionizing the retail landscape and embracing the power and potential of the metaverse.

Join Alysa Taylor, CVP, Industry, Apps and Data Marketing, and Corey Sanders, CVP, Microsoft Cloud for Industry and Global Expansion Team, for a panel discussion: Microsoft Ignite Into Focus: Industry Clouds.

And be sure to watch these relevant Microsoft Ignite sessions: 

• Optimize your customer shopping experience with Microsoft Cloud for retail
• Meetings in the metaverse: Overcoming challenges and defining leading edge practices
• Field Workers of Tomorrow: The opportunity in the Metaverse
• Jumpstart your physical operations transformation with technologies built for the industrial metaverse

Follow Microsoft Retail on Twitter and visit our Microsoft Cloud for Retail website to get the latest on how Microsoft and our partners are helping retailers deliver a seamless experience across the entire shopper journey.

¹Hybrid Work Is Just Work. Are We Doing It Wrong? Work Trend Index Special Report.

²Gartner: Hype Cycle™ for Emerging Technologies, 2022, Melissa Davis, Gary Olliffe, July 2022.

GARTNER is a registered trademark and service mark, and HYPE CYCLE is a registered trademark of Gartner, Inc. and/or its affiliates and are used herein with permission. All rights reserved.

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