Digital transformation is essential for a resilient, durable, and sustainable mining sector. Geopolitical volatility and trade uncertainties are disrupting supply chains, while the industry grapples with the challenges of meeting the soaring demand for minerals essential for the energy transition. 

Existing deposits are being exhausted and new deposits are increasingly more difficult and expensive to discover. In 2015, McKinsey & Company reported that worldwide mining operations were 28% less productive than they were a decade prior, even after adjusting for declining ore grades.² Nearly a decade later, the shortage now impacts the availability of metals creating a potential risk for a near-term supply shortfall, particularly for copper, lithium, and cobalt vital to the energy transition. There is an expected supply deficit in critical minerals like copper with a potential shortfall of 9.9 million tons by 2035.³ 

The mining industry also faces a chronic labor shortage which adds even more to its challenges, with 86% of mining executives finding it increasingly difficult to recruit and retain necessary talent.⁴ Amidst these complexities, mining companies are striving to balance productivity and profitability with purpose using cloud-based platforms, the Internet of Things (IoT), mixed reality, and more recently, generative AI.  

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Accelerating digital transformation

In the December 2023 blog, I discussed how mining companies are adopting digital technologies to enable business agility, drive efficiency, and accelerate innovation across the entire mining value chain, from exploration and extraction to processing and transportation. While these efforts have traditionally centered around specific business outcomes, current trends emphasize broader goals such as corporate environmental, social, and governance (ESG) targets and the transition to net-zero emissions. 

Meeting these goals requires a strong data foundation, digital effectiveness, and digital maturity. Transformation starts with technology-savvy leaders who have a grounding in AI and a focus on sustainability. With a vision informed by a clear understanding of their organization’s challenges and opportunities, effective leaders can take a leap forward on their innovation roadmap with solutions like Microsoft Intelligent Data Platform, which provides a single, flexible platform for databases, analytics, AI, and data governance.  

Digital maturity deepens with an empowered, skilled workforce that harnesses AI to make informed decisions and streamline repetitive tasks, gaining more time for value-added activities. For example, in my last blog, I shared how Microsoft Copilot for Microsoft 365 in Microsoft Dynamics 365 Guides combines generative AI with mixed reality to help frontline workers in industrial settings complete complex tasks and resolve problems faster for minimal downtime and accelerated learning. 

Exploring innovation with a future-ready mindset 

At Microsoft, our enduring mission is to “empower every person and organization on the planet to achieve more.” We are privileged to work with a partner ecosystem that shares our vision.  

Digital innovation that stays relevant over time integrates people, processes, technology, and information. Multinational Japanese firm Asahi Kasei Group and ZEAL Corporation showcased that approach by implementing a data management platform based on Microsoft Azure Data Factory, Microsoft Purview, and Microsoft Azure Synapse Analytics. The platform unifies 1,200 systems across multiple diverse operations such chemicals, healthcare, electronics, construction, materials, services, and engineering. By eliminating data silos, the team can gain new insights that unlock business advantages.

Siemens is another great example of enabling people to achieve more. To empower employees, the company created an AI-powered collaboration app based on Microsoft Azure OpenAI Service and Microsoft Teams. Siemens aimed to enhance innovation, efficiency, and problem-solving agility by connecting field and shop floor workers with operations and engineering teams. Now, frontline workers who find problems in the design and manufacturing process can easily connect with engineers to resolve them. 

Employees can receive notifications, create problem reports, and collaborate on tasks on any device. The app provides preconfigured industrial machinery solutions and accelerates knowledge-sharing with an AI-powered natural language interface. For instance, employees can report issues in their own language, which is automatically translated into a common language. 

In my final example, Schieder Electric wanted to speed innovation for key goals such as reducing carbon emissions. With a vast portfolio of connected devices, solutions, and services, AI has become essential for generating data-driven insights and actions The company is using multiple AI services to fast-track innovation, including Microsoft Copilot, Microsoft Cloud for Manufacturing, Azure OpenAI, and Azure Machine Learning. Schneider Electric is using Copilot to automate routine tasks and to offer intelligent code suggestions that streamline programmable logic controller (PLC) programming. The company has also created bots to help with customer service and financial analysis. 

Creating durable innovation for a sustainable future 

Digital transformation can be a long journey, and the pressing issues of today can sometimes overshadow our efforts toward growth and innovation. For durable innovation, we incorporate the McKinsey Three Horizons Model into our digital transformation roadmaps. 

The model provides a structured approach that miners can use to allocate resources effectively, balancing immediate business needs with sustained innovation for future success. Organizations are encouraged to explore new markets and invest in business models, products, and technologies that align innovation programs with future challenges and growth opportunities. As a framework for strategic innovation and growth, Three Horizons Model can support miners during times of change, disruption, and uncertainty.   

In addition to innovation initiatives, we consider critical aspects such as user adoption, change management, change fatigue, organizational capabilities, culture transformation, workforce reskilling, and governance. These considerations are vital for futureproofing the mining enterprise and sustaining digital and AI innovation. 

How Microsoft can help

Digital transformation and AI adoption are poised to revolutionize the mining industry in the next decade and beyond. Microsoft technologies are already making a significant impact, with improvements in safety, productivity, profitability, safety, health, and environmental performance. From clarifying your vision for innovation and identifying top challenges to creating your solution roadmap, a disciplined approach is crucial for continuing this momentum. 

The digital sustainable mine of the future integrates physical, digital, and sustainable elements with information, innovation, and human ingenuity. The adaptive, resilient, forward-thinking mine offers a customizable reference model and roadmap to help mining organizations achieve their vision for the future. 

That vision isn’t just about the outcome, or business impact—it’s about investing in the processes and technologies that enable the mining industry to adapt to change and help us all accelerate toward a sustainable tomorrow.  

Learn more about Microsoft solutions

• Microsoft for Energy and Resources  
• Microsoft Industry Blogs: Energy and Resources
• Microsoft AI solutions  
• Microsoft Azure AI Services  
• Microsoft Intelligent Data Platform

¹Mining’s top ten ESG trends for 2024, Mining.com.

²Productivity in mining operations: Reversing the downward trend, McKinsey & Company.

³Tracking the trends 2024, Deloitte Global.

⁴Has mining lost its luster? Why talent is moving elsewhere and how to bring them back, McKinsey & Company.

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They decided to migrate their 10-year-old architecture and technology stack to a cloud-based solution to support their long-term goals and operate more efficiently. The question was how to make the move while also keeping the power on.  

At Microsoft for Government, we work with organizations and agencies around the world to help solve these kinds of challenges. We help each organization navigate their unique requirements and chart a path that works best for them, balancing the promise of AI and cloud native applications with the need to be more cost efficient, secure, and compliant.  

For Energinet, the solution was to build a new digital operating system based on Microsoft Azure, which is improving efficiency in automating energy balancing processes, lowering costs, and resolving issues in 15 minutes that previously took an hour. Modernizing helped them meet their near-term requirements, and it positioned them to remain agile and open to change as opportunities evolve.  

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Migrate to innovate—the benefits of modernization

In many government organizations, the migration to cloud computing has long been mitigated by important concerns, including cost, security, and a need to maximize legacy systems. As cloud technology has matured, however, the barriers to adoption have declined as the price of inaction has risen.  

With the promise of AI to deliver new efficiencies and opportunities for service improvements, cloud migration is now clearly the best path forward, provided it is done in ways that meet an organization’s unique requirements.  

For government organization, the advantages of cloud computing are compelling, and easier to realize than before. Governments that choose to migrate to Azure are realizing important benefits in many areas of their greatest concerns—among them: 

• Performance and resilience: Azure enables businesses to scale their operations globally with ease, with purpose-built infrastructure, dynamic compute capacity, scalable storage, and real-time disaster-recovery options. Governments such as the State of Alaska are migrating to Azure to achieve their vision of becoming digital public service innovators. To expand access to secure services, Alaska migrated 700 applications and one-third of its infrastructure in just three months. They not only achieved better resilience, cost efficiency, and security, but with the state’s vast geography and often isolated communities, the migration also sparked a cultural shift by bringing agencies together and unlocking unexpected value. 

• Security: To counter the expanding cybersecurity threat facing governments, Azure is supported by more than 8,500 security experts, more than 100 compliance certifications, and a cloud-native application protection platform that spans the application and infrastructure stack. Microsoft plans to invest USD$20 billion in security in the next five years to continue our commitment to a safe future.¹ The importance of ensuring world-class security was a key factor in the decision by Qatar’s Ministry of Communications and Information Technology to digitize government operations with Azure. The ministry established information assurance measures and security programs that not only enhanced the government’s data security and operational efficiency, but also achieved $7.3 million in cost savings. 

• Hybrid and multi-cloud management: Azure supports hybrid, multi-cloud, and edge environments with Microsoft Azure Arc, a solution that allows governments to build applications and services with a consistent development, operations, and security model across deployments. This proved essential for the World Bank, which provides lending services in 189 developing countries around the world to help lift people out of poverty. They wanted to build applications and services that could extract insights from their SQL Server estate and multiple cloud infrastructure providers. Using Azure Arc, they streamlined their cloud migration journey, built new solutions, and gained unexpected efficiencies. 

• Cost savings: By migrating to Azure, customers can optimize costs and resources by consolidating solutions and choosing from a variety of consumption models and flexible migration approaches. For example, the Statistical Office of Republic of Serbia saved time and money by conducting the nation’s first-ever paperless census using a hybrid cloud solution, which reduced the time required to publish official results from 18 months to just six. The solution delivered faster data encoding and more accurate results, and the Statistical Office of Republic of Serbia was able to streamline maintenance while ensuring optimal security, real-time monitoring, and improved data quality.  

A 3-step approach to becoming AI-ready

Modernization, which provides for greater scale, efficiency, and flexibility, also positions an organization to explore the benefits of AI. Governments recognize the potential of AI to generate new cost efficiencies and to power new offerings in service delivery. In the near term, this is motivating many to accelerate their digital transformation journeys. 

Implementing a cloud migration strategy is an absolute prerequisite to adopting and innovating with AI in a government organization. The cloud provides the hyperscale performance required for generative AI functionality, and a modern data strategy not only consolidates disparate data systems but also ensures access control and data security.  

Cloud migration is a long-term process, and the journey is unique to every organization. Whatever the course, governments should remain mindful of the following three steps, which are key to becoming AI-ready in ways that are efficient, effective, and responsible. 

Step 1: Co-locate data and workloads in the cloud

Strategically placing applications, databases, and AI resources into the Microsoft Cloud ecosystem delivers exponential improvements in performance and prepares data and services to take advantage of new AI innovations.  

Step 2: Infuse Microsoft Azure OpenAI Service and copilot integrations

Once your data and applications are co-located, you are ready to take advantage of AI services such as Microsoft Copilot for Microsoft 365, which integrates generative AI into everyday productivity applications, and Azure OpenAI, which enables the development of customized copilots, plugins, integrated AI services, and much more.  

Step 3: Ensure secure and responsible AI

From the outset, AI innovation should be delivered with the highest standards for security, assurance, and trust. Beyond taking a leadership role in ensuring safe, secure, and trustworthy AI at a global level, Microsoft provides comprehensive guidance for governments to ensure secure and responsible AI in their efforts, such as the Microsoft Responsible AI Standard, and Microsoft responsible AI practices.   

Continue your modernization journey and become AI ready 

No matter where your government organization stands in its digital transformation, Microsoft and our network of global partners are ready to help you move forward. For more, please explore the following resources: 

• To learn how Microsoft is helping governments solve society’s biggest challenges, see our Microsoft in Government website.  

• For guidance, expert assistance, and resources to help move to Azure, visit Azure Migrate and Modernize and Azure Innovate.  

• To develop a cost management strategy when migrating or operating in the cloud, see Cloud Migration Cost Management Resources.  

¹Microsoft commits $20 billion to advance cybersecurity following meeting with President Biden, Windows Central.

The post Migrate to innovate: How governments are modernizing in advance of AI appeared first on Microsoft Industry Blogs.

It is a remarkable shift, which I and others in Microsoft for government have gauged over the past year as we’ve worked to help city governments solve their most important challenges through technology. 

At the SXSW 2024 conference, the Esri Infrastructure Management & GIS Conference, and in my recent meetings with city leaders from Canada, Finland, and the Netherlands, generative AI has been at the center of most conversations. This excitement is notable because cities are traditionally cautious about technology adoption for important reasons such as risk, privacy, security, and governance, and many are still working through their cloud migration journeys.  

The potential benefits of generative AI to improve operations and service delivery are too compelling for many cities to ignore. To cite just one example, the City of Kelowna in Canada launched an early AI initiative and is using cognitive search and conversational AI to deliver a 24-hour helpline for its 150,000 residents. Project leaders report that generative AI also enables them to automate and streamline internal processes around data entry and analysis, refactor legacy databases and code, and create new apps in minutes.  

This type of innovation has prompted other cities to explore AI innovation, beginning with creating an effective and actionable plan. 

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Success factors for AI adoption in city governments 

Every city is unique, with its own ambitions for the use of generative AI and its own set of requirements and technology considerations. In our work with cities, we have identified a set of success factors that are common across cities investing time and money in generative AI and are enjoying early success.  

1. Empower the workforce with effective upskilling  

Realizing the value of AI starts with the workforce. According to research conducted as part of Microsoft’s Public Sector Insights on Skilling, the lack of skilled workers is often the number one barrier to AI implementation among organizations worldwide.

The imperative to upskill the workforce is particularly important for cities, whose early use cases usually focus on employee productivity and the internal processes they manage. Well-trained and confident workers also help ensure the success of public-facing initiatives. Executive support is key. Workers are empowered when leadership gives them the direction and license to responsibly use AI tools within the context of their day-to-day work. 

To answer the skilling challenge, cities should invest in learning programs, building public-private upskilling partnerships, and giving people adequate time to gain skills and confidence. An ongoing learning experience platform, such as the one developed by Bank of Canada, can promote a culture of learning. Microsoft offers effective resources and strategies, including the Public Sector Center for Digital Skills, which provides specialized insights, guidance, and content, and Microsoft Learn, which offers customized training options. 

2. Build an AI-ready data strategy 

AI is only as good as the data that is made available to it. In city systems, data is often siloed or locked in spreadsheets or other static locations. A modern data strategy is one that integrates such diverse data sources, ensures data quality, establishes rules and processes for data access and management, and keeps data and systems secure.   

An excellent example of how a complete data strategy can deliver ongoing AI benefits is the Smart Qatar (TASMU) Program built by the State of Qatar. Essentially a service platform built on a common data model across multiple domains, TASMU will empower a broad array of AI applications that are expected to help contribute 2% to the nation’s gross domestic product (GDP).  

This sort of comprehensive data strategy is an important long-term goal, but cities should not wait to begin innovation on AI. Many cities are taking an incremental approach, leveraging the quality data they have in hand, with their existing cloud foundations and data governance standards to experiment with new AI use cases. A careful step-by-step approach will guide your data strategy. 

3. Establish frameworks for governance, compliance, and sovereignty  

Some city governments have been reluctant to use AI due to concerns about security, privacy, and compliance requirements. To address these concerns, cities should establish transparent frameworks for AI governance and assurance. The goal is to identify risks and goals associated with both externally facing and internally focused use cases and codify courses of action to ensure success.  

Ensuring the residency of data within strict geographic borders is a key requirement for many cities. This calls for a solution that can ensure the required level of control of sensitive data while still providing a hyperscale cloud environment for a huge number of applications. 

Microsoft offers guidance for cities to establish AI governance and enhance trust and privacy in AI innovation. For cities with strict data residency concerns, we also offer Microsoft Cloud for Sovereignty, which offers tailored cloud services to help build cloud-based workloads in compliance with specific security, policy, and regulatory requirements. 

Take the first steps in building an AI-empowered city  

For city leaders who want to advance their AI journeys, our experts and industry advisors can work with you to identify potential use cases for early innovation based on your specific goals, requirements, and environmental conditions.  

To learn more about how Microsoft can empower cities and government organizations with technology to help solve society’s biggest challenges, visit the Microsoft for government website, read our Microsoft for Government e-book, or get in touch with your Microsoft representative or technology partner.     

The post How Microsoft empowers city governments on the road to AI adoption appeared first on Microsoft Industry Blogs.

Manufacturing is one of the most data-intensive industries, as massive amounts of data are generated from sources such as sensors, machines, enterprise systems, and human interactions. However, most of this data remains siloed, unstructured, and underutilized, thereby limiting the potential for data-driven insights and innovation. To overcome this challenge, manufacturers need a unified data estate that can connect, enrich, and model data across information technology (IT) and operational technology (OT) systems—enabling easy access and analysis of data for every employee.  

We are announcing the private preview of manufacturing data solutions in Microsoft Fabric and copilot template for factory operations on Azure AI, under the Microsoft Cloud for Manufacturing. These solutions help manufacturers unify their OT and IT data estate and accelerate and scale data transformation for AI on Fabric, our end-to-end analytics software as a service (SaaS) based platform. The copilot template for factory operation on Azure AI helps manufacturers to create their own copilots for their frontline workers using their unified data. They can use natural language to work with the data and handle scenarios such as root-cause analysis, knowledge discovery, training, issue resolution, asset maintenance, and more.  

These solutions, alongside our partners supporting it, will be showcased live for the first time at Hannover Messe Industrial Conference 2024.  

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Manufacturing data solutions in Fabric 

Manufacturing data solutions in Fabric brings together OT data like factory sensor telemetry, and IT data like inventory data, into a unified data foundation in Fabric. The solution extends the value of the data by enriching it with the relevant context, following an industry standard International Society of Automation (ISA-95) information model. For example, the temperature reading of a production equipment at a specific time (OT data), is overlayed with all the information of the batch of material going through that machine, from the production order from your enterprise resource planning (ERP) system, or test result from your quality system (IT data). 

The solution ingests data from different factory sources ranging from the Internet of Things (IoT) devices to systems of records like manufacturing execution systems (MES), and more. The data is stored in Fabric for unification, enrichment, modeling, and aggregation. This unified data foundation provides a scalable and repeatable pattern to tackle all factory domain data projects—accelerating the pace of innovation, freeing up IT resources, and maximizing the value of current technology investments. 

Copilot template for factory operations on Azure AI 

Once the data is enriched and ready for AI, manufacturers can leverage the factory operations copilot template plugin to Azure OpenAI Service, augmented for complex manufacturing systems like MES. The copilot template validates results against the manufacturing data solutions through post processing to avoid hallucinations and provides responsible AI guardrails to ensure only relevant and safe responses are given within the manufacturing setting. The users, such as a Quality Engineer or Plant Manager, can use a custom interface, like a chatbot or a dashboard, to communicate with the data in natural language to obtain insights and make improvement decisions. 

To learn more about technical aspects of these solutions, review our deep dive technical blog.

Realizing the value of factory data with AI 

These solutions are already supporting leading manufacturers globally to tackle complex use cases like production monitoring, waste management, quality management, and worker enablement. 

Intertape Polymer Group increases production performance with factory data and generative AI  

Intertape Polymer Group (IPG) uses Sight Machine’s Manufacturing Data Platform to continuously transform data generated by its factory equipment into a robust data foundation for analyzing and modeling its machines, production processes, and finished products. IPG is now using Sight Machine’s Factory Copilot, a generative AI with an intuitive natural language chat interface, powered by the copilot template for factory operations on Azure AI. This tool facilitates the team’s ability to rapidly gather insights and direct work on production lines which previously operated like black boxes. Instead of working through manual spreadsheets and inaccessible data, all teammates—including production, engineering, procurement, and finance—have better information to drive decisions on products and processes throughout the plant, improving yield and reducing inventory levels.

“Our partnership with Sight Machine and Microsoft is ever evolving and continues to become more important to our daily operations within the plant, facilitating real time decisions on real time data.”

—Bill Bourgeous, Plant Manager for the IPG Tremonton, Utah facility 

Schaeffler, democratizing information access across their factory workforce

Schaeffler, a leading motion technology company, has embarked on a mission to democratize information access across their factory workforce. Employees can gain easy access to key metrics like scrap rates, yields (the proportion of usable or acceptable components), and energy usage over time using the chatbot. This will be vital to help drive cost and carbon reduction co-benefits.

“Artificial Intelligence, and in particular Generative AI, is already having an impact on the daily business at Schaeffler. Especially in the field of manufacturing and operations, the ongoing operationalization of AI solutions, combined with intensive training, enables us to optimize, rethink, and innovate the core of our company—our plants. As a leading motion technology company, Schaeffler has the ambition not only to participate but to proactively shape this ongoing transformation.”

—Stefan Soutschek, Vice President Digitalization and Operations IT, Schaeffler

Bridgestone is creating a unified factory data foundation to enhance product quality

Bridgestone is partnering with Avanade to confront production challenges head-on, focusing on critical issues related to production disruptions and scheduling inefficiencies, like yield loss, which can escalate into quality issues. As a private preview customer collaborating with Avanade, Bridgestone aims to harness the power of manufacturing data solutions in Fabric and the copilot template on Azure AI. Their goal is to implement a natural language query system that enables frontline workers, with different levels of experience, with insights that lead to faster issue resolution. The team is excited to establish a centralized system that efficiently gathers and presents critical information from various sources and facilitates informed decision-making and enhances operational agility across Bridgestone’s production ecosystem. 

“We are excited to accelerate our industrial transformation with AI in partnership with Avanade and Microsoft Cloud for Manufacturing, particularly we recognize the disruptive ​potential of generative AI, and true to our values, we want to be at the forefront of innovation equipping our front-line workers with powerful tools, like copilots, to optimize our operations.“

—Bart Kerhofs, Vice President of IT for Bridgestone, Europe, Middle East, and Africa

Microsoft Cloud for Manufacturing partner ecosystem 

These solutions are enabled by an ecosystem of partners with deep industry expertise. Systems integrators and software vendors enable factory data ingestion from different systems by building custom or proprietary connectors into Fabric—embedding the solution capabilities into their applications or building custom UI experiences for the copilot templates on Azure AI. We want to thank our private preview partners for supporting these solutions and our customers.

• Accenture and Avanade: Private preview implementation partners for manufacturing data solutions in Fabric and copilot template on Azure AI.
• Sight Machine: Manufacturing Data Platform and Factory Copilot on Microsoft Cloud for Manufacturing.
• Litmus Automation: Litmus connector.
• AVEVA: AVEVA Connect and AVEVA Data Hub on Microsoft Cloud for Manufacturing.
• Rockwell: Integration of Rockwell’s Plex Smart Manufacturing Platform with FactoryTalk DataMosaix and Microsoft Cloud for Manufacturing. 

To learn more about each of our partners, review the deep dive technical blog. 

Learn more at Hannover Messe 2024 

With manufacturing data solutions in Fabric and copilot template on Azure AI, manufacturers can better connect factory ecosystems, drive productivity, and enhance business operations using conversational assistants and accessible data analytics from the factory floor. To watch a live demonstration of these solutions, discover the additional capabilities of Microsoft Cloud for Manufacturing and our partners, and to engage with subject matter experts, join us at the Hannover Messe Industrial Fair 2024, in Hannover, Germany from April 22 to 26, 2024 in Hall 17 Stand G06. 

Explore Hannover Messe announcements and Microsoft AI solutions

• Read Azure IoT’s industrial transformation strategy on display at Hannover Messe 2024.
• Read Microsoft unveils AI features for manufacturers at Hannover Messe 2024.
• Build intelligent apps at enterprise scale with the Azure AI portfolio.
• Reshape how everyone accesses, manages, and acts on data with a single, AI-powered platform.
• Work smarter, be more productive, boost creativity, and stay connected to the people and things in your life with Microsoft Copilot.

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To address these challenges and help manufacturers work towards digital maturity, we are announcing manufacturing data solutions in Microsoft Fabric, and copilot template for factory operations on Azure AI, available in private preview. These solutions enable manufacturers to:

• Ingest and unify data directly from diverse sources across the factory ecosystem. 
• Standardize and enrich data according to the International Society of Automation (ISA-95) model for seamless interoperability.  
• Utilize custom copilots for querying data through conversational interfaces. 

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Optimize production with manufacturing data solutions in Microsoft Fabric

Manufacturing data solutions in Fabric allows users to maximize the value of factory data and uncover operational insights for production optimization. This is accomplished by unifying information technology (IT) and operational technology (OT) data into an open and secure data platform. Organizations can realize further benefits by getting manufacturing data ready for AI by enriching it with semantic context that follows an industry standard ISA-95 information model. 

Fabric enables customers to accelerate time to insight generation by unifying, enriching, and modeling manufacturing data in Fabric. The solution ingests data from a variety of sources such as factory-domain data from sensors, systems of record like manufacturing execution system (MES) and enterprise resource planning (ERP), and industrial automation applications. The data is then stored on Microsoft Fabric One Lake for unification, enrichment, modeling, and aggregation. This unified data provides a scalable and repeatable pattern to tackle all factory-domain data projects, accelerating the pace of innovation, freeing up IT resources, and maximizing the value of an organization’s technology investments. 

Customers can extract the full value of their factory data and existing manufacturing solutions landscape using manufacturing data solutions in Fabric. For example, a production supervisor conducting a root cause analysis to determine a correlation between material batches, machine utilization, and quality issues might spend weeks manually aggregating data from several systems and require IT support. With manufacturing data solutions in Fabric, the supervisor has quick access to contextualized data for analysis, completing it in hours instead of weeks. This saves valuable time and immediately provides insights that increase agility and innovation.  

Increase agility with copilot template for factory operations on Azure AI

Creating a unified data lake with manufacturing data solutions in Fabric is essential to optimize AI capabilities. Copilots can enhance responsiveness and streamline communication across teams and roles. We are building an industry-specific augmentation loop to help partners and customers use the Microsoft Copilot stack more quickly with out-of-the-box prompt templates, connectors, and skills all packaged as a standard plugin. Customers can then use natural language to get timely and accurate data from complex systems like MES, quality management system (QMS), and supply planning with minimal effort.  

With copilot template for factory operations on Azure AI, a production supervisor for example, can open a custom chatbot and quickly query the data in a conversational way and identify quality issues in minutes instead of hours. The copilot template is a managed application in the customer-tenant, offering organizations full visibility and control of their data. The template features the latest Microsoft technologies such as Azure AI models, Semantic Kernel, and Azure Cognitive Search, and is extended through the Microsoft third-party independent software vendor (ISV) ecosystem. 

Microsoft Cloud for Manufacturing Partner ecosystem

Microsoft’s partner ecosystem with its deep industry expertise extends Microsoft Cloud for Manufacturing offerings. Systems integrators and ISVs are enabling factory data ingestion from different systems by building custom or proprietary connectors into Fabric: embedding the solution capabilities into their applications or building custom UI experiences for the copilot templates on Azure AI. 

Accenture and Avanade—Manufacturing data solutions in Fabric and copilot template on Azure AI 

Empowering customers to unlock the full potential of their factory data, Accenture and Avanade collaborated on the development of the Avanade Manufacturing copilot, powered by manufacturing data solutions in Fabric, which facilitates innovation, operational efficiency, and data-driven decision-making. By leveraging data from various sources, such as MES, programmable logic controllers (PLCs), sensors, and ERP, the copilot enables seamless querying and generation of insights in natural language for factory workers.  

As leading global system integrators and trusted private preview implementation partners for Microsoft Cloud for Manufacturing, Accenture and Avanade are spearheading the delivery of manufacturing data solutions and copilot templates. With expertise in integrating key Microsoft technologies, like Fabric and Azure OpenAI Service, and collaborating across the broader ISV ecosystem, Accenture and Avanade stand at the forefront of driving digital transformation in the manufacturing sector. 

“Avanade Manufacturing copilot, powered by manufacturing data solutions in Microsoft Fabric, can help AI systems understand data from diverse OT-IT systems that manage complex manufacturing supply chains, processes, equipment, and product ranges in near real-time. With our graph-of-graphs approach to knowledge management and the open ISA-95 standard, we’re able to supercharge the ask-an-expert capabilities of Microsoft Copilot. This is vital in an era of labor and skills shortages, rising production targets, and the need to cut costs and carbon emissions.”

—Brendan Mislin, General Manager, Avanade Industry X

To learn more about Accenture and Avanade Manufacturing copilot offerings, visit their AppSource page. 

Sight Machine Manufacturing Data Platform and Factory CoPilot on Microsoft Cloud for Manufacturing

Sight Machine’s Manufacturing Data Platform (MDP) helps global manufacturers unlock the power of industrial data to increase profitability, productivity, and sustainability. Sight Machine makes it easy to integrate contextualized production data in Fabric. With its data and analytics tools, Sight Machine’s MDP enables companies to combine and analyze contextualized manufacturing data with financial, supply chain, ERP, and MES data. This allows for unprecedented levels of knowledge and enterprise-wide insight. Sight Machine’s Factory CoPilot leverages Microsoft factory operations copilot template on Azure AI to provide conversational querying of the data within the UI of the Factory CoPilot. This enables plant managers and quality engineers to interact with the data to uncover insights faster and accelerate issue resolution.  

“Until now, industrial companies have been unable to incorporate their manufacturing data as a full citizen of their data estates. With Sight Machine on Microsoft Cloud for Manufacturing, companies can optimize production scheduling globally, determine which equipment is best at fulfilling an order, know which lines are at highest risk of going down, see the status of orders, and determine when to re-route them to another facility. It has never been possible to do this in a truly data-driven way.”

—Jon Sobel, Chief Executive Officer and Co-Founder, Sight Machine 

Learn more about Sight Machine solutions on Microsoft Cloud for Manufacturing, visit the Azure Marketplace.

Litmus Automation connector

In the dynamic landscape of industrial operations, Litmus stands at the forefront of data management, offering an advanced DataOps platform that seamlessly operates at the edge. With the ability to swiftly extract, normalize, and model data along with metadata, Litmus Edge empowers enterprises with edge data expertise. Paired with manufacturing data solution in Fabric, this collaboration pioneers a transformative approach, enabling the establishment of a robust hybrid edge-to-cloud infrastructure. From streamlining real-time machine dashboards to facilitating advanced machine learning, the synergy between Litmus and manufacturing data solutions in Fabric unlocks unprecedented efficiency and innovation, seamlessly bridging the gap between edge and cloud.

To learn more about Litmus solutions on Microsoft Cloud for Manufacturing, visit the Azure Marketplace.

AVEVA Connect Industrial Intelligence Platform, integrated with Microsoft Cloud for Manufacturing

AVEVA is collaborating with Microsoft on extending AVEVA’s Industrial AI Assistant to showcase how generative AI enhances supply chain production planning and plant floor production. It helps with scheduling, improving operational efficiency, and decision making in plants and across the supply chain. Using AVEVA’s Industrial Intelligence Platform, CONNECT, and Microsoft Cloud for Manufacturing, AVEVA unifies and contextualizes production execution data and supply chain production planning data in Fabric. AVEVA’s Industrial AI Assistant provides a seamless conversational experience, through a copilot template on Azure AI, that empowers plant floor and supply chain employees with valuable insights to resolve issues and accelerate decision-making activities. 

CONNECT unifies an organization’s industrial ecosystem in a single, secure platform—helping users do more with industrial data, driving digital transformation in real time with powerful intelligence and robust insights. 

Users can easily access software as a service (SaaS) capability through CONNECT to create an industrial hybrid architecture that offers the robustness of on-premises industrial applications with the scale and accessibility of cloud. With CONNECT, companies can engineer smarter and operate better with an open and neutral industrial cloud platform. Organizations can promote sustainable growth by achieving transformation faster, reducing costs, and optimizing at scale. Lastly, manufacturers can connect their business to their ecosystem of partners and accelerate time to value using proven industrial expertise. 

Rockwell Automation’s Plex on Microsoft Cloud for Manufacturing

Rockwell is collaborating with Microsoft Cloud for Manufacturing to introduce natural language copilot experiences within Plex. These innovations aim to streamline quality issue resolution through corrective actions and root cause analysis.  

Corrective action teams often struggle with accessing the correct data and determining root cause. With today’s high workforce attrition, the ability to generate insights and share knowledge about a problem or process is also key. Leveraging Microsoft AI capabilities, Rockwell’s Plex Smart Manufacturing Platform will accelerate the resolution of customer complaints by swiftly identifying root causes and accessing critical data for corrective actions.  

Stay tuned as Rockwell and Microsoft drive innovation in quality management for manufacturing. 

“With the integration of Rockwell’s Plex Smart Manufacturing Platform with FactoryTalk DataMosaix and Microsoft Cloud for Manufacturing, customers will benefit from transformative AI tools that help drive productivity, safety, and quality.”

—Anthony Murphy, Vice President, Product Management

Learn more at Hannover Messe 2024

Discover more about manufacturing data solutions in Fabric during a live demonstration at Hanover Messe in April 2024.  

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The new AI opportunities for public finance  

Public finance organizations are generally well positioned to take advantage of AI’s ability to redefine their services, operations, and impacts on government. Those who have adopted the Microsoft Cloud and Microsoft productivity applications are benefiting from the fact that, over the past decade, and especially the last 18 months, Microsoft has been integrating AI capabilities throughout our offerings.  

Copilots—AI-driven software assistants—are delivering immediate opportunities for ingenuity among ground-level and operational employees, as seen in Microsoft Copilot for Microsoft 365, the new Microsoft Copilot for Finance, and Microsoft Bing. For business and IT leaders, copilots across Microsoft Power Platform, Dynamics 365, Security, and GitHub make it simple to unlock powerful new capabilities with embedded, low-code features. And in cases where advanced solutions are called for, Microsoft Azure OpenAI Service enables the development of customized solutions such as virtual assistants, chatbots, automated systems, and intelligent applications.  

Thanks to the broad range of these capabilities, public finance agencies can move quickly to redefine their role and leverage new opportunities. To make this vision a reality, we focus on AI innovation with three specific objectives in mind.  

1. Simplify taxpayer experience and revenue collection 

One great opportunity with AI is to make it easier for people and businesses to pay the right amounts of taxes and fees, in ways that are embedded into their lives. AI powered solutions for taxation, customs, and license and permit processes can be simple and sensible while providing the security and compliance benefits of the cloud. AI helps with managing data and fostering faster ways of information sharing across agencies—a challenge that by one estimate costs European Union governments more than 50 billion euros a year.¹ 

In this regard, AI can help in many ways, including: 

• Modernizing taxpayer services—New engagement models can proactively build data intelligence to improve revenue collection, while reducing the cost of compliance, increasing agility, and improving productivity. Taxpayers can enjoy seamless experiences fostering a greater sense of trust in agencies. This proved to be the case with Estonia’s Information System Authority, which created an AI assistant that grants people with access to secure information about vital government services in a matter of seconds as opposed to days. 
• Boosting employee productivity—Employees tend to be more effective with tools that minimize drudgery and help them focus on delivering results and finding solutions. User-friendly, interoperable, and automated processes foster better productivity, and AI enables new ways to help employees find the right information at the right time. A key factor in realizing this potential is upskilling the workforce, which is why the Bank of Canada implemented an ongoing training initiative based on Microsoft Learn that provides individualized learning pathways with online on-demand and instructor-led content.  

2. Drive informed budgeting for economic development 

Trillions of dollars are distributed by governments around the world to households, businesses, local authorities, and others for stimulus, recovery, and resilience plans.² This puts an enormous responsibility on budget and treasury agencies, which have the complex task of allocating public resources in ways that measurably impact economic development. These agencies require innovative, secure tools that enable them to allocate the right support to the right beneficiary at the right time. Done well, these solutions also enhance collaboration between government and the private sector, which ultimately fosters financial inclusion. 

To meet these challenges, AI can help by: 

• Modernizing budget planning and execution—Employees in budgeting agencies often deal with legacy systems that are insufficient for accurate forecasting and planning. AI can help with new tools that improve these processes, as well as automate budget distribution, deliver reports, and improve forecast precision. Low-code, AI-enhanced tools can accelerate the creation of powerful solutions. In Japan, for example, the Ministry of Economy, Trade and Industry slashed development timeframes from one year to as little as one month by adopting Microsoft Power Platform.  
• Boosting employee productivity—Treasury agencies have significant management responsibilities, and high demands to operate with accountability and transparency. Cloud and AI can empower teams to better manage liquidity, optimize debt management, and manage reserves and investments, while also being efficient and secure. Productivity at the United Kingdom Department for Environment, Food, and Rural Affairs improved dramatically with a new cloud solution to provide a customer portal, streamline business processes, and add real-time reporting. By automating claim entry, they processed more than 40% of their annual 250,000 payments in the first three months.  

3. Mitigate fraud and corruption in public finance 

Fraud and corruption are major problems that incur trillions of dollars in losses worldwide every year.³ AI can help public finance agencies reverse the trend by helping to spot activities that may signal fraud, evasion, or abuse of public funds. To do this, agencies need solutions that give them fast, accurate, and comprehensive analytics, with a 360-degree view of taxpayer profiles to make informed decisions. 

Examples of how AI can help mitigate fraud and corruption include: 

• Improving compliance and protecting against fraud and corruption—Public finance agencies need solutions to combat illegal activities while also safeguarding taxpayers, employees, and all their data. AI enables solutions that analyze data from traditionally siloed, disconnected sources to detect anomalies and derive insights in previously impossible ways, while inherently protecting to ensure security and regulatory compliance. SymphonyAI, an enterprise software company that employs AI to solve financial crime, recently launched a copilot for investigators that automatically collects, collates, and summarizes financial and third-party information. Early experience shows that it can improve investigator productivity by more than 60%.  
• Enabling modern risk management—Risk is often inherent for public finance agencies, so teams need effective, modern approaches to risk management. These solutions should deliver monitoring and reporting across organizations and agencies, ensuring that everyone has complete, appropriate access to information. A good example is an Electronic Invoice Anomaly Detector built jointly by Microsoft and the Inter-American Center of Tax Administrations (CIAT), which strengthens electronic invoicing and reduces fraud and evasion for tax administrators in its member and associate member countries worldwide. 

The essential role of responsible AI 

The promise of AI would be impossible without trust. That is why Microsoft has long been a leader in ensuring the development of responsible AI, with principles designed to put people first. We believe AI exists to enhance human capabilities, not replace them, and we are committed to empowering responsible AI practices that benefit the world at large.  

The Microsoft Responsible AI Standard defines product development requirements for Microsoft technologies, guided by the principles of fairness, inclusivity, reliability and safety, transparency, privacy and security, and accountability. We believe this holistic approach can help public finance agencies deliver actionable results for their communities, with a minimum of risk and unintended consequences.  

Empowering public finance agencies with data and AI

It is an exciting time to be in public finance. Connected systems driven by data and AI set the stage for governments to unlock new possibilities such as tax compliance by design, intelligent connected trade windows, digital currency, outcomes-based budgeting, and cyber and financial crime detection and prevention convergence.

To learn more about how Microsoft is helping public finance agencies to reignite economies and improve accountability with AI, visit Microsoft for Public Finance.

¹ Politico, “Billions of euros lost to poor tax data, EU watchdog says,” Bjarke Smith-Meyer, January 2021.

² OECD, Global Outlook on Financing for Sustainable Development 2021, November 2020.

³ United Nations, “Global Cost of Corruption at Least 5 Per Cent of World Gross Domestic Product, Secretary-General Tells Security Council, Citing World Economic Forum Data,” September 2018.

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What is equally if not more important, however, is the power of AI to help solve some of the world’s most challenging social problems. This resonates with the work we’re doing with Microsoft Cloud for Financial Services, where we strive to not only empower customers but also help improve the world broadly through responsible AI and cloud computing.

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It’s clear that generative AI opens new doors to create greater value for customers, the benefits of which are already dramatic across industries. In the unique case of financial services, generative AI also opens opportunities to address global problems that have long challenged almost every segment of society.

Three areas of financial services impact

In January, I had the opportunity to participate in the World Economic Forum’s annual meeting in Davos, Switzerland. Naturally, AI was a big topic of conversation, and what was most encouraging to me were the discussions and examples of its potential to impact major social challenges. In the context of social good, I see three key areas where this can happen:

1. Equity and inclusion

I see advanced and generative AI holding tremendous potential to create more inclusive and personalized financial products and experiences for a broader population. This could include the latest natural language capabilities in chatbots, integrating screen reading and narration capabilities like Seeing AI into your banking products for the blind and low vision community, and offering speech-to-text functionality for those with hearing impairments. A powerful example of the transformative potential of AI in coding is the story of Anton Mirhorodchenko, a Ukrainian software developer living with cerebral palsy. By using GitHub Copilot, he dramatically simplified his workflow and improved his productivity and outlook.

2. Poverty and stability

Financial inclusion is also key to stability, as technological innovations unlock new opportunities for data-driven financial tools that can empower a broader range of underserved communities to achieve financial independence. There’s a lot of exciting fintech innovation in this area. For example, CWallet, a fintech company specializing in digital wallet services, is empowering migrant workers in Qatar to access financial services with Microsoft Azure. There are other important fintech initiatives underway in places like Latin America and Kenya using AI and digital innovations to improve lives, reduce poverty, expand access to financial services and credit, and narrow the financial inclusion gap.

And it’s exciting to see how other organizations across industries are already innovating with Azure OpenAI Service to enable inclusive growth with technology. For example, as part of our new ADVANTA(I)GE INDIA initiative and AI skilling efforts, Indian social impact organization, Karya, is using Azure OpenAI to help make technology accessible in under-resourced languages and to more inclusive data—with work that also provides rural citizens with training, fair wages, and education about financial tools to make best use of their earnings.

3. Environment

We all recognize the critical importance of addressing climate change, and this is already an important topic in financial services. But I believe we are just scratching the surface on AI’s potential to tackle common risks and opportunities for environmental, social, and governance (ESG) and sustainability efforts in FSI due to slow manual processes, siloed data, data quality issues, lack of insights, and reporting. Generative AI advancements can aid in synthesizing structured and unstructured data, creating ESG insights and recommendations, and reporting out to stakeholders. I’m also excited to see examples from financial leaders like Emirates NBD transforming sustainability measurement capabilities with Microsoft Sustainability Manager.

When it comes to achieving these ambitions—and countless others where AI can make a major difference—success requires more than just cutting-edge technology. Microsoft believes that meaningful innovation can only happen when organizations also embrace a set of enabling principles that focus on ethics and human factors.

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The critical role of responsible AI

The excitement around this next wave of AI is undeniable, but we must wield it responsibly to avoid perpetuating biases or excluding segments of society. At Microsoft, we are committed to helping our customers use our AI products responsibly, sharing our learnings, and building trust-based partnerships.

To help financial services organizations realize AI’s potential, Microsoft has published the Responsible AI Standard, developed an Impact Assessments template, and created transparency documents for customers using our Azure OpenAI Service and products like the new Bing to share what we’ve learned. The Microsoft open-source Fairlearn toolkit can also help financial services organizations ensure their AI systems are equitable by identifying biases in data. When our partner EY put it to the test with real-world mortgage adjudication data, it improved the fairness of loan decisions, narrowing gender disparities from 7% to less than 0.5%. From Davos, you can also watch my panel discussion on the responsible deployment of AI in financial services.

How inclusive design and diversity unlock potential

Inclusive design hinges on the vast spectrum of human diversity, gleaning insights from varied perspectives. Microsoft champions design principles that recognize exclusion, learn from diversity, and create universally beneficial solutions. Technology that is designed in this way means better access, less friction, and greater emotional connection with more people.

Our commitment to helping others shift to inclusive solutions is found in our Microsoft Inclusive Design toolkit. The tools create large-scale solutions, such as digital experiences that are more responsive and less biased, and cities that are more accessible. In the financial sector, this translates into products and services designed to meet the needs of as many individuals as possible, regardless of their abilities or circumstances.

Likewise, diversity is a proven catalyst for innovation among technology teams. Studies show that greater diversity can help teams focus more on facts, process those facts more carefully, and generate more creativity and innovation.¹ Ethnically and gender-diverse management teams are more likely to financially outperform in their industry, and companies with more women in leadership positions tend to be more profitable.² So, for technology to be truly inclusive, it needs to be built by teams that reflect the diversity of its users.

A mission of empowerment through AI

My transition from the banking industry to Microsoft was driven by the potential for meaningful technological innovation that could create a positive change. Despite the challenges we face, my outlook remains optimistic. I hope this blog has set some ideas in motion for you, and I invite everyone to become involved in efforts to use AI in ways that benefit society broadly.

The Microsoft mission is to empower every individual and organization on the planet to achieve more, and we do that by building technology that we believe will change the world. To accomplish that, we know that we must embrace a set of important responsibilities. Trust, reliability, safety, privacy, security, inclusiveness, transparency, and accountability—these are the foundational principles that have guided our leadership in AI over the past decade. Along with our partners and many other global stakeholders, we invite you to join us on the important journey ahead.

To learn more about our commitment to trustworthy AI and to find further resources, please visit our Empowering responsible AI practices website.

¹Diversity wins. How inclusion matters. McKinsey & Company, May 2020.

²Why Diverse Teams Are Smarter. David Rock and Heidi Grant, Harvard Business Review, Nov 4, 2016.

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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. 

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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.

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The big impact of generative AI

Discussions about the potential of generative AI on the telco industry are everywhere. “IDC is projecting that generative AI will add nearly USD10 trillion to global GDP over the next 10 years.”¹ Other analysts are projecting telco productivity increases in the billions for customer service, marketing, sales, app development, network insights, and operations. At Microsoft, we’ve already seen the benefits of generative AI across the company. The Microsoft global customer support team streamlined operational efficiency while delivering exceptional customer satisfaction with Copilot in Dynamics 365 Customer Service. There was a 31% increase in first call resolution and a 12% increase in customer satisfaction. Learn more about modernizing your customer service experience with Microsoft Dynamics 365 Customer Service. The Microsoft Customer and Partner Solutions organization used Microsoft Copilot for Sales to simplify workflows and help its sellers more efficiently build relationships with clients.

What does generative AI do for telco?

Generative AI empowers telco to work with vast amounts of data, identify patterns, and generate novel solutions, promising to transform traditional practices and foster industry-wide innovations. By embracing generative AI, telco companies can overcome challenges, unlock new revenue streams, improve operational efficiency, and deliver exceptional customer experiences. It’s a key ingredient in accelerating the transformation from telco to techco. And with so many potential applications for generative AI, it’s important to identify what has worked so far. To provide some inspiration, here are a few success stories that show us generative AI’s potential to transform the industry and drive substantial value.

Elevating customer experiences

Indonesian telco company Telkomsel introduced Veronika—a virtual assistant that integrates Microsoft Azure OpenAI Service. Veronika is rooted in natural language processing and machine learning, and according to Vice President of Customer Journey and Digital Experience at Telkomsel, Danang Andrainto, “Veronika continues to innovate by integrating the best AI technology to infuse intelligence into its functions, resulting in the delivery of solutions that are progressively more accurate.” Veronika recommends telco packages based on customers’ needs, and it can quickly and accurately address customer concerns. Here’s more of the story of Veronika and how generative AI is making it better.

And Veronika isn’t the only one. Bots are becoming more popular and effective in providing a digital-first customer experience. They can handle complex queries, improve customer engagement, and reduce operational costs. They can reduce the average handling time and save millions of dollars per month. These bots can help customers with various tasks, such as checking balances, paying bills, troubleshooting issues, and finding the best deals. They can build personalized scripts for next-best offers based on real-time data and insights. They can enhance end-to-end call center engagements from customer inquiry summarization, providing real-time information to resolve questions for sales agents, to analyze live sentiment, and to suggest personalized scripts for next-best offers; and they can create post-call analyses on agent performances. They can also create customer-sentiment analysis to help monitor and improve customer experiences across multiple touchpoints. Bots are transforming customer care strategies for many businesses around the world.

Bots are helping employees too. South African telco group MTN launched SiYa—an employee bot that can assist workers with inquiries, information on company policies, and employee-to-company interactions. And ultimately, MTN hopes SiYa can help customers with purchases, advice, and service. “By harnessing the power of AI and APIs, we are not only future-proofing our operations but ensuring that our customers, both internal and external, can look forward to a more streamlined, efficient, and data-driven experience,” MTN South Africa CEO Charles Molapisi says.

Aimee is BT Group’s new digital assistant. Kevin Lee, Chief Digital Officer for Consumer Division of BT Group, says, “Our pilots with generative AI with Microsoft are designed to see if we can more rapidly make Aimee the most personal, customer-focused, intelligent digital assistant delivering value through every interaction.” But Lee says he doesn’t want Aimee to replace human-to-human interactions; he wants Aimee to help customers meet their needs more efficiently and accurately. Aimee is meant to be a support, and her role is unlimited.

Accelerate network operation

If we look to the technical side of things, generative AI can improve network operations for operators too. Three UK leveraged Azure Operator Insights by creating and optimizing network configurations, policies, and parameters based on the data collected from the network performance, traffic, and user behavior. Generative AI can learn from the existing network settings and generate new ones that can enhance the network’s efficiency, reliability, and security. For example, generative AI could help design and deploy optimal network slices for different use cases and customers or adjust the network parameters to cope with changing demand and conditions. Generative AI can also help to automate network management tasks like fault detection, diagnosis, and resolution by generating and executing appropriate actions based on the network state and the desired outcomes. It can use natural language processing and generation to enable more human-like interactions between network operators and the network systems, using voice commands to control the network functions or receiving natural language explanations of the network status and its issues. Generative AI applications for network operation include:

• Generative AI can be used to optimize Radio Access Network Configuration Optimization parameters based on the network performance data and the operators.
• Generative AI can generate and execute network policies, configurations, and actions based on the network data and the operator’s goals.

Organizations can be more efficient

Generative AI can help make knowledge management more efficient with the ability to increase the productivity of HR, finance, legal, and customer service departments. By using bots, organizations can provide faster and more accurate answers to their employees and customers. For example, Finland’s largest telco and tech company, Elisa, has used Microsoft Copilot for Microsoft 365, which helps knowledge workers with various tasks, such as finding documents, scheduling meetings, and creating reports. Katja Bäckström, Elisa’s Senior Vice President, Customer Service and Customer Service with Copilot, says, “With Copilot, traditional data entry is eliminated, and customer data can be accessed directly from customer discussions. Copilot can easily be asked about the latest actions with the customer and a proposal for creating the next agenda. In the end, Copilot improves both the employee experience and the customer experience.”

Multinational tech and telco company Lumen uses Copilot. Lumen’s customer service teams surface relevant policies with Copilot, they summarize tickets, and they easily find repair instructions from manuals. They can quickly create presentations, new business proposals, and statements of work. “Our people are seeing immediate productivity improvements with Copilot, allowing them to focus on more value-added activities each day,” Kate Johnson, president and CEO of Lumen Technologies, Inc. says.

Marketing and sales benefit from generative AI

But can generative AI help your marketing and sales departments?

Generative AI can help create appealing and customized content for different audiences and channels, such as blog posts, social media posts, landing pages, email campaigns, and more. For example, a telco operator could use generative AI to create titles, summaries, keywords, or captions for their online content based on the target audience’s interests and actions. 

Generative AI can also help classify and segment customers based on their attributes, desires, preferences, and actions, using data from various sources, like web analytics, customer relationship management platforms, or social media. A telco operator could use generative AI to group their customers into different profiles, for example, such as the innovators, the loyalists, and the value seekers. They could then adjust their messages and offers accordingly. 

Generative AI can help recommend appropriate and personalized products or services to customers based on factors such as their previous purchases, web history, and feedback. For example, a telco operator could use generative AI to suggest the best bundle, plan, or add-on for each customer, based on their budget, needs, and usage patterns. 

Generative AI can help create interested and qualified leads for the sales team by finding and contacting potential customers who match the ideal customer profile, using data from various sources like third-party databases, social media, or web analytics. For example, a telco operator could use generative AI to create leads for their business solutions by identifying and reaching out to prospects who are searching for similar services, have high intent, and meet the criteria of decision makers.

Amdocs, in partnership with Microsoft, launched a unified Customer Engagement Platform, leveraging the power of generative AI. Integrated with Microsoft Dynamics 365, it is an all-encompassing AI-powered marketing, sales, commerce, and customer service platform serving consumer and enterprise customers on a single, open, telco, and cloud-native platform.

Developer productivity

With GitHub Copilot for Business, 46% of new code is now written by AI, and developer productivity has increased by over 55%. AT&T uses Azure OpenAI Service in a few ways. It moves legacy code into modern code using generative AI, which helps the developers focus on creating modern tools and experiences for workers and customers. AT&T employees can ask generative AI questions about their insurance plans or getting hardware for a new employee. And AI helps with storage problems or computer issues company wide. Read more about AT&T’s developers and generative AI.

GitHub Copilot simplifies the creation and consumption of TM Forum Open APIs by generating the required code that complies to the API guidelines in the development language of your choice.

Accelerate your own generative AI journey with Microsoft

Partner with Azure OpenAI Service to transform your telco organization with AI-driven solutions for enhanced operations, innovative services, and exceptional customer experiences. The service offers many opportunities to explore AI-driven solutions for your organization. We hope to see you at Mobile World Congress 2024 as well. We’ll be there talking about how AI is accelerating the telco industry’s transformation.

About the study
The IDC study, commissioned by Microsoft, is based on results from 2,109 enterprise organizations totaling more than 13 million employees worldwide across 16 countries globally. Through the questionnaire, respondents were identified as the decision maker for AI within their organization.   

¹Official Microsoft Blog, New study validates the business value and opportunity of AI, November 2023. 

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Financial markets are changing rapidly, creating new challenges and opportunities for all participants. For our customers, there is demand for the right data at the right time with reduced complexity and increased flexibility. LSEG’s comprehensive data and analytics and Microsoft’s trusted and secure global cloud platform and AI capabilities enable us to co-innovate solutions for the financial markets eco-system. These innovations will evolve how customers gain value from their data to unlock opportunities by combining LSEG’s data and content sources in Microsoft Fabric, integrated into the enterprise-wide data catalog and governance framework of Microsoft Purview.

Innovating to meet customer needs 

Analytics and modeling are now critical to success as the increasing demand for diverse data fuels business decisions and drives the need for a simplified approach that meets business needs. In addition, as regulations become more quantitative, it has created substantial challenges in terms of lost time and capital for customers who don’t have the necessary data and analytics infrastructure in place.   

Historically, Microsoft provided our horizontal cloud platforms to our customers such as solutions for developers—Microsoft Azure, Microsoft 365, and Microsoft Teams, notably among them—while LSEG separately delivered financial market infrastructure, data, and analytics. The burden largely fell on our mutual customers to map those building blocks to their business problems and to bring these assets together in a coherent way. That forced our customers to bear the cost and complexity of this integration. 

With our partnership, we seek to help our customers climb the value chain by bringing these complementary assets together. We will transform financial services workflows to help finance and investment professionals improve decisions, communications, and productivity while maintaining regulatory compliance. Together we will offer customers a simpler and more connected solution with:   

• Cloud-based data architecture that consolidates LSEG datasets on one, flexible infrastructure that is simple, responsive, and efficient, and built to meet the data needs of the enterprise. 
• Cloud analytics and modeling services built on Microsoft Azure Machine Learning. 
• A comprehensive security approach enabling preventative and detective controls to meet the security, privacy, and compliance needs of this regulated industry.

Customers will be able to use the combination of Microsoft Fabric powered by LSEG data, analytics, and data management capabilities to enhance their workflow and bring greater efficiency and productivity to their organizations. To bring that to life, you can see in this workflow illustration how LSEG and Microsoft’s collaboration will drive significant productivity by simplifying the whole process of finding, managing, and distributing massive content sets.  

Speaking about enhancing productivity, we also discussed how the LSEG-Microsoft partnership will evolve the customer experience at-scale across global financial markets to deliver the most advanced, easily accessible financial data and insights through: 

• A single point of access to financial markets data that reduces time and cost for financial institutions to discover, integrate, manage, share, and derive insights from petabytes of financial and alternative data. 
• Intelligent analytics solutions that reduce the time and cost for creating, distributing, and running complex analytic models across APIs and through the Microsoft productivity suite. 
• An integrated financial services workspace that empowers customers to make informed decisions with confidence and greater speed through seamless workflows and increased productivity. 

The uniqueness of this partnership is in LSEG’s data and analytics, coupled with the Microsoft cloud platform, data platform, and Microsoft 365 collaboration suite. In this way, we meet customers where they are, while creating vertical industry value. Unifying data with Microsoft Cloud for Financial Services can make data and analytics much easier to discover and use—whether that is finding pricing analytics in Excel, connecting with counterparties through Microsoft Teams, or using Microsoft 365 Copilot in Microsoft productivity apps to access LSEG financial markets data. Together, we will benefit customers by increasing productivity while offering greater efficiency, resilience, and scalability across all workflows. 

Moving ahead with generative AI 

The benefits of AI and machine learning have accelerated the rate of change in financial innovation enabling frictionless customer experiences, empowering employees to apply their creativity and talent rather than focusing on tedious work while enabling deeper insights to drive better decisions.

AI enables technology to understand and speak the language of industry. Generative AI will enable organizations to better take advantage of technology, collapsing data barriers and decoding the complex landscape of macroeconomics, markets, and regulations. 

“Microsoft AI improves the way we work, and we are making AI tools a better fit for financial services. We are combining Microsoft’s dependable and scalable infrastructure with the breadth and depth of LSEG’s trusted high-quality data and IP safeguards. Together we are shaping a future where technology supercharges our customers’ workflows and insights reliably, effectively, and responsibly.“

—Dmitri Sedov, Global Group Head, LSEG Data Intelligence. 

Microsoft AI tools are making work easier, and LSEG is making AI more valuable to financial services. AI is only as good as the quality of the data it consumes. LSEG brings a unique capability to demonstrate data trust with a breadth and depth of aggregated, cleaned, and codified financial markets data as well as extensive data management knowledge and understanding of what regulators and the world’s largest financial institutions need. These capabilities sit alongside Microsoft’s Responsible AI commitments, including our recently announced Copilot Copyright Commitment to protect our customers from copyright claims arising from their use of our copilots.

The benefit of AI for customers and clients will be in how quickly and easily they can access the right data to generate insights with AI that are both reliable and relevant to the task at hand.

Microsoft Fabric and the embedded generative AI capabilities in Fabric present another great transformation for financial services. Microsoft Fabric will be the cornerstone of LSEG’s Data Platform, and their financial markets intelligence will help enable the financial markets ecosystem to leverage generative AI and other capabilities. We will also use AI to address the data discoverability challenge itself by using AI models to better understand user preferences, predictively surfacing relevant data, and categorizing vast datasets into intuitive segments. All of LSEG’s data available through Microsoft Fabric will also be published in Microsoft Purview, enabling this data to be discovered in the enterprise data catalog and governed centrally alongside other proprietary and commercially acquired data sets.

LSEG’s quantitative and engineering talent and proven ability to provide the right data for the financial services community bring the credible expertise needed to ensure generative AI provides the optimal yet secure experience for our customers. There is a profound opportunity for our customers to unlock new value and we are excited to deliver this value together with LSEG. 

Discover more

• Discover Microsoft Cloud for Financial Services.
• Learn more about Microsoft perspectives on the era of generative AI: 
  • The era of generative AI: Driving transformation in banking 
  • The era of generative AI: Driving transformation in insurance 

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