Microsoft Cloud for Financial Services

Unlock business value and deepen customer relationships in the era of AI.

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Microsoft partners at Money20/20 USA 

Microsoft was proud to showcase powerful partnerships at Money20/20 USA, featuring many partner solutions driving AI transformation in the financial services industry.  

This included a suite of compelling demos from Microsoft partners including Temenos, Accenture-Avanade, Infosys, Backbase, Symphony AI, and Zafin. Accenture-Avanade demonstrated their Relationship Manager agent, which can improve relationship management across sales and services by freeing up capacity and enhancing client interaction quality. Additionally, Infosys’s Smart Agent and Smart Bank Assist showcased improvements to banking experiences for both employees and customers.  

Microsoft partners, including Cognizant, EY, Intellect Global Transaction Banking (iGTB), Personetics, D-iD, Integrate AI, and others, participated in theater sessions at the Microsoft booth. They presented their thought leadership around the most compelling AI-enabled use cases and deployment methods within banking and financial services. Additionally, Microsoft-sponsored panels with NVIDIA and BNY uncovered practical tips and considerations for scaling and deploying the latest AI technologies, further positioning Microsoft partners as thought leaders in the AI space. 

These collaborations reflect the Microsoft commitment to empowering partners to drive impactful, AI-powered solutions that help customers achieve their business goals and transform the future of financial services. 

Partners leading the way in delivering real-world AI value for financial services  

Taking a page from our Money20/20 USA presence, we’d like to share compelling examples where our partners are using AI to transform the banking experience, empower employees, help manage risk and compliance, and modernize core banking. These partnerships are not just exploring the possibilities of AI—they’re delivering concrete, measurable outcomes in the financial services space today.  

Transforming the banking experience 

Partners are transforming the banking experience by unlocking opportunities to enhance customer engagement. Backbase is delivering more compelling customer experiences around omni-channel for banking and wealth management based on their Engagement Banking Platform powered by Microsoft Azure AI. Capgemini, the Microsoft Global Financial Services Partner of the Year, has also driven AI innovation on the Microsoft platform, improving productivity and elevating the customer experience at financial services organizations worldwide.

“Capgemini and Microsoft have collaborated on enhancing business processes with Copilot Studio and Azure OpenAI Service, helping banks and insurers better serve their customers. For instance, Capgemini recently helped a major Global bank streamline and accelerate its customer onboarding process with Microsoft Intelligent Document Processing, using AI Builder for structured documents and Azure OpenAI Service for unstructured documents. These technologies have saved an enormous amount of time developing impactful solutions for our customers.”

Vivek Desai, VP and Global Head, Microsoft CoE for Financial Services.

Additionally, global financial services solutions provider, VeriPark, is advancing custom agents that span 40 use cases across corporate and retail banking. 

 “[Collaboration] allows us to deliver cutting-edge AI solutions that not only enhance operational efficiency but also empower banks to provide personalized, real-time services to their clients.”

Özkan Erener, CEO, Veripark 

Empowering employees 

There are also meaningful opportunities for AI to empower banking employees to be more effective trusted advisors to their customers. Using Microsoft Fabric and Copilot, Finastra’s Assist.AI, powered by Azure OpenAI, is boosting trade finance employee productivity with intelligent features that enable users to better prioritize and more efficiently complete everyday lending tasks. In addition, Tata Consulting Services (TCS) is modernizing how they engage with customers via an agent designed to optimize communication between banks and their users across all customer engagement channels. 

Managing risk and compliance 

When it comes to mitigating risk and crime, partners are delivering new intelligent approaches. ASC is providing institutions with more decision-making power around managing fraud and risk, while SymphonyAI is consolidating enterprise-wide risk and compliance across their Sensa Investigation hub solution, powered by Microsoft Azure and Azure Open AI. Additionally, partners like Holistic AI are helping customers such as Mapfre identify and mitigate risks associated with AI, while harnessing their data to ensure transparency, fairness, and compliance.  

Modernizing core systems 

Microsoft partners are also using AI in the mission-critical task of modernizing payments and core banking for greater efficiency, transparency, and high-value outcomes such as unlocking new revenue streams, reducing operational costs, and enhancing customer satisfaction. Temenos is delivering banking-specific solutions to provide agility, security, and innovation—to help banking customers reduce operational costs and uphold responsible AI standards via their commitment to explainability, secure operations, and safe deployment practices. Zafin advanced their leading software-as-a-service transformation and modernization platform for banks with Azure AI; this includes their de-risked implementation process that generates a 50% reduction in time to market¹. 

“The introduction of generative AI and Microsoft Fabric from a data standpoint are crucial…”

Chris Dickin, Executive Vice President, Zafin 

Additionally, the collaboration among Microsoft, our partner Quantexa, and European bank Novo Banco has delivered advanced banking data estate modernization in the era of AI. 

Learn more about financial services solutions from Microsoft 

Whether you joined us in person at Money20/20 to see the latest AI innovations from Microsoft and our partners, or are just now discovering the difference our technologies and partners are making in the industry, we invite you to collaborate with us on your own transformative AI experiences. 

• Learn more about Microsoft solutions in the financial services industry and our work with key partners by visiting Microsoft Cloud for Financial Services.  
• Learn how our partners are delivering industry-specific solutions faster with Microsoft Industry Clouds. 
• Visit our Solutions Partner learning path (which includes Microsoft Learn documentation, a playbook, and FAQs), and explore related financial services stories in our financial services industry blog.

¹Zafin launches Zafin IO and Zafin Data Fabric, a new offering to accelerate banking transformationThis offering will simplify integration processes, cut core modernization risks to support uninterrupted banking transformation, all while breaking down data silos and unlocking the power of banks’ first-party data – Zafin

The post Beyond Money20/20 USA: Microsoft partners redefining financial services appeared first on Microsoft Industry Blogs.

Microsoft for travel and transportation

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World Aviation Festival growth and significance

Since its inception 21 years ago, the World Aviation Festival has grown from a modest gathering to a major industry event. This year’s festival highlighted this transition, with post-pandemic recovery driving significant attendance and engagement. The rapid growth of the event underscores the relevance and importance of the topics discussed, especially with the aviation industry experiencing a vibrant resurgence, growing 24% to a market worth $1.5 trillion.¹

As a supporter of the event, Microsoft met with industry leaders, customers, and partners to discuss the impact of growth, technological advancements, and a secure future for the industry. Our industry executives participated in three panels covering the topics of:

• Understanding how CEOs are approaching structural industry shifts, potential for growth, sustainability implications, and AI business transformation.
• How will generative AI impact the aviation sector, and what strategies can be employed to harness its potential.
• How can the aviation industry accurately identify risks and develop robust cybersecurity strategies to address global threats.

These panels inspired deeper follow-up conversations with conference attendees who we hosted in our meeting room suite and at our evening customer engagement event along with our industry partners. This provided an excellent opportunity to hear about the challenges and opportunities our customers face in an evolving industry and share success stories from industry peers who are leading change and delivering business results. Attendees were encouraged by this evidence and wanted to learn more about our cloud, AI, and data capabilities, including where we can support:

• Enhancing the employee experience—leveraging AI for communication and collaboration platform and digital tools that create connection and inspire collaboration with more insights.
• Innovating the traveler journey—by unlocking data intelligence to create frictionless, multi-modal travel solutions for customers.
• Creating efficient operations—that improve asset utilization, planning, and management of real-time operational impacts.
• Delivering a more engaging customer experience—to improve customer value, drive growth, and brand affinity.

Addressing aviation industry challenges and opportunities

With the many attendees at the event absorbing information across more than 600 speakers and 327 sessions, the following themes resonated as key to addressing the challenges and opportunities facing the industry.

Importance of information sharing

A recurring theme at the festival was the critical need for enhanced information sharing among airlines and airports. By integrating customer data from the moment a traveler leaves home through their entire travel experience, the industry has a significant opportunity to create personalized offers and services within their own, and extended ecosystems. This seamless customer journey was emphasized as crucial for enhancing the travel experience and operational efficiency. Another impact on information sharing is the operational differences between European and US airports that can impact data sharing and efficiency. European airports often have more integrated operations, which can enhance data sharing and streamline processes.

Focus on technology

The festival also placed a strong emphasis on leveraging new technologies, particularly AI, generative AI, and data analytics. These technologies were viewed as pivotal for the aviation industry’s growth, enabling better customer insights, personalized services, and operational efficiencies. Discussions also covered security and sustainability, highlighting their growing importance in today’s aviation landscape along with the increasing concerns over data privacy.

Legacy systems and new equilibrium

The aviation industry is at a crossroads, balancing legacy systems with the adoption of new technologies. While legacy systems still play a significant role, there is a strong push towards new distribution capabilities (NDC) and standards, such as those set by the International Air Transport Association (IATA). This shift is driven by the need to move away from outdated technologies and embrace solutions that utilize data effectively to enhance customer experience, operational efficiency, and to attract a new and more engaged workforce.

Startups leading the way

Startups are at the forefront of this technological revolution, unburdened by legacy systems and able to implement modern architectures and processes at speed. These innovative and agile companies are leading the charge in adopting new technologies and driving the industry forward. This enables a healthy and needed expansion of the industry ecosystem, and while it might pose some challenges for incumbents to compete, it also provides great opportunities to innovate and partner, further stressing the importance of re-thinking how the industry conducts its business to meet the demands of their customers.

Sustainability

Sustainability is a significant challenge, with the aviation industry aiming to be carbon neutral by 2050.² One area discussed is the introduction of sustainable aviation fuel (SAF). However, shortages of supply make the exploration of alternative fuels and sustainable practices beyond SAF important. For example, some industry scenarios are exploring electrification and hydrogen as a potential for specific use cases that can improve sustainable travel.

Technology enablers

Several key discussions at the festival highlighted the industry’s challenges and opportunities. Conversations with airlines like Alaska Airlines and Emirates provided insights into how AI and personalization technologies are being used to enhance customer experiences.

Agentic AI

While the focus on generative AI was pervasive, we also discussed the interest in AI agents, or agentic agents. Agentic agents are autonomous software programs that perform specific tasks on behalf of and alongside users, making decisions based on data, predefined rules and learning from experience. The benefits include a streamlined and user-friendly booking process, faster bookings, higher conversion rates, around-the-clock availability, personalized recommendations and potential upselling. We found companies were very interested in learning more about successful use cases where agentic AI has delivered results.

Reference architectures

In an environment where technology creates positive disruption that drives transformation, many different paths to an outcome may be explored. Rather than create a “one size fits all” approach to achieving those outcomes, we have created a composable base that partners and customers can leverage to develop their innovations using the Microsoft cloud, Azure, and industry focused reference architectures. This enables development of value-creating solutions on top of the Microsoft Cloud technology stack, helping airlines and airports manage data, break down silos, and improve operations. Customers like Adami and Fraport are leveraging these architectures to integrate data from various sources and enhance operational efficiency. Watch here to learn about our reference architecture for airlines and airports.

One major challenge is unbundling legacy systems that have been in place for decades. These systems need to be updated to utilize data effectively and implement new technologies that can help airlines operate more like e-commerce businesses.

Next steps for aviation industry innovation

The 2024 World Aviation Festival was a testament to the resilience and innovation of the aviation industry. With a focus on new technologies, information sharing, and sustainability, the festival showcased the industry’s commitment to enhancing customer experiences and operational efficiency. We were encouraged by participants’ interest in emerging cloud, data, and AI capabilities which are now seen as driving key competitive differentiation among airlines and a key opportunity to improve airport operations and services.

Moving forward from the event, we now look ahead to our key industry programs including Microsoft Ignite, and the global Microsoft AI Tour series underway, as well as the focus on the overall mobility landscape at CES 2025. We look forward to continue to working closely with our customers, ecosystem and industry to contribute and innovate for an even brighter and exciting future.

To learn more about Microsoft solutions for travel and hospitality, visit Microsoft for travel and transportation.

¹ Travel Professional News, Global travel industry roars back, reaching $1.5 trillion in 2023, August 2024.

² IATA, Net Zero Roadmaps.

The post Transforming the travel industry through innovation and collaboration: World Aviation Festival 2024 appeared first on Microsoft Industry Blogs.

• Constraints imposed by an austere and remote operating environment. 
• Increased cognitive load on individuals conducting operations due to exponential growth in the volume, veracity, and velocity of data. 
• Survivability and the need for distributed nodal command and control.

The dilemma posed here is whether technological advancements inadvertently compromise decision-making abilities due to the heightened cognitive burden on users. 

Decisive action powered by AI 

Speed, precision, and data are critical on the modern digital battlefield. Human-machine teaming allows modern soldiers to work with AI as their digital agents, using natural language or voice commands through military radios. This hands-free interaction improves situational awareness and enhances decision-making by combining AI’s analytical power with human intuition and judgment.  

Using AI and machine learning on missions will become critical to effective command and control environments. Language models have evolved to create and use enterprise-level knowledge bases, integrating external data for more complex interactions. This advancement has significant effects for mission capabilities, with early applications in: 

• Voice transcription and translation—We have already seen that when paired with Push-To-Talk (PTT) voice radios, digital audio voice streams can be captured for real-time transcription, translations, and augmentation with other sources of data. 
• Robotic command and control orchestration—With an intent to release operators from the need to operate these systems manually, we can not only free human resources to concentrate on the specifics of their mission but also reduce the force protection overhead that is required to keep operators safe. 
• Intelligence, Surveillance, and Reconnaissance (ISR) analysis—Working with multiple agents and multimodal sensors for defense use cases, we can help increase the accuracy and range of surveillance and provide a multilayered approach to detection and action. 
• Querying Battle Management Systems—We not only provide the capability to access information in a humanistic way, at the point of need, we also reduce the intense staff effort associated with briefing and analysis of data—the AI agents can take on the manual load, freeing up the human cognitive load to enable better and faster decision making. 

Agentic AI explained 

So, what do we understand about the advancement and application of Agentic AI? When discussing Agentic AI, it’s crucial to highlight the characteristics that distinguish an agent from tools like ChatGPT or traditional digital assistants we’ve seen in office settings. There are five key nonlinear elements that define agentic capabilities: 

• Planning—Instead of diving right into a task, an AI agent pauses and plans the series of steps required. This structured approach prevents errors, as we often see in traditional language model implementations with robots. 
• Reflection—Current models like ChatGPT provide answers but don’t validate them, as they lack a built-in ‘reflection’ capability. The ability to ‘reflect’ and ensure completeness is crucial to confirm that tasks are executed properly and are relevant to each subsequent step in the Agentic AI lifecycle. 
• Use of tools—When the AI agent encounters a step it can’t perform, it checks its manual for a corresponding tool, gathers needed information, executes the task, and processes the response. This is crucial for proprietary industry capabilities, allowing handoffs to external sources. 
• Collaboration—Where the human or agents work collaboratively on collective tasks. This is important for two reasons: creating clear boundaries and ensuring agents are task-specific.  
• Memory—This cycle is further powered by memory, where the agent retains and can recall prior inputs, actions, and outcomes. With this memory, the agent learns from past decisions, allowing it to improve future actions and refine its planning and reflection. 

Traditional non-agentic AI workflows vs agentic AI workflows 

Collectively, these five characteristics form a framework known as the REACT framework (Reasoning and Action). Reasoning involves planning and reflection, while action is about the execution.  

The key difference between traditional non-agentic AI workflows, often seen in zero-shot prompts, and the more advanced, agentic workflows we’ve been discussing can be seen in the diagram below. 

In practice, AI agents can be seamlessly integrated into an organization’s workflow, especially for field operators. This will result in more efficient missions, quicker responses, and a trusted pairing of humans and machines. Additionally, it will allow warfighters to focus on tactical operations while AI handles data processing and situational analysis in the background.  

This is where digital agents can come into play. Digital agents that allow operators, particularly those in forward positions, to delegate specific tasks using natural language. Incorporating these agents into your workflow can help revolutionize how your organization handles complex operations. By offering an intuitive interface, robust performance under duress, and the ability to manage tedious tasks, these agents ensure that operators at the tactical edge can focus on what really matters—making critical decisions in dynamic environments. 

Microsoft AI principles 

Microsoft is committed to advancing AI through principles that put people first. 

We put our responsible AI principles into practice through the AI, Ethics, and Effects in Engineering and Research (Aether) Committee, as well as our Office of Responsible AI (ORA). The Aether Committee advises our leadership on the challenges and opportunities presented by AI innovations. ORA sets our rules and governance processes, working closely with teams across the company to support the effort. 

Microsoft AI serves to enhance human capabilities, not replace them. It’s designed to embody principles such as fairness, inclusivity, reliability and safety, transparency, privacy and security, and accountability. By using AI to optimize administrative functions and services, stakeholders can focus on what matters most: human-centered design, decision-making, and empathy.  

Implement emerging technologies strategically 

Defense decision makers should consider not just what AI can do, but what it should do to innovate in a reliable and trusted way. It’s critical to understand the components of a holistic approach to AI that will help agencies turn meaningful innovation into actionable results that will benefit society.  To learn more contact your Microsoft Defense and Intelligence representative today, or engage with the following Microsoft resources:  

• Microsoft Agentic Agents.
• AutoGen Update: Complex Tasks and Agents.
• Copilot agent builder.  

The post Empowering defense operations with Microsoft AI appeared first on Microsoft Industry Blogs.

Microsoft for telecommunications

Accelerate telco transformation in the era of AI.

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But when we talk about “customers,” what do we really mean? Traditionally, it’s the subscriber, who interacts with telecom services daily. However, the reality is more complex. Telecoms must serve a dual audience: not only these external customers, but also their internal customers—their workforce and operational teams, whose efficiency and satisfaction are crucial for delivering seamless customer service. This dual focus reshapes how telecoms must approach innovation and transformation, making AI an important enabler for both external engagement and internal efficiency. 

AI-powered customer engagement and personalization 

When enhancing customer experience, it’s about delivering consistently excellent service at every touchpoint, including self-service systems and contact centers. Modern customers expect their telecom providers to understand and anticipate their needs, resolve issues quickly, and offer personalized solutions. Failing to meet these expectations can result in customer churn and lost opportunities. 

To meet these evolving demands, telecoms are increasingly partnering with technology providers to develop and implement advanced AI solutions that elevate customer engagement and operational efficiency. By using AI and cloud-based platforms, telecoms can create personalized, real-time responses and predictive support systems that align with customer expectations. This collaboration not only improves customer experience but also streamlines internal processes, enabling telecoms to adapt quickly in a competitive landscape. 

Telecoms deploy AI customer service solutions

A prime example of telecoms using AI to meet these demands is Vodafone’s AI chatbot, TOBi, showcased in a recent webinar². TOBi handles more than 45 million interactions per month³ across multiple languages, significantly reducing wait times and improving customer satisfaction by providing real-time, accurate responses. This capability is powered by Microsoft Azure, allowing Vodafone to efficiently scale support across 13 markets⁴. 

To fit the unique needs of telecom operators, Amdocs and Microsoft came together to create the Customer Engagement Platform, integrating advanced AI technologies and built-in capabilities, enriched with telecom-specific data. This platform integrates seamlessly with pre-sales, customer support, and beyond. For example, PLDT is partnering with Amdocs for digital transformation, leveraging real-time data from various touchpoints, such as billing and network usage to deliver highly personalized services.⁵ With the modular design of the Customer Engagement Platform, CSPs like PLDT can quickly deploy new services or updates, reducing resolution times, increasing agent productivity, and enhancing the overall customer experience, ensuring consistent and seamless interactions that enhance customer loyalty. 

Optimizing delivery with more autonomous agents

Additionally, Microsoft Dynamics 365 has introduced autonomous agents that further extend these capabilities, helping telecom companies optimize service delivery. The Customer Intent Agent dynamically identifies emerging customer needs by analyzing past and current interactions, autonomously updating knowledge libraries, and delivering contextually relevant solutions. By using Microsoft data security and AI best practices, telecoms can scale their customer care operations while maintaining competitive advantages. 

These AI-powered solutions help to ensure telecom providers can rapidly adapt to customer needs, driving satisfaction, loyalty, and retention. 

AI as the assistant for employees 

Just as customers demand excellence, the needs of employees and teams such as customer service agents, field technicians, and network engineers are a priority. AI technologies are important for streamlining workflows, surfacing actionable insights in real time, and automating routine tasks, enabling employees to focus on high-value activities and innovate within their roles. 

Applying GenAI to help address customer issues 

To illustrate, Vodafone’s integration of Microsoft Azure AI acts as an assistant for agents, automating repetitive tasks and providing insights that support complex customer interactions. By automating routine processes, Vodafone has successfully increased employee engagement and productivity, leading to an overall improvement in customer care quality. 

Similarly, the Amdocs and Microsoft Customer Engagement Platform⁶ enhances the employee experience by incorporating Microsoft Teams and Office tools. This integration equips telecom agents with real-time insights and predictive analytics, enabling them to address customer issues effectively and personalize support. The platform’s modular AI capabilities streamline workflows by automating routine interactions and consolidating multiple systems and data sources into a comprehensive 360-degree customer view. This empowers agents to make informed decisions quickly, enhancing both customer interactions and employee productivity. 

Amdocs’ low-code environment also empowers telecoms to rapidly develop and deploy virtual agents and process automation tools. These technologies assist non-technical employees in resolving customer queries efficiently while optimizing back-office operations such as billing and order management. This approach not only enhances productivity but also increases agent empowerment and engagement by allowing them to focus on more complex, high-value tasks, ultimately improving overall service quality. 

New agents to enhance the employee experience

New agents, such as Case Management Agent, further enhance these scenarios. By automating key tasks throughout the case lifecycle, from creation to resolution and follow-up, the agent reduces handle times, equipping agents with the insights needed for complex customer interactions. 

Both Amdocs and Vodafone demonstrate how Microsoft AI technology empowers telecom employees, streamlining operations and increasing efficiency by automating routine tasks and providing real-time insights. This AI-powered approach allows teams to focus on higher-value tasks that elevate service quality. 

AI as a catalyst for telecom innovation 

Amdocs and Vodafone demonstrate how AI enhances, rather than replaces, the human element in customer service. Technology is positioned as an assistant for agents, supporting them in managing routine tasks so they can engage in more meaningful, complex interactions. This approach helps telecoms provide more value-driven customer service while maintaining the human touch crucial for building customer relationships. 

AI’s transformative power extends beyond customer support centers. AI can support field services by optimizing routing, dispatch schedules, and resource allocation based on real-time data. This proactive use of AI minimizes downtime, optimizes field service operations, and further elevates customer experience. Additionally, Vodafone’s partnership with Microsoft uses AI to support neurodiverse staff and optimize network management, driving efficiency and personalized service. 

The path forward 

The future of customer care in telecom is deeply connected with AI, as demonstrated by Vodafone and Amdocs. Investing in AI leads to higher employee satisfaction, sustainable customer retention and growth, as well as ongoing innovation in a competitive landscape. As the telecommunications industry continues to evolve, embracing AI is important. Microsoft’s innovative solutions and strategic partnerships are designed to empower telecom companies to navigate this transformation successfully.  

By using AI, telecoms enhance customer experiences, drive operational efficiency, and create unique service moments. Embracing modernization of networks and the integration of real-time data analytics further position telecoms to lead in the digital age. Together, we can improve customer experience, unlock new revenue streams, and help to ensure long-term success in a rapidly changing landscape.

¹CSPs moving ahead with GenAI for cost reduction and efficiency gains. Omdia, Dec 2023

^2,3Transforming Customer Care: Vodafone’s AI Journey and Vision for the Future

⁴Magherita Della Valle, LinkedIn, Vodaphone Insights

⁵PLDT Selects Amdocs to Digitally Transform its Network Operations for Greater Agility and Enhanced Customer Experience | AMDOCS

⁶Amdocs and Microsoft Customer Engagement Platform

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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.

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Consider, for example, that Americans who live past the age of 70 can expect on average to spend $172,000 for long-term care over their lifetimes.¹ But most families (as many as 83%²) say it would be impossible or very difficult to afford $60,000 for annual in-home or assisted living care expenses.

Life and health insurance companies are working to help bridge this ever-widening gap with long-term care insurance (LTCI). And thanks to a broad range of new innovations enabled by AI, Microsoft and our partners are helping to improve LTCI for policyholders and insurers alike. This is part of our vision for intelligent insurance and our work with Microsoft Cloud for Financial Services. 

In this case, helping to make long-term care more accessible and affordable is good not just for enhancing societal well-being and reducing the strain on public resources, but also for the viability of a critical insurance product.

The unique challenges of long-term care insurance

LTCI insures people for a very costly circumstance that is very likely to occur.

About 70% of seniors will require long-term care at some point in their lives,³ and Americans spend more than $471 billion annually for long-term care.⁴ Yet Medicaid covers only 42% of long-term costs. LTCI aims to address a significant portion of long-term care costs by covering a range of services for people who need assistance with daily living activities—for example, bathing, dressing, and eating—over an extended period, often provided at home.

Despite the prevalence and urgency of the need, however, LTCI has proven challenging for insurers. Rising healthcare costs and higher-than-expected claims have created unforeseen financial pressures. People are living longer, increasing the likelihood of needing to use LTCI, and fewer customers than expected are letting their policies lapse. For insurers, this results in profitability challenges and uncertainty, while customers face higher premiums, and thus reduced access.

How AI can help improve LTCI profitability and growth

The success of insurance companies in addressing the growing need for LTCI will depend on a variety of factors, including education, awareness, and the efficiency and effectiveness of regulation. Above all, technology holds the key for transformation in profitability and growth.

We are seeing tangible results in AI innovation with our insurance customers worldwide that have direct relevance for LTCI providers. These potential benefits are prompting many insurers to accelerate their cloud migration and data management investments—a transition that is key to the LTCI sector, which lags other insurance segments. With the scale, security, and resilience of the Microsoft Cloud combined with the advanced data and analytics capabilities of Microsoft Fabric and the AI development opportunities enabled by Azure AI Studio, insurance companies can innovate rapidly and confidently to meet their specific needs.

The future of insurance in the era of AI

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Here are some of the important benefits that insurers can apply to their LTCI offerings.

Enhance underwriting and claims management

AI can streamline underwriting and claims processing in ways that improve both accuracy and efficiency. One important area of focus is straight-through processing (STP)—the automation of an entire workflow, from the initial data entry to the final decision, without the need for human intervention. STP helps to reduce delays, minimize errors, and free up valuable human resources.

In the underwriting process, AI helps enable STP for tasks such as analyzing historical data, assessing risk factors, and predicting the likelihood of claims, which helps underwriters make more informed decisions and reduces the time required for manual reviews. It can also handle a larger volume of applications without a corresponding increase in resources.

In claims processing, STP can automate the assessment and triage of claims—for example, by quickly extracting and analyzing information from a wide range of documents, including medical records, policy applications, and claims forms. Many insurers have long used optical character recognition (OCR) technology to digitize these types of documents. But the addition of generative AI supercharges how they can be understood, evaluated, and acted upon.

Automate contact center experiences

With generative AI’s natural language processing and content creation capabilities, insurers can optimize contact center operations in ways that help both the customer and the company.

AI-enabled copilots and virtual assistants can handle larger volumes of routine inquiries, helping agents and customer service representatives provide faster, more accurate responses to policyholders’ questions about coverage, claims status, and more. For example, John Hancock implemented a new AI solution to provide support for common customer issues and questions, which helps call center representatives focus their efforts and expertise on the most complex cases, with better customer experiences and reduced wait times.

Automated systems can understand and respond to customer inquiries in a conversational way, and even authenticate caller identities with voice biometrics, streamlining the identification process and enhancing security. For LTCI, AI can enable corresponding benefits through more efficient operations, better resource allocation, and enhanced customer experiences.

Prevent fraud, waste, and abuse

In the realm of fraud detection, advanced analytics and AI-powered tools can analyze vast amounts of data from healthcare vendor invoices to identify patterns and anomalies indicative of fraudulent activities in a timely manner. With better insights, insurers can proactively detect and prevent fraud, helping ensure that legitimate claims are processed swiftly while minimizing financial losses.

AI can also help insurers identify unusual patterns or anomalies that could indicate fraudulent or wasteful activities, such as flagging a particular facility if it consistently submits higher-than-average claims for certain treatments. Analyzing historical data can also help inform insurers to create more robust, data-driven processes to determine which facilities to audit or to benchmark best-in-class operators.

Expedite regulatory, contracting, and auditing activities

LTCI is inundated with regulatory, contracting, and auditing activities, many of which rely on cumbersome manual processes. AI can improve the efficiency of many of these workflows while also improving accuracy, turn-around times, and regulatory compliance. Data validation, risk assessment, and regulatory monitoring can all benefit. Moreover, AI’s predictive analytics can spot potential compliance issues, and its enhanced reporting capabilities can aid strategic decision-making.

Advancing LTCI with AI and Microsoft

We believe that with focused, creative innovation with AI, LTCI providers and their customers can look forward to a bright future in which more people can live with dignity and financial security in their senior years, thanks to high-quality, robust insurance products and services. We are excited to work with industry and our global partner ecosystem to strengthen LTCI, in line with Microsoft’s responsible AI principles and our Secure Future Initiative. 

To learn more about all our solutions, visit our Microsoft Cloud for Financial Services website.

¹AARP, “Long-Term Care Costs May Double to $5.6 Trillion by 2047,” March 2018.

²KFF, “The Affordability of Long-Term Care and Support Services: Findings from a KFF Survey,” November 2023.

³Morningstar, “100 Must-Know Statistics About Long-Term Care: 2023 Edition,” March 2023.

⁴A Place for Mom, “Long-Term Care Statistics,” September 2023.

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If you’ve read The AI Strategy Roadmap: Navigating the stages of value creation, you’re already familiar with the five drivers of AI value. This research paper and blog series explore what organizations need to succeed with AI, including establishing a clear strategy and securing senior leadership support. Our research found that AI success isn’t solely about technology—strategic, organizational, and cultural factors are equally critical.

We’ve heard from customers that it’s crucial to consider how prepared your organization is for this technological leap. In this blog post, we’ll share some learnings to help you gauge your AI readiness so you can plan how to move forward effectively.

Assess your AI readiness

When I meet with customers, they often share that they’re not sure where to start when it comes to assessing their readiness for large-scale AI transformation. It requires a strategic approach to understand your current capabilities, identify areas for improvement, and align these efforts with your business priorities to focus on the areas that will deliver the highest value. We’ve created a new AI Readiness Wizard to help you get started in evaluating your preparedness. Use the assessment to:

• Evaluate your current state. The assessment includes questions that help you determine how well your AI objectives align with your business priorities and the effectiveness of your current data access and security measures. Understanding your starting point is essential for identifying the right next steps.
• Identify gaps. By scoring your responses in the assessment, you can identify focus areas that may need more attention, such as business strategy, AI governance principles, or team expertise. This step helps prepare you for formulating a clear path forward and addressing specific areas for improvement.
• Plan your next steps. Based on your scores, the assessment categorizes your readiness into one of five stages: exploring, planning, implementing, scaling, or realizing. Each stage represents a different level of AI maturity and preparedness, guiding you on where to focus your efforts:
  • Exploring—At this stage, focus on building your AI strategy and experience. You might want to learn about key AI concepts and explore how AI is transforming the business landscape.
  • Planning—Here, you’ll concentrate on formalizing your business strategy. Look at the ways other organizations are driving value with AI and develop an informed plan for prioritizing AI projects.
  • Implementing—This stage involves focusing on leadership support and scaling AI expertise. Ensure you have the necessary resources and expertise to execute your AI initiatives effectively.
  • Scaling—At this level, you’ll aim to create an organization and culture of innovation. Scale your AI initiatives and begin analyzing the impact of AI in your organization.
  • Realizing—Focus on fostering continuous innovation within every team and the organization. Aim to embed AI technology in your operations and culture for sustained value creation.

Assessing your AI readiness requires a strategic approach to understand your current capabilities and identify areas for improvement.”

This assessment offers a structured way to reflect on your current practices and identify key areas to focus on as you develop your strategy for the future. You’ll also find resources for each stage to help you advance.

How AI is reshaping industries

Financial services

We’re seeing the financial services sector make rapid advancements in AI readiness, with 40% of organizations currently in the “implementing” stage. According to recent research, 70% of financial services organizations are using big data analytics in their operations, and 27% have piloted AI applications or AI-assisted solutions.

Additionally, more than half are allocating budgets for AI projects, providing AI-specific training, and fostering internal knowledge sharing. This commitment has enabled 27% of firms to reach the “scaling” and “realizing” stages, surpassing the 25% industry benchmark.

Healthcare

The healthcare industry shows a diverse mix of AI readiness, with 28% of organizations in the “scaling” and “realizing” stages, according to one study. Notably, 44% are actively laying the groundwork in the “exploring” and “planning” stages, focusing on learning and developing their AI strategies. The sector leads in overall maturity, but 14% of organizations report receiving no discernible value from AI, highlighting challenges in measuring the impact of AI investments within their broader business strategies.

Manufacturing

With 38% of organizations in the manufacturing industry still in the “exploring” and “planning” stages, many are focused on learning and developing AI strategies. Research shows that manufacturers actively deploy AI across operations, research and development (R&D), and supply chain management to address key business challenges. 25% believe they achieve significant value from AI implementation.

Manufacturing organizations also have a greater likelihood of appointing AI leadership, which we’re learning enables them to excel in fostering the operational and cultural factors that support value creation, resulting in more firms reaching the “realizing” and “scaling” stages.

Retail

We’ve seen a wide range of AI readiness in the retail sector. While some retailers use AI to enhance customer relationships and drive revenue, research shows that 43% are still in the “exploring” and “planning” stages. This divide is evident between those who adopted cloud technology early—about 25%—and those who have yet to embrace it, with 8% still not using cloud services. Notably, 21% of retailers have a chief AI officer, highlighting commitment among leadership to embed AI into their operations.

Map your AI journey

After assessing your readiness and gathering insights, you’ll want to outline a plan to address gaps and advance through the stages of AI maturity. Our findings at Microsoft have shown that crafting a strategic plan outlining how AI technology will fit into your organizational framework is a great place to start. The AI Strategy Roadmap: Navigating the stages of value creation is a valuable resource designed to guide you through this process.

As you define your AI strategy and roadmap, you might find our e-book, Building a Foundation for AI Success: A Leader’s Guide, helpful in identifying key focus areas for AI implementation.

The post A strategic approach to assessing your AI readiness appeared first on Microsoft Industry Blogs.

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Achieve more in the energy and resources industry with trusted data and AI solutions

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Transforming the global energy industry is not a small feat, nor one that happens without the collective work of dedicated partnerships and innovative technology. The standardized data model and secure data sharing in Microsoft Azure Data Manager for Energy along with operations data management powered by Azure AI and Microsoft Copilot can accelerate innovation across the end-to-end CCS value chain. Copilot and Azure Data Manager for Energy put data and AI to work, integrating industry datasets, applications, and other cloud services—managing intensive workloads at global scale, and quickly ingesting data for analytics and decision-making. These are high-impact capabilities that ultimately help energy companies accelerate their transition to more sustainable practices by reducing time, costs, and risks associated with their complex operational requirements.     

Enhancing energy operations with modern data management  

Data modernization is a critical component in advancing sustainability and CCS efforts within the energy sector. By leveraging Azure Data Manager for Energy, energy companies can efficiently manage and analyze vast amounts of data—enabling more accurate and comprehensive simulations of subsurface reservoirs. This capability is essential for identifying optimal CO₂ storage locations and ensuring the safe and efficient injection and storage of carbon dioxide.  

The platform’s robust, scalable, and secure data management solutions allow for real-time data integration and continuous model refinement, which are crucial for making informed decisions and mitigating risks. Additionally, Azure Data Manager for Energy’s high-performance computing capabilities enable rapid simulations, which significantly reduce the time required for planning studies and optimizing reservoir performance. These high-impact capabilities ultimately help energy companies accelerate their transition to more sustainable practices by reducing time, costs, and risks associated with their complex operational requirements. 

Harnessing the power of AI with Copilot 

Along with data modernization and robust data analytics, Azure Data Manager for Energy users will have the option to take advantage of Copilot to interact with well data. Azure Data Manager for Energy helps ingest and organize domain-specific data from across the enterprise data landscape to enhance data access, analysis, and application interoperability. Developed in alignment with OSDU® standards, Azure Data Manager for Energy helps get the right data organized within the right domain workflow while providing trustworthy data delivery that sets the stage for improved and timely analysis.  

However, the enterprise data landscape for any analysis may extend beyond domain-specific data types and require reports with different file types, as well as images, data and records stored in other databases, spreadsheets, and shared folders. Further, the entire value chain extends into data from operations, supply chain, health, safety and environment (HSE), enterprise resource planning (ERP), legal and compliance, and even social media—some of which may be hosted on external platforms.  

In these scenarios, generative AI capabilities can help users optimize data for enhanced insights—faster. One example of how to approach this is with Microsoft Fabric, an end-to-end analytics and data platform. Fabric can help integrate the data in Azure Data Manager for Energy with other adjacent data sources, ultimately preparing it for analysis and other interactions through AI and Copilot. This means users can potentially run traditional AI-powered workflows such as automated interpretation of data or event prediction through machine learning-driven algorithms. They can also leverage Copilot to chat with the data or implement intelligent search, domain-based intelligent assistants, or cross-domain intelligent advisors.  

In doing so, end users—people in roles across geoscience or petrophysics—have an easier and faster way to interact with and query their data, both within and outside Azure Data Manager for Energy. Plus, data engineers and data scientists have a foundation from which to build similar solutions for their end users. The Copilot capabilities also mean simplified research processes and the generation of valuable data insights, enabling enterprise and business unit leaders, as well as data scientists and geophysicists, to make more informed decisions and take advantage of greater efficiencies in reservoir management.  

Optimize carbon capture and storage and enhance reservoir management 

Building on the capabilities of Copilot and Azure Data Manager for Energy, we can further optimize CCS to work towards a more sustainable future. Reservoir modeling is a critical aspect of modern energy management, playing a vital role in the underground storage of CO₂. This multidisciplinary field involves the integration of geological, geophysical, thermal, and engineering data to create detailed models of subsurface reservoirs. Reservoir engineers create models that simulate the behavior of fluids within the reservoir to predict future performance and optimize injection and production strategies. With global energy demand projected to increase 47% by 2050,² the need for sustainable energy solutions and CCS is paramount.  

Microsoft is working with partners to provide the efficiency, predictive power, and speed of reservoir simulations and optimizations. Built on top of Azure Data Manager for Energy, customers can now leverage Azure’s robust enterprise capabilities in security, scalability, and reliability, while accessing its domain-specific solutions and maintaining full control over their data.   

Traditionally, identifying optimal CO₂ storage locations requires lengthy studies, sometimes spanning months or even years. The work Microsoft is doing with partners transforms this process by enabling scalable and efficient simulations. This will enable engineers to run numerous models in parallel, leveraging high-performance computing to quickly analyze vast datasets and identify the best storage locations. The ability to perform rapid simulations at scale significantly reduces the time required for planning studies.

Explore more energy solutions and resources 

At Microsoft, our dedication and commitment to accelerating the energy transition to carbon-free resources is matched only by the power of our partner ecosystem and the knowledge-sharing that makes it all possible. With Azure Data Manager for Energy, industry leaders can connect to an open ecosystem of interoperable applications from independent software vendors (ISVs) and the Microsoft ecosystem of productivity tools. By harnessing capabilities and features from across Microsoft and partner solutions, energy leaders can optimize value across their entire enterprise while working towards sustainability goals.  

Ready to dive deeper? Check out additional resources to learn more. 

• Learn how Azure Data Manager for Energy can accelerate your journey to data modernization.
• Read the Microsoft Industry Blogs: Energy and resources  to learn about themes and actions critical for energy transition.
• Learn how to achieve more in the energy and resources industry with trusted data and AI solutions from Microsoft.

¹ McKinsey & Company, Global Energy Perspective 2024, September 2024.

² S&P Global, Global energy demand to grow 47% by 2050, with oil still top source: US EIA, October 2021.

The post Driving operational efficiency and sustainability with AI and data modernization appeared first on Microsoft Industry Blogs.

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

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

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

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

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

Powerful personalization at scale with AI

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

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

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

Making AI-powered personalization “real”

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

But, how?

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

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

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

Embrace the future of marketing with AI

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

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

Learn more

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

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

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

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Read Microsoft’s new report on healthcare security trends

Prior to 2020, there was an unspoken rule of threat actors to not launch attacks against schools and children, infrastructure, and healthcare organizations.¹ However, that “rule” no longer applies, and in the past four years the healthcare threat landscape has seen tremendous shifts for the worse.

To put this shift into context, consider these trends from the Microsoft Threat Intelligence report showing healthcare cybersecurity challenges:

• Healthcare is one of the top 10 most targeted industries in the second quarter of 2024²—and has been for the past four quarters.
• Ransomware attacks are costly, with healthcare organizations losing an average of $900,000 per day on downtime alone.³
• In a recent study, out of the 99 healthcare organizations that admitted to paying a ransom and disclosed the ransom paid, the average payment was $4.4 million.⁴

The serious impact of ransomware on healthcare

While the potential financial risk for healthcare organizations is high, lives are at stake because ransomware attacks impact patient outcomes. If healthcare providers are not able to use diagnostic equipment or access patient medical records because it’s under ransom, care will be disrupted.

Healthcare facilities located near hospitals that are impacted by ransomware are also affected because they experience a surge of patients needing care and are unable to support them in an urgent manner. As a result, patients can experience longer wait times, which studies show could lead to more severe stroke cases and heart attack cases.⁵

These attacks don’t just impact facilities in large cities; in fact, rural health clinics are also a target for cyberattacks. They are particularly vulnerable to ransomware incidents because they often have limited means to prevent and remediate security risks. This can be devastating for a community as these hospitals are often the only healthcare option for many miles in the communities they serve.  

Why healthcare is an appealing target for threat actors

Healthcare organizations collect and store extremely sensitive data, which likely contributes to threat actors targeting them in ransomware attacks. However, a more significant reason these facilities are at risk is the potential for huge financial payouts. As referenced earlier, lives are at stake and healthcare facilities committed to patient care can’t risk poor patient outcomes if their systems are taken down. They also can’t risk their patients’ data being exposed if they don’t pay the ransom. That reputation for paying ransoms—for understandable reasons—makes them a target.

What is phishing?Learn more 

Healthcare facilities are also targeted because of their limited security resources and cybersecurity investments to defend against these threats compared to other sectors. Facilities often lack staff dedicated to cybersecurity and in fact, some facilities don’t have a chief information security officer (CISO) or dedicated security operations center at all. Instead, their IT department may be tasked with managing cybersecurity. Doctors, nurses, and healthcare staff may not have received any cybersecurity training or know the signs to look for to identify a phishing email.

Explore healthcare security trends in new Microsoft report

How cyber criminals target healthcare organizations

Financially motivated cyber criminals are using an evolving set of ransomware tactics on healthcare organizations. One common approach involves two steps. First, they gain access to an organization’s network, often using social engineering tactics through a phishing email or text. Then, they use that access to deploy ransomware to encrypt and lock healthcare systems and data so they can seek a ransom for their release.

“Once ransomware is deployed, attackers typically move quickly to encrypt critical systems and data, often within a matter of hours,” said Jack Mott of Microsoft Threat Intelligence in the Microsoft ransomware report. “They target essential infrastructure, such as patient records, diagnostic systems, and even billing operations, to maximize the impact and pressure on healthcare organizations to pay the ransom.”

Social engineering tactics often involve convincing the email recipient to act in ways they normally wouldn’t, such as clicking on an unknown link, and using the tactics of urgency, emotion, and habit. Social engineering fraud is a serious problem. In just this fiscal year, a staggering 389 healthcare institutions across the United States fell victim to ransomware attacks, according to the 2024 Microsoft Digital Defense Report.⁶ The aftermath was severe, resulting in network closures, offline systems, delays in critical medical operations, and rescheduled appointments.

Another common approach is ransomware as a service (RaaS), a cybercrime business model growing in popularity. The RaaS model is an agreement between an operator, who develops extortion tools, and an affiliate, who deploys the ransomware. Both parties benefit from a successful ransomware and extortion attack, and it’s “democratized access to sophisticated ransomware tools,” Mott said. This model enables cyber criminals without the means of developing their own tools to launch their nefarious activities. Sometimes, they may simply purchase network access from a cybercrime group that has already breached a network. RaaS severely widens the risk to healthcare organizations, making ransomware more accessible and frequent.

Cybercrime tactics continue to grow in sophistication. Microsoft is continually tracking the latest cybercrime threats to support our customers and increase the knowledge of the entire global community. These threats include actions by threat actor groups Vanilla Tempest and Sangria Tempest, which are known for their financially motivated criminal activities.

US healthcare at risk: Read the report

Take a collective defense approach to boost your cyber resilience and visibility

We recognize that not all organizations have a robust cybersecurity team or even the resources to enable a cybersecurity resilience strategy. This is why it is important for us as a community to come together and share best practices, tools, and guidance. We encourage your organization to collaborate with regional, national, and global healthcare organizations such as Health-ISAC (Information Sharing and Analysis Centers). The Health-ISAC provides healthcare organizations with platforms to exchange threat intelligence. Health-ISAC Chief Security Officer Errol Weiss says these organizations are like “virtual neighborhood watch programs,” sharing threat experiences and defense strategies. 

It’s also important to foster a security-first mindset among healthcare staff. Dr. Christian Dameff and Dr. Jeff Tully, Co-directors of the University of California San Diego Center for Healthcare Cybersecurity, emphasize that breaking down silos between IT security teams, emergency managers, and clinical staff to develop cohesive incident response plans is key. They also recommend running high-fidelity clinical simulations that expose doctors and nurses to real-world cyberattack scenarios.

For rural hospitals that provide critical services to the communities they serve across the US, Microsoft created the Microsoft Cybersecurity Program for Rural Hospitals, which provides affordable access to Microsoft security solutions, builds cybersecurity capacity, and helps solve root challenges through innovation.

For healthcare organizations that have the resources, as part of this report we provide guidance on how to:

• Establish a robust governance framework.
• Create an incident response and detection plan. Then be prepared to execute it efficiently during an actual attack to minimize damage and ensure a quick recovery.
• Implement continuous monitoring and real-time detection capabilities.
• Educate your organization using our cybersecurity awareness and education #BeCyberSmart Kit.
• Harness more resilience strategies found in the report.

Given the serious cyberthreats against healthcare organizations, it’s critical to protect your assets by understanding the situation and taking steps to prevent it. For more details on the current healthcare cyberthreat landscape and ransomware threats, and for more in-depth guidance on boosting resilience, read the “US healthcare at risk: Strengthening resiliency against ransomware attacks” report and watch our healthcare threat intelligence briefing video, which is included in the report. To stay up-to-date on the latest threat intelligence insights and get actionable guidance for your security efforts, bookmark Microsoft Security Insider.

Learn more

To learn more about Microsoft Security solutions, visit our website. Bookmark the Security blog to keep up with our expert coverage on security matters. Also, follow us on LinkedIn (Microsoft Security) and X (@MSFTSecurity) for the latest news and updates on cybersecurity.

¹How to protect your networks from ransomware, justice.gov.

²Threat Landscape: Healthcare and Public Health Sector, April 2024. Microsoft Threat Intelligence.

³On average, healthcare organizations lose $900,000 per day to downtime from ransomware attacks, Comparitech. March 6, 2024.

⁴Healthcare Ransomware Attacks Continue to Increase in Number and Severity, The HIPAA Journal. September 2024.

⁵Ransomware Attack Associated With Disruptions at Adjacent Emergency Departments in the US, JAMA Network. May 8, 2023.

⁶Microsoft Digital Defense Report 2024.

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